Daytona vs Maritime

Agent access

1. ai-native userPoint an agent at llms.txt or agent-oriented docs

   weight 2 · round drawn
   Daytona✓fullprobed9/10

   Daytona serves a live llms.txt (200 OK) and .md-suffixed docs pages for agent consumption, plus a dedicated agent-skills page listing API/CLI/SDK patterns for agents. This directly satisfies pointing an agent at llms.txt/agent-oriented docs. Missing for 10: independent third-party confirmation that agents actually consume these successfully in practice.

   • [probe] “PROBE llms.txt: HTTP 200 at https://www.daytona.io/llms.txt # Daytona > Daytona is a secure and elastic infrastructure for running AI-gener…”
   • [probe] “PROBE docs-md: HTTP 200 at https://www.daytona.io/docs/en/sandboxes.md # Sandboxes Daytona provides **full composable computers** — *…”
   • [claimed-docs] “An agent skill for creating and running sandboxes on the Daytona platform. Includes references and practical patterns for the Daytona API, C…”

   proof ↗⚑ flag

   Maritime✓fullprobed9/10

   Maritime hosts a live llms.txt (confirmed by a 200 probe) plus a dedicated CLI llms.txt and an ai-agents docs page explicitly designed for agents (Claude Code, Cursor, etc.) to consume, authenticate, and act on — directly matching the story. Missing for 10: no independent third-party confirmation that agents actually navigate these docs successfully in practice.

   • [probe] “PROBE llms.txt: HTTP 200 at https://maritime.sh/llms.txt # Maritime > Maritime is a cloud platform for deploying, hosting, and managing AI …”
   • [claimed-docs] “Templates are cloneable agent packages; publishers can share them via link or the public library.”
   • [claimed-docs] “Pass --json on EVERY command. Success: one JSON value on stdout, stderr empty.”
   • [claimed-docs] “The Maritime CLI is built to be operated by other AI agents, not just humans: machine-readable output on every command, stable exit codes, a…”
   • [claimed-docs] “Paste one prompt into Claude, Cursor, or any assistant and it can install the CLI, authenticate, and deploy agents for you.”

   proof ↗⚑ flag

2. ai-native userRun the product headlessly / in CI for automation

   weight 2 · round drawn
   Daytona✓fullprobed8/10

   Daytona is designed for programmatic, headless use: SDKs (Python/TypeScript), a CLI, and a REST API (OpenAPI spec) let sandboxes be created, managed, and code executed entirely without a UI, and snapshots/warm pools support fast, repeatable automation suitable for CI pipelines. Docs show example code creating sandboxes and running commands programmatically, and network/firewall controls support automated untrusted-code execution scenarios common in CI. Missing for 10: no explicit CI integration guide (e.g., GitHub Actions example) or third-party report of using Daytona inside a CI pipeline.

   • [claimed-docs] “The Daytona TypeScript SDK provides a powerful interface for programmatically interacting with Daytona Sandboxes.”
   • [claimed-docs] “The Daytona Python SDK provides a robust interface for programmatically interacting with Daytona Sandboxes.”
   • [claimed-docs] “Daytona provides command-line access to core features for interacting with Daytona Sandboxes, including managing their lifecycle, snapshots,…”
   • [claimed-docs] “sandbox = daytona.create() # Execute a command response = sandbox.process.exec("echo 'Hello, World!'")”
   • [probe] “PROBE openapi: HTTP 200 at https://www.daytona.io/openapi.json — contains "openapi" key”
   • [claimed-docs] “keep a configured number of pre-created, running sandboxes built from a snapshot; matching sandbox create requests claim one warm sandbox fr…”
   • [claimed-docs] “Daytona lets you spin up sandboxes in milliseconds and shut them down just as fast. Use what you need, when you need it.”

   proof ↗⚑ flag

   Maritime✓fullprobed8/10

   Maritime provides a CLI with --json flag on every command, machine-readable output, stable exit codes, and env-var (non-interactive) authentication via MARITIME_TOKEN, explicitly designed for automation/CI use and control by other AI agents rather than humans, plus a RESTful API and idempotent provisioning for scripted workflows. This directly supports headless/CI operation across CLI and SDK/API paths. Missing for 10: no explicit CI pipeline example (e.g., GitHub Actions/GitLab CI snippet) or independent third-party confirmation of CI usage.

   • [claimed-docs] “Pass --json on EVERY command. Success: one JSON value on stdout, stderr empty.”
   • [claimed-docs] “The Maritime CLI is built to be operated by other AI agents, not just humans: machine-readable output on every command, stable exit codes, a…”
   • [claimed-docs] “Authenticate with an env var, never interactively. Mint a long-lived key (`mk_…`) with `maritime keys create` and export it as `MARITIME_TOK…”
   • [claimed-docs] “Mint a long-lived key (`mk_…`) with `maritime keys create` and export it as `MARITIME_TOKEN`. Keys never expire; browser-login tokens do.”
   • [claimed-docs] “RESTful API for managing agents programmatically.”
   • [claimed-docs] “provision is idempotent on it: it returns the existing agent if there is one, else creates a new one. Safe to call on every sign-in.”
   • [probe] “official CLI documented at https://maritime.sh/docs/cli”

   proof ↗⚑ flag

3. ai-native userPlug MCP servers into this product so it can use their tools

   weight 3 · round drawn
   Daytona—none0/10

   The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is "none", never "na". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)

   ⚑ flag

   Maritime—none0/10

   Maritime is a platform for deploying and hosting agents built with frameworks (CrewAI, LangGraph, etc.), but the evidence pack shows no mention of MCP server integration or a mechanism for agents to consume external MCP tool servers. Nothing describes plugging in MCP servers to extend tool use.

   ⚑ flag

4. ai-native userConnect an agent via an official MCP server

   weight 3 · round to Daytona
   Daytona✓fullprobed8/10

   Daytona is a sandbox/infrastructure platform, not itself an agent, so publishing an MCP server is on-axis, and docs explicitly confirm a first-party MCP server letting agents like Claude, Cursor, and Windsurf interact with sandboxes programmatically. Missing for 10: independent/hands-on corroboration of the MCP server working in practice beyond vendor docs.

   • [claimed-docs] “Initialize the MCP server with your preferred AI agent. Supported agents include Claude, Cursor, and Windsurf.”
   • [claimed-docs] “Daytona Model Context Protocol (MCP) server enables AI agents to interact with Daytona Sandboxes programmatically.”
   • [probe] “official MCP server documented at https://www.daytona.io/docs/mcp”

   proof ↗⚑ flag

   Maritime—none0/10

   Maritime is a platform for deploying/hosting agents (not itself an agent), so an official MCP server is a fair axis to expect, but no evidence pack item mentions MCP, an MCP server, or MCP-compatible endpoints — only REST API, CLI, SDKs, and webhooks are documented.

   • [claimed-docs] “RESTful API for managing agents programmatically.”
   • [claimed-docs] “The Maritime CLI lets you deploy and manage agents from your terminal.”
   • [claimed-docs] “Mint a personal API key with `maritime keys create` and send it as a Bearer token, the same token the CLI itself uses.”

   proof ↗⚑ flag

5. ai-native userUse an official CLI

   weight 2 · round to Maritime
   Daytona✓fullprobed8/10

   Daytona documents an official CLI providing command-line access to sandbox lifecycle, snapshots, and other core features, and this CLI is also integrated into an agent-skill workflow alongside SDKs and API. Community evidence corroborates hands-on CLI usage (favorable comparison to DevPod for command-line users), though one report flags install-script quality issues unrelated to CLI functionality itself. Missing for 10: deeper independent third-party validation of CLI feature completeness and no explicit AI-native command examples beyond agent-skill reference.

   • [claimed-docs] “Daytona provides command-line access to core features for interacting with Daytona Sandboxes, including managing their lifecycle, snapshots,…”
   • [probe] “official CLI documented at https://www.daytona.io/docs/tools/cli”
   • [claimed-docs] “An agent skill for creating and running sandboxes on the Daytona platform. Includes references and practical patterns for the Daytona API, C…”
   • [community] “Comparing to DevPod: 'this looks like a step up in ease from faffing about with DevPod. Then again I'm a command line guy all the way, and D…”

   proof ↗⚑ flag

   Maritime✓fullprobed9/10

   Maritime ships an official npm-installable CLI explicitly designed for AI-native use: machine-readable/--json output on every command, stable exit codes, a self-describing contract, and env-var (non-interactive) authentication via MARITIME_TOKEN, all documented at /docs/cli and /docs/ai-agents. It also supports agent-driven install/auth/deploy flows via prompted assistants. Missing for 10: independent/hands-on verification beyond vendor docs.

   • [claimed-docs] “The Maritime CLI lets you deploy and manage agents from your terminal.”
   • [claimed-docs] “npm install -g maritime-cli”
   • [claimed-docs] “The Maritime CLI is built to be operated by other AI agents, not just humans: machine-readable output on every command, stable exit codes, a…”
   • [claimed-docs] “Authenticate with an env var, never interactively. Mint a long-lived key (`mk_…`) with `maritime keys create` and export it as `MARITIME_TOK…”
   • [claimed-docs] “Pass --json on EVERY command. Success: one JSON value on stdout, stderr empty.”
   • [claimed-docs] “Paste one prompt into Claude, Cursor, or any assistant and it can install the CLI, authenticate, and deploy agents for you.”
   • [probe] “official CLI documented at https://maritime.sh/docs/cli”

   proof ↗⚑ flag

6. ai-native userDrive the product through a documented public API

   weight 3 · round to Daytona
   Daytona✓fullprobed9/10

   Daytona exposes a documented OpenAPI spec, Python/TypeScript SDKs, a CLI, and an MCP server, all explicitly aimed at programmatic sandbox control by AI agents, with code examples (e.g., daytona.create(), sandbox.process.exec). This directly satisfies API-driven, agentic usage; missing for 10: independent third-party validation of API completeness/versioning stability beyond vendor docs.

   • [probe] “PROBE openapi: HTTP 200 at https://www.daytona.io/openapi.json — contains "openapi" key”
   • [claimed-docs] “The Daytona TypeScript SDK provides a powerful interface for programmatically interacting with Daytona Sandboxes.”
   • [claimed-docs] “The Daytona Python SDK provides a robust interface for programmatically interacting with Daytona Sandboxes.”
   • [claimed-docs] “Daytona provides command-line access to core features for interacting with Daytona Sandboxes, including managing their lifecycle, snapshots,…”
   • [probe] “official MCP server documented at https://www.daytona.io/docs/mcp”
   • [claimed-docs] “sandbox = daytona.create() # Execute a command response = sandbox.process.exec("echo 'Hello, World!'")”
   • [claimed-docs] “An agent skill for creating and running sandboxes on the Daytona platform. Includes references and practical patterns for the Daytona API, C…”

   proof ↗⚑ flag

   Maritime✓fullprobed8/10

   Maritime documents a full public API (REST endpoints, TypeScript/Python SDKs, idempotent provisioning, webhooks, scoped bearer-token auth) plus a CLI explicitly built for AI agents with machine-readable JSON output and stable exit codes, directly enabling AI-native driving of the platform. missing for 10: a discoverable machine-readable spec (openapi.json/swagger.json returned 404) and independent third-party corroboration of the API's stability.

   • [claimed-docs] “RESTful API for managing agents programmatically.”
   • [claimed-docs] “Mint a personal API key with `maritime keys create` and send it as a Bearer token, the same token the CLI itself uses.”
   • [claimed-docs] “Tag each agent with your own id via `externalId`. `provision` is **idempotent** on it: it returns the existing agent if there is one, else c…”
   • [claimed-docs] “provision is idempotent on it: it returns the existing agent if there is one, else creates a new one. Safe to call on every sign-in.”
   • [claimed-docs] “Get notified when a customer's agent changes state, instead of polling. Subscribe a URL, and Maritime POSTs a signed JSON body on every life…”
   • [claimed-docs] “The Maritime CLI is built to be operated by other AI agents, not just humans: machine-readable output on every command, stable exit codes, a…”
   • [claimed-docs] “Authenticate with an env var, never interactively. Mint a long-lived key (`mk_…`) with `maritime keys create` and export it as `MARITIME_TOK…”
   • [claimed-docs] “Pass --json on EVERY command. Success: one JSON value on stdout, stderr empty.”
   • [probe] “PROBE openapi: all candidate paths 404 (https://maritime.sh/openapi.json, https://maritime.sh/swagger.json, https://maritime.sh/api/openapi.…”

   proof ↗⚑ flag

7. ai-native userIssue scoped/least-privilege API credentials for an agent

   weight 2 · round to Maritime
   Daytona—none0/10

   Evidence shows API keys/SSH tokens exist and network/firewall controls limit egress, but there is no documentation of scoped or least-privilege credential issuance (e.g., per-agent permission scopes, role-based API keys) for agents. Missing for 10: any docs on scoped/limited-permission API key creation, credential scoping to specific sandboxes/actions, or least-privilege agent auth model.

   • [claimed-docs] “Set firewall parameters when creating a sandbox or update them while the sandbox is running.”
   • [claimed-docs] “Network limits control outbound internet access from sandboxes. Each sandbox runs behind a firewall that restricts which external IP address…”
   • [claimed-docs] “Each sandbox runs behind a firewall that restricts which external IP addresses and domains it can reach, preventing untrusted code from exfi…”
   • [claimed-docs] “Daytona provides SSH access to your sandboxes using token-based authentication. This allows you to connect from local terminals, IDEs, and d…”

   proof ↗⚑ flag

   Maritime✓fullclaimed8/10

   Maritime documents scoped API keys explicitly: every key carries scopes, users should hand subsystems the narrowest key they need, and a dedicated example shows a Computers-only key being refused on all other endpoints — a concrete least-privilege mechanism. This pairs with CLI/env-based key minting (mk_ tokens) suited to agent automation. Missing for 10: an exhaustive scope catalog/reference and independent (non-vendor) verification of scope enforcement in practice.

   • [claimed-docs] “Hand a subsystem the narrowest key it needs. A leaked key can then only do what its scope allows.”
   • [claimed-docs] “To hand out a key that reaches computers and nothing else, create a **Computers key** in the Computers section of Settings, API keys”
   • [claimed-docs] “Every key carries scopes. Hand a subsystem the _narrowest_ key it needs. A leaked key can then only do what its scope allows.”
   • [claimed-docs] “a key whose only scope is `computers` is refused on every other Maritime endpoint”
   • [claimed-docs] “Hand a subsystem the _narrowest_ key it needs. A leaked key can then only do what its scope allows.”
   • [claimed-docs] “Authenticate with an env var, never interactively. Mint a long-lived key (`mk_…`) with `maritime keys create` and export it as `MARITIME_TOK…”
   • [claimed-docs] “Mint a long-lived key (`mk_…`) with `maritime keys create` and export it as `MARITIME_TOKEN`. Keys never expire; browser-login tokens do.”

   proof ↗⚑ flag

8. ai-native userBuild against official SDKs

   weight 2 · round to Daytona
   Daytona✓fullprobed9/10

   Daytona ships official Python and TypeScript SDKs with documented programmatic interfaces and code samples, plus a CLI, OpenAPI spec, and MCP server for agent integration—strong evidence of first-party, agent-friendly SDK support. Missing for 10: independent hands-on developer corroboration specifically of SDK usage (community evidence is about unrelated install/SSH issues, not SDK quality).

   • [claimed-docs] “The Daytona TypeScript SDK provides a powerful interface for programmatically interacting with Daytona Sandboxes.”
   • [claimed-docs] “The Daytona Python SDK provides a robust interface for programmatically interacting with Daytona Sandboxes.”
   • [claimed-docs] “sandbox = daytona.create() # Execute a command response = sandbox.process.exec("echo 'Hello, World!'")”
   • [claimed-docs] “Daytona provides command-line access to core features for interacting with Daytona Sandboxes, including managing their lifecycle, snapshots,…”
   • [probe] “PROBE openapi: HTTP 200 at https://www.daytona.io/openapi.json — contains "openapi" key”
   • [probe] “official CLI documented at https://www.daytona.io/docs/tools/cli”
   • [claimed-docs] “An agent skill for creating and running sandboxes on the Daytona platform. Includes references and practical patterns for the Daytona API, C…”

   proof ↗⚑ flag

   Maritime✓fullprobed8/10

   Maritime documents official TypeScript and Python SDKs with idempotent agent provisioning, scoped authentication, webhooks, messaging, and a Files API, plus an npm-installable CLI and RESTful API, all clearly aimed at building AI-native integrations. missing for 10: no public OpenAPI/swagger spec found (404s) and no independent/hands-on corroboration beyond first-party docs.

   • [claimed-docs] “Tag each agent with your own id via `externalId`. `provision` is **idempotent** on it: it returns the existing agent if there is one, else c…”
   • [claimed-docs] “Mint a personal API key with `maritime keys create` and send it as a Bearer token, the same token the CLI itself uses.”
   • [claimed-docs] “Give every one of your users their own agent with the TypeScript or Python SDK.”
   • [claimed-docs] “Send a message and wait for the reply. Sleeping agents wake automatically.”
   • [claimed-docs] “RESTful API for managing agents programmatically.”
   • [claimed-docs] “The Maritime Files API: eight endpoints and both SDKs to browse, upload, download, and edit an agent's disk over HTTP, even while the agent …”
   • [claimed-docs] “provision is idempotent on it: it returns the existing agent if there is one, else creates a new one. Safe to call on every sign-in.”
   • [probe] “PROBE openapi: all candidate paths 404 (https://maritime.sh/openapi.json, https://maritime.sh/swagger.json, https://maritime.sh/api/openapi.…”

   proof ↗⚑ flag

9. ai-native userSubscribe to events via webhooks

   weight 2 · round to Maritime
   Daytona—none0/10

   No evidence anywhere in the pack mentions webhooks or event subscription mechanisms; Daytona's documented capabilities cover sandboxes, snapshots, SDKs, CLI, MCP, and SSH, but nothing about publishing or subscribing to events via webhooks.

   ⚑ flag

   Maritime✓fullclaimed7/10

   Docs explicitly describe subscribing a URL to receive signed JSON POSTs on every agent lifecycle event, replacing polling, which directly matches the story. missing for 10: independent/hands-on confirmation of webhook delivery reliability, payload schema details, and signature verification specifics.

   • [claimed-docs] “Get notified when a customer's agent changes state, instead of polling. Subscribe a URL, and Maritime POSTs a signed JSON body on every life…”
   • [claimed-docs] “Hand a subsystem the narrowest key it needs. A leaked key can then only do what its scope allows.”

   proof ↗⚑ flag

Agentic features

1. ai-native userSet up automations that run autonomously in the background

   weight 2 · round to Maritime
   Daytona—none0/10

   Daytona documents programmatic sandbox creation, SDKs, CLI, and an MCP server for agents to control sandboxes, but there is no evidence of a built-in scheduler, cron, trigger, or background automation runner that would let a user set up autonomous background automations natively within Daytona itself. Missing for 10: any documented scheduling/trigger/webhook mechanism, evidence of persistent autonomous background jobs, or first-party automation orchestration beyond on-demand sandbox execution.

   • [claimed-docs] “Sandboxes are isolated runtime environments you can manage programmatically to run code.”
   • [claimed-docs] “The Daytona TypeScript SDK provides a powerful interface for programmatically interacting with Daytona Sandboxes.”
   • [claimed-docs] “The Daytona Python SDK provides a robust interface for programmatically interacting with Daytona Sandboxes.”
   • [claimed-docs] “Daytona provides command-line access to core features for interacting with Daytona Sandboxes, including managing their lifecycle, snapshots,…”
   • [claimed-docs] “Daytona Model Context Protocol (MCP) server enables AI agents to interact with Daytona Sandboxes programmatically.”

   proof ↗⚑ flag

   Maritime✓fullclaimed8/10

   Maritime provides cron triggers with standard five-field expressions that wake agents on schedule and deliver prompts, plus agents can self-schedule their own wake triggers (e.g., 'send me a digest every weekday at 9'), and agents run persistently on always-on micro-VMs with sleep/wake economics, enabling true background autonomous automation. Missing for 10: independent/hands-on verification of trigger reliability and no mention of monitoring/alerting on automation failures.

   • [claimed-docs] “A cron trigger takes a standard five-field expression, wakes the agent on schedule, and delivers its `prompt` as a chat message on every fir…”
   • [claimed-docs] “Ask the agent (OpenClaw-family and Hermes templates): tell it "send me a digest every weekday at 9" and the platform syncs the schedule it s…”
   • [claimed-docs] “tell it "send me a digest every weekday at 9" and the platform syncs the schedule it sets for itself into real wake triggers”
   • [claimed-docs] “Always-on is a flat add-on for the rare agent that must react in milliseconds around the clock.”
   • [claimed-docs] “Send a message and wait for the reply. Sleeping agents wake automatically.”
   • [claimed-docs] “Maritime gives it what a laptop session can't: its own always-on micro-VM in the cloud, persistent storage, sleep/wake economics, and every …”

   proof ↗⚑ flag

2. ai-native userOperate the product with natural-language commands

   weight 2 · round drawn
   Daytona~partialprobed6/10

   Daytona ships an official MCP server that lets AI agents (Claude, Cursor, Windsurf) drive sandbox creation/management via natural language, and an 'agent skill' doc packages API/CLI/SDK patterns for agent use, so natural-language operation is possible when mediated through a connected AI agent. However Daytona itself has no native NL command interface — direct operation is via SDKs, CLI, API, or web terminal, all imperative/code-based. Missing for 10: evidence of a first-party conversational/NL interface built into Daytona (not just via third-party agent + MCP), and independent confirmation of this workflow in practice.

   • [claimed-docs] “Initialize the MCP server with your preferred AI agent. Supported agents include Claude, Cursor, and Windsurf.”
   • [claimed-docs] “Daytona Model Context Protocol (MCP) server enables AI agents to interact with Daytona Sandboxes programmatically.”
   • [claimed-docs] “An agent skill for creating and running sandboxes on the Daytona platform. Includes references and practical patterns for the Daytona API, C…”
   • [probe] “official MCP server documented at https://www.daytona.io/docs/mcp”

   proof ↗⚑ flag

   Maritime~partialclaimed6/10

   Maritime documents natural-language operation at the agent level (chat with agents, cron-triggered prompts set via natural language like 'send me a digest every weekday at 9') and CLI/API designed to be driven by AI agents via prompts (paste a prompt into Claude/Cursor to install and operate). However, the core Maritime platform itself (dashboard, agent management) is not shown to be operable purely via natural-language commands by the end user — the NL interface is mediated through the CLI/API being agent-friendly rather than a native NL command surface for platform operations. missing for 10: evidence of a native NL interface for platform-level operations (not just agent chat/prompts), independent/hands-on confirmation of this workflow succeeding.

   • [claimed-docs] “Ask the agent (OpenClaw-family and Hermes templates): tell it "send me a digest every weekday at 9" and the platform syncs the schedule it s…”
   • [claimed-docs] “Paste one prompt into Claude, Cursor, or any assistant and it can install the CLI, authenticate, and deploy agents for you.”
   • [claimed-docs] “tell it "send me a digest every weekday at 9" and the platform syncs the schedule it sets for itself into real wake triggers”
   • [claimed-docs] “The Maritime CLI is built to be operated by other AI agents, not just humans: machine-readable output on every command, stable exit codes, a…”
   • [claimed-docs] “point Claude Code or your Maritime agent at a provider's runbook page and it can inventory the old provider with read-only credentials, recr…”

   proof ↗⚑ flag

Api quality

1. ai-native userExplore an interactive API reference with runnable examples

   weight 2 · round to Daytona
   Daytona~partialprobed3/10

   Daytona publishes a machine-readable OpenAPI spec and SDK docs with code snippets (e.g., Python example), suggesting an API reference exists, but there is no evidence of an interactive, in-browser 'try it now' explorer or runnable examples embedded in the docs themselves. missing for 10: evidence of a Swagger/ReadMe-style interactive API explorer, in-docs code execution, or runnable example sandbox tied to the API reference.

   • [probe] “PROBE openapi: HTTP 200 at https://www.daytona.io/openapi.json — contains "openapi" key”
   • [claimed-docs] “sandbox = daytona.create() # Execute a command response = sandbox.process.exec("echo 'Hello, World!'")”
   • [claimed-docs] “The Daytona TypeScript SDK provides a powerful interface for programmatically interacting with Daytona Sandboxes.”
   • [claimed-docs] “The Daytona Python SDK provides a robust interface for programmatically interacting with Daytona Sandboxes.”

   proof ↗⚑ flag

   Maritime—none0/10

   Docs describe a RESTful API and CLI but there's no evidence of an interactive API reference (e.g., Swagger/OpenAPI explorer) with runnable examples; probes explicitly show no openapi.json/swagger.json spec found at any candidate path. missing for 10: interactive API console/playground, OpenAPI spec, runnable code examples in docs.

   • [claimed-docs] “RESTful API for managing agents programmatically.”
   • [claimed-docs] “Mint a personal API key with `maritime keys create` and send it as a Bearer token, the same token the CLI itself uses.”
   • [probe] “PROBE openapi: all candidate paths 404 (https://maritime.sh/openapi.json, https://maritime.sh/swagger.json, https://maritime.sh/api/openapi.…”

   proof ↗⚑ flag

2. ai-native userDownload a machine-readable API spec (OpenAPI or equivalent)

   weight 2 · round to Daytona
   Daytona✓fullprobed9/10

   Daytona hosts a machine-readable OpenAPI spec directly at /openapi.json (verified HTTP 200 with valid 'openapi' key), confirming a downloadable API spec exists. Missing for 10: no independent third-party corroboration of spec completeness or versioning.

   • [probe] “PROBE openapi: HTTP 200 at https://www.daytona.io/openapi.json — contains "openapi" key”

   proof ↗⚑ flag

   Maritime—none0/10

   Maritime documents a RESTful API and CLI with --json output, but there is no evidence of a downloadable machine-readable spec (OpenAPI/Swagger); active probes for openapi.json, swagger.json, and similar paths all returned 404.

   • [probe] “PROBE openapi: all candidate paths 404 (https://maritime.sh/openapi.json, https://maritime.sh/swagger.json, https://maritime.sh/api/openapi.…”
   • [claimed-docs] “RESTful API for managing agents programmatically.”
   • [claimed-docs] “Mint a personal API key with `maritime keys create` and send it as a Bearer token, the same token the CLI itself uses.”

   proof ↗⚑ flag

3. ai-native userTest against a sandbox environment without touching production data

   weight 1 · round to Daytona
   Daytona✓fullprobed8/10

   Daytona sandboxes are isolated, ephemeral runtime environments with dedicated kernel/filesystem/network stack, firewalled outbound access, and can be spun up/torn down in milliseconds from snapshots—purpose-built for AI agents to run and test code without touching production systems. SDKs, CLI, and MCP server make it straightforward for AI-native workflows to programmatically create disposable sandboxes for testing. Missing for 10: no explicit documentation or case study framing sandboxes as a 'staging vs production data' workflow, and no independent/hands-on verification of isolation guarantees beyond vendor docs.

   • [claimed-docs] “Sandboxes are isolated runtime environments you can manage programmatically to run code.”
   • [claimed-docs] “Each sandbox runs in isolation, giving it a dedicated kernel, filesystem, network stack, and allocated vCPU, RAM, and disk.”
   • [claimed-docs] “Network limits control outbound internet access from sandboxes. Each sandbox runs behind a firewall that restricts which external IP address…”
   • [claimed-docs] “Each sandbox runs behind a firewall that restricts which external IP addresses and domains it can reach, preventing untrusted code from exfi…”
   • [claimed-docs] “Daytona lets you spin up sandboxes in milliseconds and shut them down just as fast. Use what you need, when you need it.”
   • [claimed-docs] “Daytona Model Context Protocol (MCP) server enables AI agents to interact with Daytona Sandboxes programmatically.”
   • [probe] “PROBE docs-md: HTTP 200 at https://www.daytona.io/docs/en/sandboxes.md # Sandboxes Daytona provides **full composable computers** — *…”
   • [claimed-docs] “keep a configured number of pre-created, running sandboxes built from a snapshot; matching sandbox create requests claim one warm sandbox fr…”

   proof ↗⚑ flag

   Maritime—none0/10

   Maritime's docs cover deployment, keys/scopes, templates, triggers, and agent lifecycle, but nothing describes a sandbox/staging environment isolated from production data for testing purposes. Missing for 10: any mention of a sandbox/test mode, staging environment, or data isolation guarantees between test and production agents.

   ⚑ flag

4. ai-native userRely on versioned APIs with a documented deprecation policy

   weight 2 · round drawn
   Daytona—none0/10

   There is an OpenAPI spec and SDKs, but no evidence of API versioning scheme or a documented deprecation policy anywhere in the docs pack. Missing for 10: explicit API version scheme (e.g., v1/v2 paths), changelog of breaking changes, documented deprecation/sunset policy or timelines.

   • [probe] “PROBE openapi: HTTP 200 at https://www.daytona.io/openapi.json — contains "openapi" key”

   proof ↗⚑ flag

   Maritime—none0/10

   No evidence of API versioning scheme (e.g., v1/v2 paths) or any documented deprecation policy; OpenAPI spec probes all 404, and no docs mention version support windows or breaking-change policy.

   • [probe] “PROBE openapi: all candidate paths 404 (https://maritime.sh/openapi.json, https://maritime.sh/swagger.json, https://maritime.sh/api/openapi.…”
   • [claimed-docs] “RESTful API for managing agents programmatically.”

   proof ↗⚑ flag

1. ai-native userPerform bulk operations across many items at once

   weight 2 · round drawn
   Daytona~partialclaimed4/10

   Daytona's SDKs/CLI/API allow programmatic creation and management of sandboxes, and features like snapshot-based warm pools (docs-23/30) imply scaling to many pre-created instances, which could support scripted bulk workflows. However, there is no documented explicit bulk/batch operation primitive (e.g., bulk delete, bulk snapshot, batch API endpoint) for acting on many items in a single call. missing for 10: explicit bulk/batch API or CLI commands, independent evidence of bulk operations at scale, documentation of concurrency limits or bulk performance.

   • [claimed-docs] “The Daytona Python SDK provides a robust interface for programmatically interacting with Daytona Sandboxes.”
   • [claimed-docs] “Daytona provides command-line access to core features for interacting with Daytona Sandboxes, including managing their lifecycle, snapshots,…”
   • [claimed-docs] “keep a configured number of pre-created, running sandboxes built from a snapshot; matching sandbox create requests claim one warm sandbox fr…”
   • [claimed-docs] “keep a configured number of pre-created, running sandboxes built from a snapshot; matching sandbox create requests claim one warm sandbox fr…”
   • [claimed-docs] “sandbox = daytona.create() # Execute a command response = sandbox.process.exec("echo 'Hello, World!'")”

   proof ↗⚑ flag

   Maritime~partialclaimed4/10

   There is explicit support for bulk import of config/env vars (maritime-docs-11) and a scriptable CLI/API with JSON output (maritime-docs-9, maritime-docs-36) that an AI-native user could loop over to perform bulk actions, but there is no documented batch/bulk endpoint for operating on many agents or resources at once. missing for 10: a dedicated bulk-operation API/CLI command (e.g. bulk create/delete/update agents), evidence of scaling to many items reliably, and independent confirmation of bulk workflows in practice.

   • [claimed-docs] “Bulk import from a .env file (or stdin), then hot-reload”
   • [claimed-docs] “Pass --json on EVERY command. Success: one JSON value on stdout, stderr empty.”
   • [claimed-docs] “RESTful API for managing agents programmatically.”
   • [claimed-docs] “The Maritime CLI is built to be operated by other AI agents, not just humans: machine-readable output on every command, stable exit codes, a…”

   proof ↗⚑ flag

2. ai-native userDefine rules that trigger actions automatically on events

   weight 3 · round to Maritime
   Daytona—none0/10

   Daytona provides sandbox infrastructure and SDKs for programmatic control, but there is no evidence of an event/trigger-based rules engine (e.g., webhooks, event listeners, or conditional automation triggers) that fires actions automatically on events.

   ⚑ flag

   Maritime~partialclaimed6/10

   Maritime supports triggers (cron with five-field expressions, natural-language schedule setting, webhooks for lifecycle events) that wake agents and fire actions automatically, which covers rule-based automation on events. However, this is event/schedule-based triggering rather than a general-purpose conditional rules engine (e.g., no evidence of arbitrary if/then logic across multiple event types or complex conditions). missing for 10: evidence of a general conditional rules/policy engine beyond cron and webhook triggers, and independent/hands-on confirmation of trigger reliability.

   • [claimed-docs] “A cron trigger takes a standard five-field expression, wakes the agent on schedule, and delivers its `prompt` as a chat message on every fir…”
   • [claimed-docs] “Ask the agent (OpenClaw-family and Hermes templates): tell it "send me a digest every weekday at 9" and the platform syncs the schedule it s…”
   • [claimed-docs] “tell it "send me a digest every weekday at 9" and the platform syncs the schedule it sets for itself into real wake triggers”
   • [claimed-docs] “Get notified when a customer's agent changes state, instead of polling. Subscribe a URL, and Maritime POSTs a signed JSON body on every life…”
   • [claimed-docs] “Always-on is a flat add-on for the rare agent that must react in milliseconds around the clock.”

   proof ↗⚑ flag

3. ai-native userSchedule recurring jobs or workflows

   weight 2 · round to Maritime
   Daytona—none0/10

   Daytona provides sandbox infrastructure, SDKs, CLI, and MCP server for on-demand code execution, but there is no evidence of any scheduling, cron-like, or recurring job/workflow orchestration feature. missing for 10: scheduler/cron API, recurring job definitions, workflow orchestration, trigger-based automation.

   ⚑ flag

   Maritime✓fullclaimed8/10

   Maritime explicitly supports cron triggers with standard five-field expressions that wake agents and deliver prompts on schedule, plus a natural-language flow where an agent sets its own recurring schedule ('send me a digest every weekday at 9') that syncs into real wake triggers. This directly covers recurring job/workflow scheduling for AI-native users. Missing for 10: independent/hands-on verification beyond vendor docs, and no detail on managing/editing/cancelling existing scheduled triggers via CLI/API.

   • [claimed-docs] “A cron trigger takes a standard five-field expression, wakes the agent on schedule, and delivers its `prompt` as a chat message on every fir…”
   • [claimed-docs] “Ask the agent (OpenClaw-family and Hermes templates): tell it "send me a digest every weekday at 9" and the platform syncs the schedule it s…”
   • [claimed-docs] “tell it "send me a digest every weekday at 9" and the platform syncs the schedule it sets for itself into real wake triggers”
   • [claimed-docs] “Always-on is a flat add-on for the rare agent that must react in milliseconds around the clock.”

   proof ↗⚑ flag

4. ai-native userVersion, review, and roll back my automations

   weight 1 · round drawn
   Daytona—none0/10

   The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is "none", never "na". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)

   ⚑ flag

   Maritime—none0/10

   No evidence of version history, review workflows, or rollback for automations/agent configs; docs cover deployment, triggers, builds, and CLI but nothing about versioning or reverting agent configuration/automation changes.

   ⚑ flag

Agent workloads

1. ai-native userRun coding agents like Claude Code or Codex inside the sandbox following the vendor's own recipe

   weight 2 · round to Maritime
   Daytona—none0/10

   Evidence shows Daytona sandboxes can execute arbitrary code, host an MCP server for external AI agents (Claude, Cursor, Windsurf) to control sandboxes, and includes an 'agent skill' for creating/running sandboxes — but none of this documents a vendor recipe for actually running Claude Code or Codex *inside* a sandbox as a coding agent. Missing for 10: an explicit vendor tutorial/recipe demonstrating installation and execution of Claude Code/Codex within a sandbox environment.

   • [claimed-docs] “Initialize the MCP server with your preferred AI agent. Supported agents include Claude, Cursor, and Windsurf.”
   • [claimed-docs] “Daytona Model Context Protocol (MCP) server enables AI agents to interact with Daytona Sandboxes programmatically.”
   • [claimed-docs] “An agent skill for creating and running sandboxes on the Daytona platform. Includes references and practical patterns for the Daytona API, C…”

   proof ↗⚑ flag

   Maritime✓fullclaimed8/10

   Maritime documents dedicated template recipes for running Claude Code and Codex inside its sandboxed micro-VMs (persistent storage, sleep/wake, metered LLM access by default, chat channels), directly matching the vendor's own recipe framing for these named coding agents. missing for 10: independent/hands-on verification beyond vendor docs, and no direct evidence of a step-by-step 'follow this exact recipe' walkthrough output for a live run.

   • [claimed-docs] “Maritime gives it what a laptop session can't: its own always-on micro-VM in the cloud, persistent storage, sleep/wake economics, and every …”
   • [claimed-docs] “No key is required: the agent uses Maritime's metered LLM access by default.”
   • [claimed-docs] “Every ready-made template is a real agent framework, pre-configured and one image pull from live. Pick one, name it, deploy.”
   • [claimed-docs] “point Claude Code or your Maritime agent at a provider's runbook page and it can inventory the old provider with read-only credentials, recr…”
   • [claimed-docs] “Paste one prompt into Claude, Cursor, or any assistant and it can install the CLI, authenticate, and deploy agents for you.”

   proof ↗⚑ flag

Workloads

1. developerRun a headless browser or full desktop environment inside the sandbox

   weight 2 · round to Daytona
   Daytona✓fullclaimed8/10

   Daytona has a documented Computer Use feature providing mouse, keyboard, screenshot, screen recording, and display operations for GUI automation within sandboxes, plus Windows VM sandboxes for running desktop applications and tooling. This directly supports running headless browser/desktop environments inside the sandbox. Missing for 10: independent/hands-on verification of Computer Use in production and explicit mention of a headless browser package or Chrome/Chromium support out-of-the-box.

   • [claimed-docs] “Computer Use enables programmatic control of desktop environments within sandboxes. It provides mouse, keyboard, screenshot, screen recordin…”
   • [claimed-docs] “Computer Use enables programmatic control of desktop environments within sandboxes.”
   • [claimed-docs] “It provides mouse, keyboard, screenshot, screen recording, and display operations for automating GUI interactions and testing desktop applic…”
   • [claimed-docs] “Windows OS runtime in a virtual machine for running Windows applications and tooling.”
   • [claimed-docs] “Daytona also provides VM sandboxes with a dedicated Linux VM or Windows operating system”

   proof ↗⚑ flag

   Maritime✓fullclaimed7/10

   Maritime docs explicitly describe 'Computers': a persistent Linux desktop (XFCE, Chromium, LibreOffice) per user driven by a model, and the changelog confirms agents get a real Linux desktop with screenshot/mouse/keyboard control and live takeover — this covers full desktop environments including a browser (Chromium) inside the sandbox. missing for 10: no independent/hands-on corroboration of performance or headless-only browser mode, and no detail on resource limits or concurrency.

   • [claimed-docs] “A persistent Linux desktop (XFCE, Chromium, LibreOffice) for each of _your_ end users, driven by _your_ model.”
   • [claimed-docs] “Agents are getting a real Linux desktop they can drive: screenshot, mouse, keyboard, and a live view you can take over.”
   • [claimed-docs] “To hand out a key that reaches computers and nothing else, create a **Computers key** in the Computers section of Settings, API keys”

   proof ↗⚑ flag

2. developerAttach GPUs to sandboxed workloads

   weight 2 · round to Daytona
   Daytona✓fullclaimed8/10

   Daytona docs explicitly describe GPU sandboxes with NVIDIA and AMD GPU acceleration for model inference, fine-tuning, and CUDA/GPU-accelerated compute, directly matching the story. Missing for 10: no independent/hands-on confirmation of GPU attachment working in practice, and no detail on how GPU type/quantity is specified in SDK/CLI calls.

   • [claimed-docs] “GPU sandboxes with NVIDIA GPU acceleration for model inference, fine-tuning, and CUDA-accelerated compute.”
   • [claimed-docs] “GPU sandboxes with NVIDIA and AMD GPU acceleration for model inference, fine-tuning, and GPU-accelerated compute”

   proof ↗⚑ flag

   Maritime—none0/10

   No evidence anywhere in the pack mentions GPUs, GPU-accelerated instances, or hardware acceleration for sandboxed agent workloads; all compute descriptions reference micro-VMs, sleep/wake, and CPU-based containers only. Missing for 10: any mention of GPU attachment, GPU instance types, or hardware accelerator support.

   • [claimed-docs] “Maritime gives it what a laptop session can't: its own always-on micro-VM in the cloud, persistent storage, sleep/wake economics, and every …”
   • [claimed-docs] “Any Docker container becomes an agent: three endpoints get you sleep/wake, channels, and triggers.”

   proof ↗⚑ flag

Files

1. developerRead, write, upload, and download files in the sandbox filesystem via the SDK

   weight 2 · round to Daytona
   Daytona~partialclaimed5/10

   Daytona's SDK docs (Python/TypeScript) are described as a general interface for programmatically interacting with sandboxes, and volume docs mention shared file access ('read from large files instantly - no need to upload files manually'), implying some file I/O capability, but the evidence pack never explicitly documents dedicated SDK methods for reading, writing, uploading, or downloading individual files in the sandbox filesystem. missing for 10: explicit SDK fs.read_file/write_file/upload_file/download_file API documentation, code examples showing file upload/download via SDK, independent confirmation of these operations working.

   • [claimed-docs] “The Daytona TypeScript SDK provides a powerful interface for programmatically interacting with Daytona Sandboxes.”
   • [claimed-docs] “The Daytona Python SDK provides a robust interface for programmatically interacting with Daytona Sandboxes.”
   • [claimed-docs] “Volumes are FUSE-based mounts that provide shared file access across Daytona sandboxes.”
   • [claimed-docs] “They enable sandboxes to read from large files instantly - no need to upload files manually to each sandbox.”
   • [claimed-docs] “Volumes are FUSE-based mounts that provide shared file access across Daytona sandboxes. They enable sandboxes to read from large files insta…”
   • [claimed-docs] “sandbox = daytona.create() # Execute a command response = sandbox.process.exec("echo 'Hello, World!'")”

   proof ↗⚑ flag

   Maritime—none0/10

   No evidence pack item mentions SDK-based file read/write/upload/download operations against a sandbox filesystem; the docs cover agent provisioning, webhooks, auth scoping, triggers, computers (desktop), CLI, and migration but nothing about filesystem file I/O via SDK. Missing for full: no filesystem API/SDK method docs, no upload/download endpoint references, no code samples showing file operations.

   ⚑ flag

Runtimes

1. developerDefine custom sandbox templates or bring my own container image

   weight 2 · round to Daytona
   Daytona✓fullclaimed8/10

   Docs clearly show custom snapshot creation from a Dockerfile/image, defining base OS, runtimes, packages, and project-level setup, which Daytona builds into a snapshot used to create sandboxes — this directly satisfies bringing a custom container image/template. missing for 10: independent/hands-on verification of custom image builds and no explicit mention of registry-hosted image import or advanced build customization options.

   • [claimed-docs] “define the base operating system, language runtimes, packages, and project-level setup in an image or Dockerfile, and Daytona builds it into…”
   • [claimed-docs] “Snapshots are persistent, point-in-time captures of sandbox state, including the filesystem, installed packages, dependencies, and settings.”
   • [claimed-docs] “captures and persists a sandbox’s current state; container sandboxes capture filesystem state only (cold snapshots), VM sandboxes capture fi…”
   • [claimed-docs] “keep a configured number of pre-created, running sandboxes built from a snapshot; matching sandbox create requests claim one warm sandbox fr…”

   proof ↗⚑ flag

   Maritime~partialclaimed6/10

   Docs confirm bring-your-own-image support via a documented three-endpoint contract that works with any Docker image (CrewAI, LangGraph, AutoGen, custom scripts), plus a build log and template system for packaging custom agents. However, there's no detailed spec of the contract itself, no example custom Dockerfile, and no independent/hands-on confirmation of successfully running a fully custom image end-to-end. missing for 10: full technical spec of the three-endpoint contract, a worked example of a custom image deployment, independent verification of custom image support beyond docs claims.

   • [claimed-docs] “CrewAI, LangGraph, AutoGen, your own Python script: any Docker image that speaks a three-endpoint contract gets the whole platform.”
   • [claimed-docs] “Templates are cloneable agent packages; publishers can share them via link or the public library.”
   • [claimed-docs] “The agent page streams the build log live under **Overview → Builds**.”
   • [claimed-docs] “Every ready-made template is a real agent framework, pre-configured and one image pull from live. Pick one, name it, deploy.”

   proof ↗⚑ flag

2. developerExecute code in multiple language runtimes (Python, JavaScript, and more) and get rich results back

   weight 2 · round to Daytona
   Daytona✓fullclaimed8/10

   Daytona docs explicitly document code execution across multiple languages with both stateless and stateful/persistent interpreter contexts, backed by Python/TypeScript SDK examples and support for images/Dockerfiles defining custom language runtimes. Rich results are implied via persistent state, process exec responses, and structured sandbox tooling, though no explicit mention of rich output formats (e.g., images/plots/HTML) is shown. missing for 10: explicit documentation of rich/structured output formats (charts, images, HTML) and independent/hands-on confirmation of multi-language execution results.

   • [claimed-docs] “Run independent code snippets where each execution starts from a clean interpreter state... run Python code in a persistent interpreter cont…”
   • [claimed-docs] “You can run code snippets in multiple languages with support for both stateless execution and stateful interpretation with persistent contex…”
   • [claimed-docs] “Daytona provides methods to execute code in sandboxes. You can run code snippets in multiple languages with support for both stateless execu…”
   • [claimed-docs] “sandbox = daytona.create() # Execute a command response = sandbox.process.exec("echo 'Hello, World!'")”
   • [claimed-docs] “define the base operating system, language runtimes, packages, and project-level setup in an image or Dockerfile, and Daytona builds it into…”
   • [claimed-docs] “The Daytona Python SDK provides a robust interface for programmatically interacting with Daytona Sandboxes.”
   • [claimed-docs] “The Daytona TypeScript SDK provides a powerful interface for programmatically interacting with Daytona Sandboxes.”

   proof ↗⚑ flag

   Maritime—none0/10

   Maritime's evidence describes deploying persistent Docker-based agents (any Docker image with sleep/wake/channels/triggers), a Files API, and a Linux desktop 'Computers' feature, but nothing describes a code-execution/sandbox API that runs snippets in multiple language runtimes (Python, JS, etc.) and returns structured/rich results (e.g., stdout, images, dataframes) the way a code-interpreter tool would.

   • [claimed-docs] “Any Docker container becomes an agent: three endpoints get you sleep/wake, channels, and triggers.”
   • [claimed-docs] “A persistent Linux desktop (XFCE, Chromium, LibreOffice) for each of _your_ end users, driven by _your_ model.”
   • [claimed-docs] “The Maritime Files API: eight endpoints and both SDKs to browse, upload, download, and edit an agent's disk over HTTP, even while the agent …”
   • [claimed-docs] “CrewAI, LangGraph, AutoGen, your own Python script: any Docker image that speaks a three-endpoint contract gets the whole platform.”

   proof ↗⚑ flag

Untrusted code

1. developerExecute untrusted, AI-generated code without risking my own infrastructure

   weight 3 · round to Daytona
   Daytona✓fullprobed9/10

   Daytona's core product is isolated sandboxes with dedicated kernel/filesystem/network stack, network firewalling to prevent exfiltration, SDKs/CLI for programmatic code execution, and explicit positioning as infrastructure for running AI-generated code safely. This directly matches the story of executing untrusted AI-generated code without risking one's own infrastructure. Missing for 10: no independent third-party security audit or hands-on penetration test confirming isolation guarantees hold under adversarial conditions.

   • [claimed-docs] “Sandboxes are isolated runtime environments you can manage programmatically to run code.”
   • [claimed-docs] “Network limits control outbound internet access from sandboxes. Each sandbox runs behind a firewall that restricts which external IP address…”
   • [claimed-docs] “Each sandbox runs behind a firewall that restricts which external IP addresses and domains it can reach, preventing untrusted code from exfi…”
   • [claimed-docs] “Each sandbox runs in isolation, giving it a dedicated kernel, filesystem, network stack, and allocated vCPU, RAM, and disk.”
   • [probe] “PROBE llms.txt: HTTP 200 at https://www.daytona.io/llms.txt # Daytona > Daytona is a secure and elastic infrastructure for running AI-gener…”
   • [probe] “PROBE docs-md: HTTP 200 at https://www.daytona.io/docs/en/sandboxes.md # Sandboxes Daytona provides **full composable computers** — *…”
   • [claimed-docs] “sandbox = daytona.create() # Execute a command response = sandbox.process.exec("echo 'Hello, World!'")”

   proof ↗⚑ flag

   Maritime~partialprobed5/10

   Maritime hosts agents as Docker images on 'serverless infrastructure' (maritime-docs-1, maritime-probe-1) and provides persistent isolated Linux desktops per end user (maritime-docs-5), which implies agent code runs off the developer's own machines/infra. However, there is no explicit documentation of sandboxing guarantees, resource/network isolation, or security boundaries specifically for running untrusted or AI-generated code safely. Missing for 10: explicit sandbox/isolation security model, resource-limit guarantees, and any independent verification that untrusted code cannot escape or affect other tenants/infra.

   • [claimed-docs] “CrewAI, LangGraph, AutoGen, your own Python script: any Docker image that speaks a three-endpoint contract gets the whole platform.”
   • [claimed-docs] “A persistent Linux desktop (XFCE, Chromium, LibreOffice) for each of _your_ end users, driven by _your_ model.”
   • [probe] “PROBE llms.txt: HTTP 200 at https://maritime.sh/llms.txt # Maritime > Maritime is a cloud platform for deploying, hosting, and managing AI …”

   proof ↗⚑ flag

2. developerRun arbitrary shell commands and install packages inside the sandbox

   weight 2 · round to Daytona
   Daytona✓fullclaimed9/10

   Daytona sandboxes support running arbitrary commands via SDK (sandbox.process.exec), web terminal, SSH access, and CLI, plus snapshot/image building for installing packages and dependencies (Dockerfile/image-based setup). This directly covers shell command execution and package installation with strong first-party documentation across multiple interfaces. Missing for 10: independent hands-on verification specifically of arbitrary shell/package install workflows (community evidence covers other aspects like SSH glitches, not this exact capability).

   • [claimed-docs] “sandbox = daytona.create() # Execute a command response = sandbox.process.exec("echo 'Hello, World!'")”
   • [claimed-docs] “define the base operating system, language runtimes, packages, and project-level setup in an image or Dockerfile, and Daytona builds it into…”
   • [claimed-docs] “Daytona provides a browser-based web terminal for interacting with your sandboxes. The web terminal allows you to run commands, view files, …”
   • [claimed-docs] “Daytona provides SSH access to your sandboxes using token-based authentication. This allows you to connect from local terminals, IDEs, and d…”
   • [claimed-docs] “Daytona provides command-line access to core features for interacting with Daytona Sandboxes, including managing their lifecycle, snapshots,…”
   • [claimed-docs] “Run independent code snippets where each execution starts from a clean interpreter state... run Python code in a persistent interpreter cont…”

   proof ↗⚑ flag

   Maritime—none0/10

   Maritime's docs describe deploying agents via Docker images, templates, and a custom framework contract, but there is no evidence of an interactive shell/terminal access or arbitrary package installation inside a running sandbox for a developer to use directly. The 'Computers' feature offers a persistent desktop for end-users, not a shell for arbitrary command execution by the developer.

   ⚑ flag

Agent lifecycle

1. ai-native userMy agent can provision its own sandbox, execute code, read the results, and tear it down — end to end without a human

   weight 3 · round to Daytona
   Daytona✓fullprobed9/10

   Daytona provides SDKs (Python/TS), CLI, API, and an MCP server explicitly designed for AI agents to programmatically create sandboxes, execute code (stateless/stateful), read results, and destroy sandboxes without human intervention — with docs showing exact code like sandbox.create() and process.exec(). This is corroborated by an OpenAPI spec, agent-skill docs, and llms.txt describing Daytona as infrastructure for AI-generated code execution. Missing for 10: independent hands-on validation of a full agentic create-execute-teardown loop (current community evidence is older/unrelated to this workflow).

   • [claimed-docs] “Sandboxes are isolated runtime environments you can manage programmatically to run code.”
   • [claimed-docs] “Run independent code snippets where each execution starts from a clean interpreter state... run Python code in a persistent interpreter cont…”
   • [claimed-docs] “Daytona Model Context Protocol (MCP) server enables AI agents to interact with Daytona Sandboxes programmatically.”
   • [claimed-docs] “sandbox = daytona.create() # Execute a command response = sandbox.process.exec("echo 'Hello, World!'")”
   • [claimed-docs] “An agent skill for creating and running sandboxes on the Daytona platform. Includes references and practical patterns for the Daytona API, C…”
   • [probe] “PROBE docs-md: HTTP 200 at https://www.daytona.io/docs/en/sandboxes.md # Sandboxes Daytona provides **full composable computers** — *…”
   • [probe] “PROBE openapi: HTTP 200 at https://www.daytona.io/openapi.json — contains "openapi" key”
   • [probe] “official MCP server documented at https://www.daytona.io/docs/mcp”

   proof ↗⚑ flag

   Maritime~partialclaimed6/10

   Maritime's CLI and SDK let an agent provision (idempotent) itself, run in a sandboxed micro-VM/container, and be managed via authenticated API/CLI calls with JSON output designed for machine consumption, and agents can be torn down/put to sleep automatically when idle. However, the evidence is docs-only with no hands-on trace of an agent actually reading back code-execution results or explicit 'delete/destroy' teardown call, and no independent verification of the full autonomous loop. missing for 10: concrete evidence of an agent reading execution output programmatically, an explicit teardown/delete API example, and independent/hands-on confirmation of the full provision→execute→read→teardown loop.

   • [claimed-docs] “Tag each agent with your own id via `externalId`. `provision` is **idempotent** on it: it returns the existing agent if there is one, else c…”
   • [claimed-docs] “Pass --json on EVERY command. Success: one JSON value on stdout, stderr empty.”
   • [claimed-docs] “The Maritime CLI is built to be operated by other AI agents, not just humans: machine-readable output on every command, stable exit codes, a…”
   • [claimed-docs] “Authenticate with an env var, never interactively. Mint a long-lived key (`mk_…`) with `maritime keys create` and export it as `MARITIME_TOK…”
   • [claimed-docs] “Send a message and wait for the reply. Sleeping agents wake automatically.”
   • [claimed-docs] “provision is idempotent on it: it returns the existing agent if there is one, else creates a new one. Safe to call on every sign-in.”
   • [claimed-docs] “Any Docker container becomes an agent: three endpoints get you sleep/wake, channels, and triggers.”

   proof ↗⚑ flag

Isolation

1. platform-engineerRely on a documented hard isolation boundary (microVM or equivalent) between sandboxes and my systems

   weight 3 · round to Daytona
   Daytona~partialclaimed6/10

   Daytona documents each sandbox running with a dedicated kernel, filesystem, network stack, and VM sandboxes (dedicated Linux/Windows VM), plus firewall/network isolation, indicating some hard-isolation architecture, but the docs never explicitly name a specific microVM technology (e.g., Firecracker/gVisor) or provide a security whitepaper/threat model for platform engineers. missing for 10: explicit microVM/hypervisor technology name, formal security/isolation whitepaper or third-party audit, independent verification of isolation guarantees.

   • [claimed-docs] “Each sandbox runs in isolation, giving it a dedicated kernel, filesystem, network stack, and allocated vCPU, RAM, and disk.”
   • [claimed-docs] “Daytona also provides VM sandboxes with a dedicated Linux VM or Windows operating system”
   • [claimed-docs] “Network limits control outbound internet access from sandboxes. Each sandbox runs behind a firewall that restricts which external IP address…”
   • [claimed-docs] “Each sandbox runs behind a firewall that restricts which external IP addresses and domains it can reach, preventing untrusted code from exfi…”
   • [claimed-docs] “captures and persists a sandbox’s current state; container sandboxes capture filesystem state only (cold snapshots), VM sandboxes capture fi…”

   proof ↗⚑ flag

   Maritime—none0/10

   No evidence pack item documents a specific isolation mechanism (microVM, gVisor, container hardening, VM boundary) between sandboxes and host systems; docs mention Docker images, serverless infra, and scoped API keys but not the underlying execution isolation boundary. Missing for 10: any explicit statement of sandbox isolation technology, security model documentation, or third-party audit/confirmation of hard isolation.

   ⚑ flag

2. ai-native userGive an agent a sandbox where host secrets and credentials are unreachable by the code it runs

   weight 2 · round to Daytona
   Daytona✓fullprobed8/10

   Daytona sandboxes run in fully isolated environments with dedicated kernel, filesystem, and network stack separate from the host (docs-28), and network egress is firewalled per-sandbox to prevent exfiltration (docs-19, docs-26), meaning code executed via the SDK/MCP server never has access to the host machine or its credentials by construction. missing for 10: explicit documentation of secret/credential injection or scoping mechanisms, and independent/hands-on confirmation that host secrets specifically cannot leak (only architectural isolation claims).

   • [claimed-docs] “Each sandbox runs in isolation, giving it a dedicated kernel, filesystem, network stack, and allocated vCPU, RAM, and disk.”
   • [claimed-docs] “Network limits control outbound internet access from sandboxes. Each sandbox runs behind a firewall that restricts which external IP address…”
   • [claimed-docs] “Each sandbox runs behind a firewall that restricts which external IP addresses and domains it can reach, preventing untrusted code from exfi…”
   • [claimed-docs] “Sandboxes are isolated runtime environments you can manage programmatically to run code.”
   • [probe] “PROBE docs-md: HTTP 200 at https://www.daytona.io/docs/en/sandboxes.md # Sandboxes Daytona provides **full composable computers** — *…”

   proof ↗⚑ flag

   Maritime~partialclaimed6/10

   Maritime's docs describe per-agent micro-VM isolation with secrets 'injected only into that agent's VM' and encrypted at rest, plus scoped API keys so a leaked key 'can only do what its scope allows,' which together imply host-level credentials aren't reachable by agent code. However, there's no explicit documentation of sandbox mechanics like blocking cloud metadata endpoints, filesystem isolation from the host, or independent security testing confirming secrets are truly unreachable. missing for 10: explicit host-credential isolation guarantees (e.g., no metadata service access, filesystem boundary details), independent/hands-on security verification.

   • [claimed-docs] “Variables are **secret by default**: stored encrypted at rest, injected only into that agent's VM, and always masked in the UI.”
   • [claimed-docs] “Hand a subsystem the narrowest key it needs. A leaked key can then only do what its scope allows.”
   • [claimed-docs] “To hand out a key that reaches computers and nothing else, create a **Computers key** in the Computers section of Settings, API keys”
   • [claimed-docs] “a key whose only scope is `computers` is refused on every other Maritime endpoint”
   • [claimed-docs] “Every key carries scopes. Hand a subsystem the _narrowest_ key it needs. A leaked key can then only do what its scope allows.”

   proof ↗⚑ flag

Network policy

1. platform-engineerRestrict or allow the sandbox's network egress with explicit policy

   weight 3 · round to Daytona
   Daytona✓fullclaimed8/10

   Daytona documents explicit network egress policy controls via firewall parameters set at sandbox creation or updated at runtime, restricting which IPs/domains a sandbox can reach to prevent data exfiltration—directly matching the platform-engineer's egress policy story. Missing for 10: no independent/hands-on verification of the firewall enforcement or examples of complex allow/deny rule syntax.

   • [claimed-docs] “Set firewall parameters when creating a sandbox or update them while the sandbox is running.”
   • [claimed-docs] “Network limits control outbound internet access from sandboxes. Each sandbox runs behind a firewall that restricts which external IP address…”
   • [claimed-docs] “Each sandbox runs behind a firewall that restricts which external IP addresses and domains it can reach, preventing untrusted code from exfi…”

   proof ↗⚑ flag

   Maritime—none0/10

   No evidence pack item mentions network egress controls, firewall rules, or any explicit allow/deny policy for outbound traffic from the sandbox/agent VM; the closest material covers API key scoping and secret management, not network policy.

   ⚑ flag

1. ai-native userDo everything through the API that I can do in the UI

   weight 2 · round to Daytona
   Daytona~partialprobed7/10

   Daytona exposes a full OpenAPI spec, Python/TypeScript SDKs, and a CLI covering sandbox lifecycle, snapshots, volumes, execution, and network limits, indicating strong API-first design consistent with programmatic parity to UI actions. However, there is no explicit documentation enumerating every UI feature and confirming 1:1 API coverage, so full parity is implied rather than proven. Missing for 10: explicit UI-to-API feature parity statement, independent confirmation that all dashboard actions (e.g., billing, org management) are also API-exposed.

   • [probe] “PROBE openapi: HTTP 200 at https://www.daytona.io/openapi.json — contains "openapi" key”
   • [claimed-docs] “The Daytona TypeScript SDK provides a powerful interface for programmatically interacting with Daytona Sandboxes.”
   • [claimed-docs] “The Daytona Python SDK provides a robust interface for programmatically interacting with Daytona Sandboxes.”
   • [claimed-docs] “Daytona provides command-line access to core features for interacting with Daytona Sandboxes, including managing their lifecycle, snapshots,…”
   • [claimed-docs] “Sandboxes are isolated runtime environments you can manage programmatically to run code.”
   • [claimed-docs] “Snapshots are persistent, point-in-time captures of sandbox state, including the filesystem, installed packages, dependencies, and settings.”
   • [claimed-docs] “Volumes are FUSE-based mounts that provide shared file access across Daytona sandboxes.”

   proof ↗⚑ flag

   Maritime~partialprobed6/10

   Docs show a broad API/SDK/CLI surface (provisioning, keys, webhooks, triggers, computers, templates, builds) and CLI --json mode implying the CLI itself is just an API wrapper, suggesting an API-first architecture. However there's no explicit statement of full UI/API parity and no discoverable OpenAPI spec (probes return 404s for all schema locations), so completeness of API coverage relative to every UI feature can't be confirmed. missing for 10: explicit parity statement, published OpenAPI/API reference, independent confirmation that every UI action has an API equivalent.

   • [claimed-docs] “Mint a personal API key with `maritime keys create` and send it as a Bearer token, the same token the CLI itself uses.”
   • [claimed-docs] “Pass --json on EVERY command. Success: one JSON value on stdout, stderr empty.”
   • [claimed-docs] “Tag each agent with your own id via `externalId`. `provision` is **idempotent** on it: it returns the existing agent if there is one, else c…”
   • [claimed-docs] “Hand a subsystem the narrowest key it needs. A leaked key can then only do what its scope allows.”
   • [claimed-docs] “A persistent Linux desktop (XFCE, Chromium, LibreOffice) for each of _your_ end users, driven by _your_ model.”
   • [claimed-docs] “The agent page streams the build log live under **Overview → Builds**.”
   • [claimed-docs] “Templates are cloneable agent packages; publishers can share them via link or the public library.”
   • [probe] “PROBE openapi: all candidate paths 404 (https://maritime.sh/openapi.json, https://maritime.sh/swagger.json, https://maritime.sh/api/openapi.…”

   proof ↗⚑ flag

2. ai-native userExport all of my data in open formats and leave

   weight 3 · round to Daytona
   Daytona~partialclaimed4/10

   Daytona's core repo is open source and free to fork/self-host (daytona-gh-1), and its CLI/SDK/SSH access let users programmatically pull files, snapshots, and volumes out of sandboxes (daytona-docs-12, daytona-docs-31, daytona-docs-32), giving a de facto path to extract data. However there is no documented bulk 'export all data' feature, no stated open data format for exports, and no explicit anti-lock-in/portability commitment. Missing for 10: an explicit data-export tool or guarantee, documented open format for exported snapshots/volumes, and any statement addressing account/data portability upon leaving.

   • [github] “It remains public and free to use, fork, and build on under the LICENSE, as is and without support or warranty.”
   • [claimed-docs] “Daytona provides command-line access to core features for interacting with Daytona Sandboxes, including managing their lifecycle, snapshots,…”
   • [claimed-docs] “Volumes are FUSE-based mounts that provide shared file access across Daytona sandboxes. They enable sandboxes to read from large files insta…”
   • [claimed-docs] “Daytona provides SSH access to your sandboxes using token-based authentication. This allows you to connect from local terminals, IDEs, and d…”
   • [claimed-docs] “captures and persists a sandbox’s current state; container sandboxes capture filesystem state only (cold snapshots), VM sandboxes capture fi…”

   proof ↗⚑ flag

   Maritime—none0/10

   Evidence shows import/migration INTO Maritime (from AWS/Heroku/VPS, .env bulk import) and a CLI with --json output, but nothing documents exporting agent configs, data, secrets, or state OUT in open formats to leave the platform. missing for 10: an export command or documented data-portability feature, open-format export of agent state/configs, any migration-out guide.

   • [claimed-docs] “Bulk import from a .env file (or stdin), then hot-reload”
   • [claimed-docs] “Migrate from AWS, Heroku, or a VPS”
   • [claimed-docs] “Pass --json on EVERY command. Success: one JSON value on stdout, stderr empty.”

   proof ↗⚑ flag

3. ai-native userRead the product's source under an open license

   weight 2 · round to Daytona
   Daytona!disputedcontradicted4/10

   The GitHub repo evidence states Daytona 'remains public and free to use, fork, and build on under the LICENSE' (daytona-gh-1), suggesting an open-license posture, but community evidence directly reports that Daytona went closed source, with users criticizing it as 'security by obscurity' (daytona-comm-6) — a concrete contradiction rather than mere skepticism. Missing for 10: a specific named OSI license, clarity on current licensing status post-controversy, and independent confirmation of what is actually open today.

   • [github] “It remains public and free to use, fork, and build on under the LICENSE, as is and without support or warranty.”
   • [community] “Community reaction to Daytona going closed source was skeptical: 'The vulnerabilities will still easily be found without the source. This is…”

   proof ↗⚑ flag

   Maritime—none0/10

   Maritime is a closed commercial SaaS platform; there is no evidence of an open-source license or public source repository anywhere in the docs or probes. missing for 10: an open-source license, a public source code repository, any mention of licensing terms for the platform's own code.

   ⚑ flag

4. ai-native userSelf-host the core product

   weight 3 · round to Daytona
   Daytona!disputedcontradicted4/10

   The Daytona GitHub repo states the project 'remains public and free to use, fork, and build on under the LICENSE,' implying self-hosting is possible, and docs show programmatic sandbox management that could run locally. However, community evidence reports Daytona later 'going closed source,' with users explicitly skeptical of the change, directly contradicting the self-hostable/forkable claim. Missing for 10: clear current licensing status, self-hosting setup/deployment docs, and confirmation the core sandbox runtime (not just SDKs/CLI) can run outside Daytona's hosted infrastructure.

   • [github] “It remains public and free to use, fork, and build on under the LICENSE, as is and without support or warranty.”
   • [community] “Community reaction to Daytona going closed source was skeptical: 'The vulnerabilities will still easily be found without the source. This is…”

   proof ↗⚑ flag

   Maritime—none0/10

   Maritime is explicitly described as a cloud platform with agents running on serverless infrastructure, and no documentation offers a self-hosted deployment option for the core platform itself (Docker images are for agent frameworks running atop Maritime's cloud, not for self-hosting Maritime).

   • [probe] “PROBE llms.txt: HTTP 200 at https://maritime.sh/llms.txt # Maritime > Maritime is a cloud platform for deploying, hosting, and managing AI …”
   • [claimed-docs] “Migrate from AWS, Heroku, or a VPS”

   proof ↗⚑ flag

Latency

1. developerStart sandboxes with documented sub-second-to-few-second cold starts

   weight 3 · round to Daytona
   Daytona~partialclaimed6/10

   Daytona's docs claim sandboxes spin up 'in milliseconds' and that warm pools let sandboxes be claimed 'instantly' from pre-created pools, directly supporting fast cold-start claims. However, there are no concrete benchmark numbers (e.g., specific p50/p95 latency stats) and no independent/hands-on corroboration of actual startup times in the evidence pack. Missing for 10: quantified latency benchmarks, independent/third-party timing verification, and details on cold vs warm start differences beyond marketing copy.

   • [claimed-docs] “Daytona lets you spin up sandboxes in milliseconds and shut them down just as fast. Use what you need, when you need it.”
   • [claimed-docs] “keep a configured number of pre-created, running sandboxes built from a snapshot; matching sandbox create requests claim one warm sandbox fr…”
   • [claimed-docs] “keep a configured number of pre-created, running sandboxes built from a snapshot; matching sandbox create requests claim one warm sandbox fr…”
   • [claimed-docs] “Use what you need, when you need it. $200 in free compute included.”

   proof ↗⚑ flag

   Maritime—none0/10

   Maritime's docs describe sleep/wake behavior (e.g., 'Sleeping agents wake automatically', 'puts it to sleep when idle, and wakes it on the next visit') and an 'always-on' add-on for millisecond reaction, but no evidence anywhere documents actual cold-start latency numbers (sub-second to few-second) for waking a sandboxed agent from sleep.

   • [claimed-docs] “Send a message and wait for the reply. Sleeping agents wake automatically.”
   • [claimed-docs] “Any repo with a Dockerfile can be served on a public, no-login HTTPS URL. Maritime builds it, runs it serverlessly, puts it to sleep when id…”
   • [claimed-docs] “Always-on is a flat add-on for the rare agent that must react in milliseconds around the clock.”

   proof ↗⚑ flag

Scale

1. platform-engineerRun large concurrent fleets of sandboxes with documented concurrency limits

   weight 3 · round drawn
   Daytona—none0/10

   Evidence covers sandbox lifecycle, snapshots, warm pools, GPU/VM options, and SDKs, but no documented concurrency limits, fleet-scale benchmarks, or per-account/org sandbox quotas are given anywhere in the pack. Missing for 10: documented concurrency/rate limits, fleet-scale benchmarks or case studies, quota/scaling guidance for platform engineers.

   • [claimed-docs] “Daytona lets you spin up sandboxes in milliseconds and shut them down just as fast. Use what you need, when you need it.”
   • [claimed-docs] “keep a configured number of pre-created, running sandboxes built from a snapshot; matching sandbox create requests claim one warm sandbox fr…”
   • [claimed-docs] “keep a configured number of pre-created, running sandboxes built from a snapshot; matching sandbox create requests claim one warm sandbox fr…”

   proof ↗⚑ flag

   Maritime—none0/10

   No evidence of documented concurrency limits or guidance for running large fleets of sandboxes at scale; docs cover provisioning, idempotency, and billing per-agent but never state concurrency ceilings, throughput benchmarks, or fleet-scale operational guidance.

   ⚑ flag

2. developerKeep a sandbox session running for hours or days for long agent tasks

   weight 2 · round to Maritime
   Daytona—none0/10

   Docs describe fast sandbox creation/teardown, snapshots, and warm pools, but there is no evidence of explicit support for sustaining a single sandbox session continuously for hours or days (e.g., keep-alive settings, max runtime limits, or session persistence guarantees) — the messaging even emphasizes ephemeral 'spin up in milliseconds, shut down just as fast' usage. missing for 10: explicit documentation of long-running session support, auto-stop/keep-alive controls, or customer evidence of multi-hour/day agent tasks sustained in a single sandbox.

   • [claimed-docs] “Daytona lets you spin up sandboxes in milliseconds and shut them down just as fast. Use what you need, when you need it.”
   • [claimed-docs] “Sandboxes are isolated runtime environments you can manage programmatically to run code.”
   • [claimed-docs] “keep a configured number of pre-created, running sandboxes built from a snapshot; matching sandbox create requests claim one warm sandbox fr…”

   proof ↗⚑ flag

   Maritime~partialprobed5/10

   Maritime advertises serverless infra by default with an 'always-on' add-on for agents that must run continuously, and a persistent Linux desktop model for end users, suggesting long-lived sessions are possible but only via a paid add-on rather than a default guarantee for hours/days-long sandbox sessions. missing for 10: explicit documentation of session/session-timeout limits, confirmation that a sandbox session can run unattended for hours or days without the always-on add-on, and independent verification of long-running task stability.

   • [claimed-docs] “Always-on is a flat add-on for the rare agent that must react in milliseconds around the clock.”
   • [claimed-docs] “A persistent Linux desktop (XFCE, Chromium, LibreOffice) for each of _your_ end users, driven by _your_ model.”
   • [probe] “PROBE llms.txt: HTTP 200 at https://maritime.sh/llms.txt # Maritime > Maritime is a cloud platform for deploying, hosting, and managing AI …”

   proof ↗⚑ flag

Pricing

1. developerStart building with a free tier or included credits without talking to sales

   weight 1 · round drawn
   Daytona✓fullclaimed8/10

   Daytona's pricing page explicitly offers $200 in free compute included and lets developers start immediately ('spin up sandboxes in milliseconds... use what you need, when you need it'), plus the GitHub repo is public/free to use without needing sales contact. Missing for 10: independent confirmation of the free-tier signup flow/limits and no evidence of any sales-gated onboarding step.

   • [claimed-docs] “Use what you need, when you need it. $200 in free compute included.”
   • [claimed-docs] “Daytona lets you spin up sandboxes in milliseconds and shut them down just as fast. Use what you need, when you need it.”
   • [github] “It remains public and free to use, fork, and build on under the LICENSE, as is and without support or warranty.”

   proof ↗⚑ flag

   Maritime✓fullclaimed8/10

   Docs explicitly state the free plan includes 3 agents with no card required, and quickstart/CLI docs show a self-serve path (npm install, deploy in under 5 minutes) with no sales contact implied anywhere. missing for 10: independent/third-party confirmation of the free tier's real-world limits and no hands-on verification of the signup flow.

   • [claimed-docs] “the free plan includes 3 agents, no card required”
   • [claimed-docs] “Deploy your first agent and talk to it, in under 5 minutes.”
   • [claimed-docs] “npm install -g maritime-cli”
   • [claimed-docs] “One subscription, a set number of agents, flat prices for extras. No meters, no hourly rates: how much your agents run never changes the bil…”

   proof ↗⚑ flag

2. platform-engineerPay per second only for the compute a sandbox actually uses

   weight 3 · round to Daytona
   Daytona~partialclaimed6/10

   Daytona's pricing page emphasizes millisecond spin-up/teardown and 'use what you need, when you need it' with $200 free compute credit, implying usage-based billing, but the evidence never states an explicit per-second billing granularity or rate card. Missing for 10: explicit per-second billing rate documentation, independent confirmation of billing granularity, and detail on how partial-second usage is metered/rounded.

   • [claimed-docs] “Daytona lets you spin up sandboxes in milliseconds and shut them down just as fast. Use what you need, when you need it.”
   • [claimed-docs] “Use what you need, when you need it. $200 in free compute included.”

   proof ↗⚑ flag

   Maritime—none0/10

   Maritime's own billing docs explicitly state the opposite of per-second usage billing: 'One subscription, a set number of agents, flat prices for extras. No meters, no hourly rates: how much your agents run never changes the bill' and 'Always-on is a flat add-on.' There is no evidence of any per-second or usage-metered compute pricing for sandboxes.

   • [claimed-docs] “One subscription, a set number of agents, flat prices for extras. No meters, no hourly rates: how much your agents run never changes the bil…”
   • [claimed-docs] “Always-on is a flat add-on for the rare agent that must react in milliseconds around the clock.”

   proof ↗⚑ flag

1. ai-native userChoose where my data is stored (region/residency)

   weight 2 · round drawn
   Daytona—none0/10

   No evidence in the pack mentions data residency, region selection, or geographic storage controls for sandboxes/snapshots/volumes; missing for 10: any documentation of selectable regions/data centers, compliance/residency guarantees, or admin controls for data location.

   ⚑ flag

   Maritime—none0/10

   No evidence in the pack mentions data residency, region selection, or geographic storage controls anywhere in Maritime's docs or probes; nothing addresses where agent/user data is physically stored.

   ⚑ flag

2. ai-native userPrevent my data from being used to train AI models

   weight 3 · round drawn
   Daytona—none0/10

   No evidence in the pack addresses AI training data usage, data retention policies, or opt-out mechanisms for model training; the evidence covers sandboxing, SDKs, and infrastructure but not privacy/data-training posture.

   ⚑ flag

   Maritime—none0/10

   No evidence in the pack addresses data-training opt-out or AI model training policies for Maritime; the docs cover deployment, hosting, keys, CLI, etc., but nothing about preventing customer data from being used to train AI models.

   ⚑ flag

3. ai-native userControl data retention and deletion

   weight 2 · round drawn
   Daytona—none0/10

   No evidence pack items address data retention policies, deletion controls, or data lifecycle management for sandboxes/volumes/snapshots; the docs cover sandbox creation, execution, networking and SDKs but nothing on retention/deletion controls a user can configure.

   ⚑ flag

   Maritime—none0/10

   No evidence describes data retention policies, deletion controls, or export/purge mechanisms for agent data, files, or session logs; docs mention encrypted-at-rest secrets and append-only session logs but nothing about user-controlled retention or deletion.

   ⚑ flag

4. ai-native userOpt out of telemetry and usage tracking

   weight 2 · round drawn
   Daytona—none0/10

   No evidence pack items mention telemetry, usage tracking, analytics collection, or an opt-out mechanism for Daytona; the axis applies (any cloud/SaaS dev platform could document such settings) but no such documentation or capability is evidenced.

   ⚑ flag

   Maritime—none0/10

   No evidence pack item mentions telemetry, usage tracking, analytics collection, or an opt-out mechanism anywhere in Maritime's docs; the closest topics (scoped API keys, secret variables) address access control, not telemetry preferences.

   ⚑ flag

Lifecycle

1. developerSet timeouts so sandboxes shut down automatically and stop billing when idle or done

   weight 2 · round drawn
   Daytona—none0/10

   The evidence pack shows fast sandbox creation/teardown and general lifecycle management (docs-14, docs-12) but contains no mention of configurable idle/auto-stop timeouts or billing-tied automatic shutdown settings. missing for 10: explicit auto-stop/idle-timeout configuration option, documentation on billing behavior when idle, confirmation that timeout can be set per-sandbox.

   • [claimed-docs] “Daytona lets you spin up sandboxes in milliseconds and shut them down just as fast. Use what you need, when you need it.”
   • [claimed-docs] “Daytona provides command-line access to core features for interacting with Daytona Sandboxes, including managing their lifecycle, snapshots,…”

   proof ↗⚑ flag

   Maritime—none0/10

   The docs describe Maritime as serverless agent hosting and mention an 'always-on' paid add-on (implying default agents are not always running), but no evidence describes a developer-configurable idle timeout or automatic shutdown-to-stop-billing mechanism. Missing for 10: any documented timeout setting, idle-detection policy, or billing-pause behavior tied to inactivity.

   • [claimed-docs] “Always-on is a flat add-on for the rare agent that must react in milliseconds around the clock.”

   proof ↗⚑ flag

2. developerSpin up an isolated sandbox with one API/SDK call and get a live environment in seconds

   weight 3 · round to Daytona
   Daytona✓fullprobed9/10

   Daytona's docs and SDK examples show one-call sandbox creation (daytona.create()), millisecond-scale spin-up/teardown, and isolated per-sandbox runtime with dedicated kernel/filesystem/network, backed by Python/TS SDKs, CLI, and OpenAPI-documented REST API. Community evidence (comm-4, comm-5) independently corroborates ease-of-use versus alternatives like DevPod. Missing for 10: no independent hands-on benchmark verifying actual 'seconds' provisioning latency claim beyond vendor marketing copy.

   • [claimed-docs] “sandbox = daytona.create() # Execute a command response = sandbox.process.exec("echo 'Hello, World!'")”
   • [claimed-docs] “Daytona lets you spin up sandboxes in milliseconds and shut them down just as fast. Use what you need, when you need it.”
   • [claimed-docs] “Each sandbox runs in isolation, giving it a dedicated kernel, filesystem, network stack, and allocated vCPU, RAM, and disk.”
   • [claimed-docs] “The Daytona TypeScript SDK provides a powerful interface for programmatically interacting with Daytona Sandboxes.”
   • [claimed-docs] “The Daytona Python SDK provides a robust interface for programmatically interacting with Daytona Sandboxes.”
   • [claimed-docs] “Daytona provides command-line access to core features for interacting with Daytona Sandboxes, including managing their lifecycle, snapshots,…”
   • [probe] “PROBE openapi: HTTP 200 at https://www.daytona.io/openapi.json — contains "openapi" key”
   • [community] “Comparing to DevPod: 'this looks like a step up in ease from faffing about with DevPod. Then again I'm a command line guy all the way, and D…”

   proof ↗⚑ flag

   Maritime✓fullprobed7/10

   Maritime's SDK exposes an idempotent `provision` call that creates/returns a live agent environment, and docs claim full agent deployment (image pull → live, chat-ready) in under 5 minutes, with CLI/API equivalents (`maritime keys create`, RESTful API). This matches the 'one call, live environment fast' story well, though the fastest documented time is minutes not literal seconds and there's no independent/hands-on latency benchmark or public OpenAPI spec confirming the exact call shape. Missing for 10: independent timing/benchmark evidence corroborating 'seconds', and a verifiable OpenAPI/API schema (probe shows openapi.json 404).

   • [claimed-docs] “Tag each agent with your own id via `externalId`. `provision` is **idempotent** on it: it returns the existing agent if there is one, else c…”
   • [claimed-docs] “provision is idempotent on it: it returns the existing agent if there is one, else creates a new one. Safe to call on every sign-in.”
   • [claimed-docs] “Deploy your first agent and talk to it, in under 5 minutes.”
   • [claimed-docs] “Every ready-made template is a real agent framework, pre-configured and one image pull from live. Pick one, name it, deploy.”
   • [claimed-docs] “RESTful API for managing agents programmatically.”
   • [claimed-docs] “The Maritime CLI lets you deploy and manage agents from your terminal.”
   • [probe] “PROBE openapi: all candidate paths 404 (https://maritime.sh/openapi.json, https://maritime.sh/swagger.json, https://maritime.sh/api/openapi.…”

   proof ↗⚑ flag

Preview access

1. developerExpose a port from the sandbox on a public preview URL to reach services running inside

   weight 2 · round to Maritime
   Daytona—none0/10

   The evidence pack covers sandboxes, snapshots, volumes, network limits, SSH access, web terminal, and SDKs, but contains no mention of preview URLs or exposing a running service's port publicly from a sandbox. No documentation item addresses port forwarding or preview link generation.

   ⚑ flag

   Maritime~partialclaimed6/10

   Maritime's docs describe deploying any Dockerfile-based repo to a public, no-login HTTPS URL that reaches the running container's service, matching the core story of exposing an internal service via a public preview URL (maritime-docs-29, maritime-docs-47). However, the docs never explicitly describe port-selection mechanics (e.g., multiple ports, custom port binding) and all evidence is first-party with no independent/hands-on corroboration. missing for 10: explicit port-selection/multi-port docs, independent verification that the public URL reaches an arbitrary internal port.

   • [claimed-docs] “Any repo with a Dockerfile can be served on a public, no-login HTTPS URL. Maritime builds it, runs it serverlessly, puts it to sleep when id…”
   • [claimed-docs] “Any repo with a Dockerfile can be served on a public, no-login HTTPS URL.”

   proof ↗⚑ flag

Snapshots

1. developerPause a running sandbox and resume it later with filesystem and memory state intact

   weight 2 · round to Daytona
   Daytona~partialclaimed6/10

   Daytona docs confirm snapshots capture filesystem and, for VM sandboxes, memory state ('hot snapshots'), enabling pause/resume with state intact, while container sandboxes only get filesystem-only 'cold snapshots'. There's no independent/hands-on evidence confirming actual pause/resume behavior in practice or performance at scale. missing for 10: hands-on/independent verification of resume fidelity, explicit pause/resume API documentation (vs. snapshot creation), and confirmation that memory state persistence works reliably outside VM-only sandboxes.

   • [claimed-docs] “Snapshots are persistent, point-in-time captures of sandbox state, including the filesystem, installed packages, dependencies, and settings.”
   • [claimed-docs] “captures and persists a sandbox’s current state; container sandboxes capture filesystem state only (cold snapshots), VM sandboxes capture fi…”
   • [claimed-docs] “define the base operating system, language runtimes, packages, and project-level setup in an image or Dockerfile, and Daytona builds it into…”

   proof ↗⚑ flag

   Maritime—none0/10

   No evidence in the pack mentions pausing/suspending a sandbox or preserving filesystem and memory state across a pause/resume cycle; docs cover provisioning, webhooks, triggers, templates, and CLI/API auth but nothing about snapshot or suspend-resume persistence.

   ⚑ flag

2. developerSnapshot a sandbox and later restore or fork new sandboxes from that snapshot

   weight 3 · round to Daytona
   Daytona✓fullclaimed8/10

   Daytona docs explicitly describe snapshots as persistent point-in-time captures of sandbox filesystem/state (docs-2, docs-22), created from images/Dockerfiles or existing sandboxes (docs-21), and used to create new sandboxes/pools (docs-23/30), directly matching the snapshot-then-restore/fork workflow, with SDK/CLI support for managing snapshots (docs-11, docs-12). Missing for 10: independent/hands-on verification of fork/restore behavior beyond docs, and no explicit example code showing a restore-from-snapshot call.

   • [claimed-docs] “Snapshots are persistent, point-in-time captures of sandbox state, including the filesystem, installed packages, dependencies, and settings.”
   • [claimed-docs] “define the base operating system, language runtimes, packages, and project-level setup in an image or Dockerfile, and Daytona builds it into…”
   • [claimed-docs] “captures and persists a sandbox’s current state; container sandboxes capture filesystem state only (cold snapshots), VM sandboxes capture fi…”
   • [claimed-docs] “keep a configured number of pre-created, running sandboxes built from a snapshot; matching sandbox create requests claim one warm sandbox fr…”
   • [claimed-docs] “keep a configured number of pre-created, running sandboxes built from a snapshot; matching sandbox create requests claim one warm sandbox fr…”
   • [claimed-docs] “Daytona provides command-line access to core features for interacting with Daytona Sandboxes, including managing their lifecycle, snapshots,…”

   proof ↗⚑ flag

   Maritime—none0/10

   No evidence in the pack mentions snapshotting a sandbox/agent state or restoring/forking new instances from such a snapshot; templates (maritime-docs-7) allow cloning a pre-configured agent package but not capturing/restoring runtime state.

   ⚑ flag