Model Gateways & Routers Arena
Requesty vs Kong AI Gateway
Kong AI Gateway wins · 11–14 (18 drawn)
Agenticness — how well agents can access and operate the productAgenticness
How well agents can access and operate the product
Agent access
ai-native userPoint an agent at llms.txt or agent-oriented docs
weight 2 · round drawnRequesty's docs site serves a working llms.txt (HTTP 200) with a structured index, plus .md variants of docs pages that explicitly point agents to llms.txt for the full documentation index, directly enabling an agent to be pointed at agent-oriented docs. missing for 10: no independent/third-party confirmation of agents actually consuming this successfully, and no dedicated docs page describing the llms.txt feature itself.
- [probe] “PROBE llms.txt: HTTP 200 at https://docs.requesty.ai/llms.txt # Requesty > Requesty is a unified LLM gateway and OpenAI-compatible API for …”
- [probe] “PROBE docs-md: HTTP 200 at https://docs.requesty.ai/quickstart.md > ## Documentation Index > Fetch the complete documentation index at: http…”
A probe confirms developer.konghq.com/llms.txt returns HTTP 200 with a valid llms.txt manifest listing Kong's docs (including AI Gateway), and individual doc pages are also served as .md files (e.g. ai-providers.md, a2a.md), making the docs directly agent-consumable. missing for 10: no explicit vendor statement instructing agents to use llms.txt, and the generic /.md probe for the ai-gateway index returned 404, showing coverage is per-page rather than fully uniform.
- [probe] “PROBE llms.txt: HTTP 200 at https://developer.konghq.com/llms.txt # developer.konghq.com > Kong's developer documentation for Kong Gateway,…”
- [claimed-docs] “The core of AI Gateway is the ability to serve AI Models from various AI Model Providers via a provider-agnostic API.”
- [probe] “PROBE docs-md: HTTP 404 at https://developer.konghq.com/ai-gateway/.md”
ai-native userRun the product headlessly / in CI for automation
weight 2 · round drawnRequesty is an OpenAI-compatible API gateway accessed via simple base_url swap, meaning any script or CI pipeline using the OpenAI SDK can call it headlessly without a UI, and BYOK/API-key auth supports non-interactive use. However, there is no explicit documentation or example showing CI/automation setup (e.g., GitHub Actions, headless auth flow, or CLI for pipelines). Missing for 10: explicit CI/automation docs or examples, headless auth/token provisioning guidance, independent confirmation of CI usage.
- [claimed-docs] “If you're already using the OpenAI SDK, point it at Requesty and you're done. No SDK changes, no new client to learn.”
- [claimed-docs] “base_url="https://router.requesty.ai/v1", # was: https://api.openai.com/v1”
- [claimed-docs] “Bring Your Own Keys (BYOK) allows you to use your personal API keys from various providers with Requesty.”
Kong AI Gateway exposes a CLI (kongctl) and Konnect APIs (list-ai-gateways, config conversion) plus a quickstart script, implying it can be driven headlessly and scripted rather than only via UI. However there is no explicit CI or CD pipeline example, declarative config-as-code workflow, or GitOps documentation demonstrating automated headless operation in CI. Missing for 10: explicit CI or CD pipeline documentation, dedicated headless-mode guide, independent CI usage report.
- [claimed-docs] “list-ai-gateways summary: List AI Gateways description: Returns a list of AI Gateways in the organization.”
- [claimed-docs] “walks you through migrating an existing configuration using the kongctl AI Gateway conversion extension”
- [claimed-docs] “You can use the quickstart script to get a demo instance of AI Gateway running almost instantly.”
- [probe] “official CLI documented at https://developer.konghq.com/kongctl/”
ai-native userPlug MCP servers into this product so it can use their tools
weight 3 · round to RequestyRequesty's MCP Gateway explicitly lets AI coding assistants (Claude Code, Cursor, Roo Code) connect to MCP servers through Requesty's unified API, directly matching the story. missing for 10: deeper documentation on configuring specific MCP servers and independent/hands-on corroboration of the gateway working in practice.
- [claimed-docs] “The MCP (Model Context Protocol) Gateway enables AI coding assistants like Claude Code, Cursor, and Roo Code to securely connect to MCP serv…”
Kong AI Gateway documents an 'AI MCP Proxy' capability that explicitly can front upstream MCP servers (in addition to converting REST APIs into MCP tools), meaning it can plug in external MCP servers so the traffic/tools flow through the gateway. However, the evidence is a single brief concept mention with no detailed configuration walkthrough, no example of registering a specific upstream MCP server, and no independent/hands-on corroboration. Missing for 10: a full docs page on connecting/registering upstream MCP servers, concrete config examples, and third-party verification.
- [claimed-docs] “AI MCP Proxy bridges Kong-managed Services to the Model Context Protocol, converting REST APIs into MCP tools or fronting upstream MCP serve…”
- [claimed-docs] “Turn any API into an MCP server using the AI MCP Server entity. This approach does not require an LLM and provides full control over product…”
- [claimed-docs] “Govern LLM, MCP, and agent-to-agent (A2A) traffic with the same Kong AI Gateway.”
ai-native userConnect an agent via an official MCP server
weight 3 · round to Kong AI GatewayRequesty is a gateway/router (not itself an agent), and it documents an official 'MCP Gateway' that lets AI coding assistants like Claude Code, Cursor, and Roo Code securely connect to MCP servers through Requesty's unified API, directly matching the story. Missing for 10: independent/hands-on corroboration beyond first-party docs and more detail on setup/config specifics.
- [claimed-docs] “The MCP (Model Context Protocol) Gateway enables AI coding assistants like Claude Code, Cursor, and Roo Code to securely connect to MCP serv…”
- [claimed-docs] “Using the Requesty integration, you can: Use 300+ models while coding, giving you flexibility to choose the best model for each task.”
Kong AI Gateway ships an official 'AI MCP Server' entity and 'AI MCP Proxy' that converts REST APIs into MCP tools or fronts upstream MCP servers, letting agents connect via a first-party MCP interface, plus governance of MCP traffic (auth, OTel metrics, rate limiting) alongside it. Missing for 10: independent/hands-on verification of an agent successfully connecting through the MCP server and no example configuration walkthrough shown in the pack.
- [claimed-docs] “AI MCP Proxy bridges Kong-managed Services to the Model Context Protocol, converting REST APIs into MCP tools or fronting upstream MCP serve…”
- [claimed-docs] “Turn any API into an MCP server using the AI MCP Server entity. This approach does not require an LLM and provides full control over product…”
- [claimed-docs] “Govern LLM, MCP, and agent-to-agent (A2A) traffic with the same Kong AI Gateway.”
- [claimed-docs] “AI Gateway can export OpenTelemetry (OTLP) metrics for generative AI, MCP, and A2A traffic through an OpenTelemetry AI Policy.”
ai-native userUse an official CLI
weight 2 · round to Kong AI GatewayRequestynone0/10Requesty is presented as a unified LLM gateway/router with SDK compatibility and integrations (Claude Code, Cursor, etc.), but no evidence mentions an official Requesty CLI tool. Evidence only covers API routing, policies, MCP gateway, and dashboard features—no CLI is documented or probed.
Kong ships an official CLI, kongctl, documented at a dedicated docs URL and explicitly used for AI Gateway tasks (e.g., the 'kongctl AI Gateway conversion extension' for migrating configurations). This confirms a real, first-party CLI exists for AI Gateway workflows. Missing for 10: deeper documentation of kongctl's full command surface for AI-native tasks (agents, MCP, A2A) and independent/hands-on corroboration beyond the docs probe.
- [claimed-docs] “walks you through migrating an existing configuration using the kongctl AI Gateway conversion extension”
- [probe] “official CLI documented at https://developer.konghq.com/kongctl/”
ai-native userDrive the product through a documented public API
weight 3 · round to RequestyRequesty exposes a documented, OpenAI-compatible public API (base_url swap, quickstart docs) plus a rich set of documented API-level features (fallback policies, load balancing, caching, structured outputs, BYOK) that let an AI-native user drive the product programmatically. missing for 10: an actual OpenAPI/swagger spec (probe found all candidate spec paths 404) and independent third-party corroboration of API robustness.
- [claimed-docs] “If you're already using the OpenAI SDK, point it at Requesty and you're done. No SDK changes, no new client to learn.”
- [claimed-docs] “base_url="https://router.requesty.ai/v1", # was: https://api.openai.com/v1”
- [claimed-docs] “Fallback Policies automatically retry your requests with different models if one fails, ensuring your application stays reliable even when i…”
- [claimed-docs] “Load Balancing Policies distribute your requests across multiple models based on weights you define. Perfect for A/B testing, gradual rollou…”
- [claimed-docs] “Requesty makes every supported model speak structured JSON — from simple json_object mode to strict, schema-enforced json_schema mode.”
- [probe] “PROBE llms.txt: HTTP 200 at https://docs.requesty.ai/llms.txt # Requesty > Requesty is a unified LLM gateway and OpenAI-compatible API for …”
- [probe] “PROBE openapi: all candidate paths 404 (https://docs.requesty.ai/openapi.json, https://docs.requesty.ai/swagger.json, https://docs.requesty.…”
Kong documents a Konnect API for managing AI Gateway resources (e.g., list-ai-gateways endpoint) and a dedicated kongctl CLI, showing programmatic control exists and is documented. However, probes for a discoverable OpenAPI/swagger spec at standard locations all returned 404, so the breadth/openness of the API surface is not independently confirmed. Missing for 10: a publicly discoverable OpenAPI/swagger spec, broader endpoint coverage beyond the single example, and independent/hands-on confirmation of API usage.
- [claimed-docs] “list-ai-gateways summary: List AI Gateways description: Returns a list of AI Gateways in the organization.”
- [probe] “official CLI documented at https://developer.konghq.com/kongctl/”
- [probe] “PROBE openapi: all candidate paths 404 (https://developer.konghq.com/openapi.json, https://developer.konghq.com/swagger.json, https://develo…”
ai-native userIssue scoped/least-privilege API credentials for an agent
weight 2 · round to Kong AI GatewayRequestynone0/10Requesty's docs mention BYOK (bringing your own provider keys), analytics, and 'centralized governance,' but there is no evidence of issuing scoped or least-privilege Requesty API keys/credentials specifically for individual agents. No mention of key scoping, permission levels, or per-agent credential issuance appears anywhere in the evidence pack.
- [claimed-docs] “Bring Your Own Keys (BYOK) allows you to use your personal API keys from various providers with Requesty.”
- [claimed-docs] “Access 600+ models through one API with intelligent routing, real-time analytics and centralized governance.”
Kong AI Gateway provides building blocks relevant to credential control for agents — AI Auth Strategy entities can authenticate via enterprise IdPs (Okta/Azure AD/OIDC) 'without managing keys manually,' and AI Vault lets model providers, auth strategies, agents, and MCP servers reference secrets from external backends instead of storing literal keys, plus Azure Managed Identity support for scoped cloud authentication. However, none of this evidence explicitly describes issuing scoped/least-privilege credentials per agent (e.g., fine-grained permission scopes, per-agent token minting) — it's centralized secret referencing and IdP auth rather than a dedicated least-privilege credential-issuance mechanism. missing for 10: explicit documentation of scoped/least-privilege credential issuance per agent, granular permission/scope controls tied to individual agent identities, and independent/hands-on verification of this behavior.
- [claimed-docs] “An AI Vault entity lets you register an external secret backend (AWS Secrets Manager, HashiCorp Vault, environment variables, or others) so …”
- [claimed-docs] “Authenticate enterprise users through an existing identity provider (Okta, Azure AD, Google, or any OIDC-compliant IdP) without managing key…”
- [claimed-docs] “Yes, if AI Gateway is running on Azure, you can configure an AI Model Provider to detect the designated Managed Identity or User-Assigned Id…”
ai-native userBuild against official SDKs
weight 2 · round to RequestyRequesty documents OpenAI-SDK compatibility (point the OpenAI SDK's base_url at Requesty) so developers can build using the OpenAI SDK's standard interfaces, but there is no evidence of a dedicated first-party Requesty SDK package (e.g., a requesty-python or requesty-node client) or SDKs for other languages beyond OpenAI-compatible clients. missing for 10: dedicated official Requesty-branded SDKs, multi-language SDK coverage, and independent confirmation of SDK maintenance/versioning.
- [claimed-docs] “If you're already using the OpenAI SDK, point it at Requesty and you're done. No SDK changes, no new client to learn.”
- [claimed-docs] “base_url="https://router.requesty.ai/v1", # was: https://api.openai.com/v1”
- [probe] “PROBE llms.txt: HTTP 200 at https://docs.requesty.ai/llms.txt # Requesty > Requesty is a unified LLM gateway and OpenAI-compatible API for …”
Kong AI Gatewaynone0/10The evidence pack contains no mention of official SDKs for building against Kong AI Gateway (only a CLI 'kongctl' and REST API references), and probes explicitly show no OpenAPI/SDK artifacts (404s for openapi.json, swagger.json, etc.). While an API gateway could plausibly offer official client SDKs, none are documented here.
- [probe] “PROBE openapi: all candidate paths 404 (https://developer.konghq.com/openapi.json, https://developer.konghq.com/swagger.json, https://develo…”
- [probe] “official CLI documented at https://developer.konghq.com/kongctl/”
- [claimed-docs] “list-ai-gateways summary: List AI Gateways description: Returns a list of AI Gateways in the organization.”
Agentic features
ai-native userGet AI-generated insights and suggestions from my data inside the product
weight 2 · round drawnRequestynone0/10Requesty's docs describe a real-time analytics dashboard for usage/cost/latency tracking, but nowhere is there evidence of AI-generated insights, recommendations, or suggestions derived from that data — the dashboard is purely observational reporting.
- [claimed-docs] “Requesty's analytics dashboard gives you complete visibility into your AI usage across all models and providers. Track costs, requests, toke…”
Kong AI Gatewaynone0/10Kong AI Gateway provides usage analytics, logs, and cost/latency metrics (docs-3, docs-9, docs-10), but these are raw operational metrics/dashboards, not AI-generated insights or suggestions derived from the user's own data. No evidence shows the product itself using AI to analyze a customer's traffic/data and proactively surface recommendations or insights.
- [claimed-docs] “See usage analytics for requests, tokens, errors, and latency”
- [claimed-docs] “You can use these metrics to: Track LLM request latency and upstream provider processing time * Monitor token consumption ... * Calculate AI…”
- [claimed-docs] “AI Gateway logs capture detailed information about the request and response payloads, token usage, model details, latency, and cost metrics.”
Api quality
ai-native userExplore an interactive API reference with runnable examples
weight 2 · round drawnRequestynone0/10The evidence pack shows only markdown-based quickstart docs and code snippets; explicit probes for an OpenAPI/swagger spec all returned 404, and there is no mention of an interactive, runnable API reference or playground.
- [probe] “PROBE openapi: all candidate paths 404 (https://docs.requesty.ai/openapi.json, https://docs.requesty.ai/swagger.json, https://docs.requesty.…”
- [claimed-docs] “If you're already using the OpenAI SDK, point it at Requesty and you're done. No SDK changes, no new client to learn.”
- [claimed-docs] “base_url="https://router.requesty.ai/v1", # was: https://api.openai.com/v1”
Kong AI Gatewaynone0/10Evidence shows Kong publishes API reference content (e.g., list-ai-gateways endpoint docs) but there is no indication of an interactive reference with runnable/try-it-out examples; probes for OpenAPI/swagger specs on the docs site all returned 404, suggesting no such interactive tooling is exposed.
- [claimed-docs] “list-ai-gateways summary: List AI Gateways description: Returns a list of AI Gateways in the organization.”
- [probe] “PROBE openapi: all candidate paths 404 (https://developer.konghq.com/openapi.json, https://developer.konghq.com/swagger.json, https://develo…”
ai-native userDownload a machine-readable API spec (OpenAPI or equivalent)
weight 2 · round drawnRequestynone0/10Requesty is an API/gateway product where a downloadable OpenAPI spec would be a natural and expected artifact, so the axis applies, but a direct probe found all candidate OpenAPI/swagger paths returning 404 and no docs page references a spec file. No first-party mention of an OpenAPI spec exists anywhere in the docs pack.
- [probe] “PROBE openapi: all candidate paths 404 (https://docs.requesty.ai/openapi.json, https://docs.requesty.ai/swagger.json, https://docs.requesty.…”
Kong AI Gatewaynone0/10Evidence includes API reference pages (e.g. Konnect AI Gateway API docs) but no downloadable OpenAPI/Swagger spec file is evidenced; a direct probe for standard OpenAPI/swagger.json locations returned 404 at all candidate paths, indicating no machine-readable spec is exposed there.
- [probe] “PROBE openapi: all candidate paths 404 (https://developer.konghq.com/openapi.json, https://developer.konghq.com/swagger.json, https://develo…”
- [claimed-docs] “list-ai-gateways summary: List AI Gateways description: Returns a list of AI Gateways in the organization.”
ai-native userRely on versioned APIs with a documented deprecation policy
weight 2 · round drawnRequestynone0/10No evidence of API versioning scheme or a documented deprecation policy; OpenAPI spec probes all returned 404, and none of the docs mention version numbers, changelogs, or deprecation timelines.
- [probe] “PROBE openapi: all candidate paths 404 (https://docs.requesty.ai/openapi.json, https://docs.requesty.ai/swagger.json, https://docs.requesty.…”
Kong AI Gatewaynone0/10The evidence shows an API reference exists (e.g., 'v1' Konnect AI Gateway API) but there is no documentation of a versioning scheme or deprecation policy for the AI Gateway APIs, and OpenAPI spec probes returned 404s.
- [claimed-docs] “list-ai-gateways summary: List AI Gateways description: Returns a list of AI Gateways in the organization.”
- [probe] “PROBE openapi: all candidate paths 404 (https://developer.konghq.com/openapi.json, https://developer.konghq.com/swagger.json, https://develo…”
Automation depth — how much of the product can run unattendedAutomation depth
How much of the product can run unattended
ai-native userPerform bulk operations across many items at once
weight 2 · round drawnRequestynone0/10Requesty's docs cover routing, fallback, load balancing, caching, and analytics for individual requests, but there is no evidence of a batch/bulk API or mechanism for processing many items in a single operation.
Kong AI Gatewaynone0/10The evidence covers proxying, observability, security, and MCP/A2A support but never describes any bulk or batch operation capability (e.g., batch creating/updating routes, plugins, or AI entities across many items at once). The kongctl CLI and Konnect API are mentioned but only for single-entity or config migration tasks, not bulk item processing.
- [claimed-docs] “list-ai-gateways summary: List AI Gateways description: Returns a list of AI Gateways in the organization.”
- [claimed-docs] “walks you through migrating an existing configuration using the kongctl AI Gateway conversion extension”
- [probe] “official CLI documented at https://developer.konghq.com/kongctl/”
ai-native userDefine rules that trigger actions automatically on events
weight 3 · round to RequestyRequesty provides several automatic policy mechanisms that act as event-triggered rules: Fallback Policies automatically retry with different models on failure events, Load Balancing Policies route by defined weights, and Guardrails automatically scan/act on content before reaching a provider. These are rule-based automations tied to conditions/events, but they are fixed policy types rather than a general-purpose custom event/action rule engine that an AI-native user could freely define. Missing for 10: a general rules/automation builder allowing arbitrary custom triggers and actions beyond the predefined fallback/load-balancing/guardrail policy types, and independent evidence of this automation working in production.
- [claimed-docs] “Fallback Policies automatically retry your requests with different models if one fails, ensuring your application stays reliable even when i…”
- [claimed-docs] “Your request goes to the primary model first. If it fails (timeout, rate limit, error, etc.), the router immediately tries the next model in…”
- [claimed-docs] “Load Balancing Policies distribute your requests across multiple models based on weights you define. Perfect for A/B testing, gradual rollou…”
- [claimed-docs] “Guardrails scan AI request content for sensitive information before it reaches a model provider.”
Kong AI Gateway supports policy-based automatic actions triggered by request/response events—e.g., Prompt Guards and content-safety policies acting on chat/completion requests, AI Rate Limiting Advanced enforcing limits automatically, AWS Guardrails validating requests/responses before forwarding, and semantic caching/routing acting on similarity triggers—which functions as rule-based automation on traffic events. However, this is scoped to gateway traffic policies rather than a general-purpose event/trigger automation framework for arbitrary conditions and actions. Missing for 10: evidence of a generic conditional rule engine (if-this-then-that style), user-defined custom triggers beyond built-in policies, and any workflow/orchestration automation beyond request-level policy enforcement.
- [claimed-docs] “AI Gateway supports content safety features across providers and also includes our Prompt Guards that act on any llm/v1/chat or llm/v1/compl…”
- [claimed-docs] “AI Rate Limiting Advanced - Plugin | Kong Docs”
- [claimed-docs] “Apply safety and DLP policies to prevent toxic content and remove personally identifiable information.”
- [claimed-docs] “AI Gateway can perform intelligent request routing, caching, and content filtering using semantic similarity queries.”
- [claimed-docs] “Use AWS Guardrails to validate requests and/or responses before forwarding them between clients and upstream LLMs.”
Caching performance — stories about caching performance in this arenaCaching performance
Stories about caching performance in this arena
Caching
developerCache responses at the gateway to cut cost and latency on repeated requests
weight 2 · round drawnRequesty documents automatic gateway-level caching of long system prompts and repeated content to reduce costs, with usage analytics tracking cache savings and latency in real-time, directly matching the story's intent. missing for 10: independent/hands-on benchmarks confirming actual latency/cost reduction, and detail on cache TTL/configurability.
- [claimed-docs] “Requesty's auto caching automatically caches long system prompts and repeated content to reduce costs on any provider that supports prompt c…”
- [claimed-docs] “Requesty's analytics dashboard gives you complete visibility into your AI usage across all models and providers. Track costs, requests, toke…”
Kong ships a dedicated AI Semantic Cache plugin explicitly designed to cache LLM responses at the gateway, and separate docs confirm gateway-level semantic caching to reduce redundant calls and improve latency/cost. This is first-party documentation with a named product feature, though no independent benchmark or hands-on cost/latency measurement is provided. Missing for 10: independent/hands-on validation of actual cost/latency savings, and exact-match (non-semantic) cache configuration details.
- [claimed-docs] “AI Semantic Cache - Plugin | Kong Docs”
- [claimed-docs] “AI Gateway can perform intelligent request routing, caching, and content filtering using semantic similarity queries.”
Latency
platform engineerRun traffic through gateway infrastructure that adds minimal latency overhead to provider calls
weight 2 · round drawnRequesty is a gateway/router with caching, fallback, and analytics features that imply performance-conscious design, and analytics dashboard tracks latency, but there is no explicit benchmark or claim quantifying gateway-added latency overhead. missing for 10: published latency benchmarks or overhead numbers, independent performance testing, and any explicit low-latency architecture documentation.
- [claimed-docs] “Requesty's auto caching automatically caches long system prompts and repeated content to reduce costs on any provider that supports prompt c…”
- [claimed-docs] “Requesty's analytics dashboard gives you complete visibility into your AI usage across all models and providers. Track costs, requests, toke…”
- [claimed-docs] “Access 600+ models through one API with intelligent routing, real-time analytics and centralized governance.”
Kong documents latency-related tooling (usage analytics and OTel metrics that track LLM request latency and upstream processing time, semantic caching, load balancing across models) which implies attention to performance, but there is no explicit claim or benchmark quantifying the gateway's own added latency overhead as 'minimal'. missing for 10: quantified latency-overhead benchmarks, independent performance tests, explicit vendor claim of low proxy overhead.
- [claimed-docs] “You can use these metrics to: Track LLM request latency and upstream provider processing time * Monitor token consumption ... * Calculate AI…”
- [claimed-docs] “AI Semantic Cache - Plugin | Kong Docs”
- [claimed-docs] “AI Gateway provides load balancing capabilities to distribute requests across multiple LLM models.”
- [claimed-docs] “See usage analytics for requests, tokens, errors, and latency”
Cost controls — stories about cost controls in this arenaCost controls
Stories about cost controls in this arena
Agent guardrails
ai-native userGive an autonomous agent its own key with budget and rate guardrails so it cannot run away on spend
weight 2 · round to Kong AI GatewayRequestynone0/10Requesty is an LLM gateway product where issuing scoped API keys with spend/rate limits would be a natural and expected capability, but the evidence pack only shows BYOK (using your own provider keys), content-safety 'guardrails' (sensitive-data scanning, not spend limits), and usage analytics dashboards for visibility — none of which describe issuing an agent its own key with a budget cap or rate limit to prevent runaway spend.
- [claimed-docs] “Bring Your Own Keys (BYOK) allows you to use your personal API keys from various providers with Requesty.”
- [claimed-docs] “Guardrails scan AI request content for sensitive information before it reaches a model provider.”
- [claimed-docs] “Requesty's analytics dashboard gives you complete visibility into your AI usage across all models and providers. Track costs, requests, toke…”
Kong AI Gateway supports per-consumer/agent authentication (AI Auth Strategy, AI Vault for keys) plus AI Rate Limiting Advanced and model cost calculation/tracking that matches provider billing, which together let an operator scope an agent's key and cap its request rate and see accrued spend. However, evidence shows cost *calculation and observability* (docs-9, docs-10, docs-18) rather than an explicit hard budget cap/kill-switch that automatically cuts off an agent once a spend threshold is reached, so the 'cannot run away on spend' guarantee is only partially evidenced. Missing for 10: explicit documented budget-threshold enforcement/auto-cutoff tied to a specific agent key, and a worked example of issuing a scoped key to an autonomous agent with combined rate+spend limits.
- [claimed-docs] “AI Rate Limiting Advanced - Plugin | Kong Docs”
- [claimed-docs] “AI Gateway calculates that figure for every request to a large language model (LLM) provider, matching what the provider actually bills.”
- [claimed-docs] “An AI Vault entity lets you register an external secret backend (AWS Secrets Manager, HashiCorp Vault, environment variables, or others) so …”
- [claimed-docs] “Authenticate enterprise users through an existing identity provider (Okta, Azure AD, Google, or any OIDC-compliant IdP) without managing key…”
- [claimed-docs] “You can use these metrics to: Track LLM request latency and upstream provider processing time * Monitor token consumption ... * Calculate AI…”
Budgets
platform engineerSet hard budgets and spend limits per key, team, or user
weight 3 · round to Kong AI GatewayRequestynone0/10The evidence pack shows analytics/usage tracking and BYOK/governance mentions, but no documentation of setting hard budgets or spend limits per API key, team, or user. This is a plausible and expected axis for an LLM gateway product like Requesty, so absence of evidence yields 'none'.
- [claimed-docs] “Requesty's analytics dashboard gives you complete visibility into your AI usage across all models and providers. Track costs, requests, toke…”
- [claimed-docs] “Access 600+ models through one API with intelligent routing, real-time analytics and centralized governance.”
Kong AI Gateway provides cost calculation, tracking across teams, and rate limiting plugins (AI Rate Limiting Advanced) which can enforce request/token limits per consumer, but evidence shows rate limiting and cost tracking as separate capabilities rather than a unified hard 'budget/spend limit' enforcement tied directly to cost thresholds per key/team/user. missing for 10: explicit documentation of setting a dollar-based spend cap or budget that auto-blocks requests once exceeded, per-key/team/user budget configuration UI or API, and independent confirmation of budget enforcement in production.
- [claimed-docs] “AI Rate Limiting Advanced - Plugin | Kong Docs”
- [claimed-docs] “AI Gateway calculates that figure for every request to a large language model (LLM) provider, matching what the provider actually bills.”
- [claimed-docs] “AI Gateway can proxy requests from AI command-line tools to LLM providers. This gives you centralized control over AI traffic: log all reque…”
- [claimed-docs] “You can use these metrics to: Track LLM request latency and upstream provider processing time * Monitor token consumption ... * Calculate AI…”
Spend tracking
platform engineerTrack spend per model, key, team, or user across all providers in one place
weight 3 · round to Kong AI GatewayRequesty's analytics dashboard tracks costs, requests, tokens, cache savings, and latency across all models/providers in real-time, and trace_id/user_id tagging plus BYOK support per-key tracking, which covers most of the story. missing for 10: explicit documentation of per-team breakdowns/team-level grouping, per-user cost attribution dashboards, and independent/hands-on corroboration of the analytics dashboard's granularity beyond vendor docs.
- [claimed-docs] “Requesty's analytics dashboard gives you complete visibility into your AI usage across all models and providers. Track costs, requests, toke…”
- [claimed-docs] “Requests with the same trace_id or user_id always go to the same model.”
- [claimed-docs] “Bring Your Own Keys (BYOK) allows you to use your personal API keys from various providers with Requesty.”
- [claimed-docs] “Access 600+ models through one API with intelligent routing, real-time analytics and centralized governance.”
Kong AI Gateway computes exact per-request LLM costs matching provider billing, logs cost metrics with token/model details, exports cost-related OTel metrics, and documentation explicitly states cost tracking 'across teams'; since all providers route through the same gateway, aggregation across providers is plausible. However, evidence never shows a consolidated dashboard or breakdown specifically by model, API key, or individual user — only 'teams' is explicitly named as a tracked dimension. Missing for 10: explicit per-user and per-API-key cost attribution/dashboard, and a documented single-pane cross-provider spend view (vs. raw logs/metrics that would need external aggregation).
- [claimed-docs] “AI Gateway calculates that figure for every request to a large language model (LLM) provider, matching what the provider actually bills.”
- [claimed-docs] “AI Gateway logs capture detailed information about the request and response payloads, token usage, model details, latency, and cost metrics.”
- [claimed-docs] “You can use these metrics to: Track LLM request latency and upstream provider processing time * Monitor token consumption ... * Calculate AI…”
- [claimed-docs] “AI Gateway can proxy requests from AI command-line tools to LLM providers. This gives you centralized control over AI traffic: log all reque…”
- [claimed-docs] “AI Gateway can export OpenTelemetry (OTLP) metrics for generative AI, MCP, and A2A traffic through an OpenTelemetry AI Policy.”
Key management — stories about key management in this arenaKey management
Stories about key management in this arena
Byok
developerBring my own provider API keys and have the gateway use them for my traffic
weight 2 · round to RequestyRequesty explicitly documents a BYOK feature allowing developers to use personal API keys from various providers with the gateway, alongside the standard OpenAI-compatible routing setup. Missing for 10: independent/hands-on confirmation of BYOK working in practice and details on key storage/security guarantees.
- [claimed-docs] “Bring Your Own Keys (BYOK) allows you to use your personal API keys from various providers with Requesty.”
- [claimed-docs] “If you're already using the OpenAI SDK, point it at Requesty and you're done. No SDK changes, no new client to learn.”
- [claimed-docs] “base_url="https://router.requesty.ai/v1", # was: https://api.openai.com/v1”
Docs show AI Model Providers are configured with credentials, and an AI Vault entity lets AI Model Providers reference externally-stored secrets (AWS Secrets Manager, HashiCorp Vault, env vars) rather than storing keys as literal values, and Azure-specific managed identity auth is also supported — implying developers can supply and manage their own provider keys/secrets. However, no explicit doc snippet shows the exact 'paste your own API key per provider' config example or per-request key override behavior. Missing for 10: an explicit example of configuring a provider with a literal/bring-your-own API key, and confirmation of per-consumer or per-request key overrides.
- [claimed-docs] “The core of AI Gateway is the ability to serve AI Models from various AI Model Providers via a provider-agnostic API.”
- [claimed-docs] “Yes, if AI Gateway is running on Azure, you can configure an AI Model Provider to detect the designated Managed Identity or User-Assigned Id…”
- [claimed-docs] “An AI Vault entity lets you register an external secret backend (AWS Secrets Manager, HashiCorp Vault, environment variables, or others) so …”
- [claimed-docs] “Authenticate enterprise users through an existing identity provider (Okta, Azure AD, Google, or any OIDC-compliant IdP) without managing key…”
Programmatic admin
ai-native userProvision gateways, keys, and budgets programmatically through an admin API
weight 3 · round to Kong AI GatewayRequestynone0/10No evidence of an admin API for programmatically provisioning gateways, keys, or budgets; docs cover BYOK (manual key entry), analytics dashboard, and routing policies, but nothing about API-driven account/key/budget provisioning. The OpenAPI/swagger probe returned 404 for all candidate paths, further indicating no discoverable admin API surface.
- [claimed-docs] “Bring Your Own Keys (BYOK) allows you to use your personal API keys from various providers with Requesty.”
- [claimed-docs] “Requesty's analytics dashboard gives you complete visibility into your AI usage across all models and providers. Track costs, requests, toke…”
- [probe] “PROBE openapi: all candidate paths 404 (https://docs.requesty.ai/openapi.json, https://docs.requesty.ai/swagger.json, https://docs.requesty.…”
Kong exposes a Konnect API for listing/managing AI Gateways (list-ai-gateways) and entity-based configuration (AI Auth Strategy, AI Vault) that implies programmatic provisioning of gateways and credentials, plus a kongctl CLI for gateway configuration/migration. However, there is no explicit documentation of an API or CLI for creating/managing spend budgets or quotas, and no end-to-end example of provisioning gateway+keys+budget via API. Missing for 10: explicit budget/quota provisioning API, a full API reference for creating AI Auth Strategies/keys programmatically, and independent confirmation of the admin API's completeness for this workflow.
- [claimed-docs] “list-ai-gateways summary: List AI Gateways description: Returns a list of AI Gateways in the organization.”
- [claimed-docs] “An AI Vault entity lets you register an external secret backend (AWS Secrets Manager, HashiCorp Vault, environment variables, or others) so …”
- [claimed-docs] “Authenticate enterprise users through an existing identity provider (Okta, Azure AD, Google, or any OIDC-compliant IdP) without managing key…”
- [claimed-docs] “walks you through migrating an existing configuration using the kongctl AI Gateway conversion extension”
- [claimed-docs] “AI Gateway calculates that figure for every request to a large language model (LLM) provider, matching what the provider actually bills.”
Virtual keys
platform engineerMint gateway-managed keys for teams and apps without exposing raw provider keys
weight 3 · round to Kong AI GatewayRequestynone0/10Evidence covers BYOK (users supplying their own provider keys into Requesty) and general governance/analytics claims, but nothing describes an admin/platform-engineer flow for minting distinct gateway-managed keys per team or app that shield the underlying provider keys. Missing for 10: documentation of virtual/scoped key issuance, team/app-level key scoping, or key rotation/management APIs.
- [claimed-docs] “Bring Your Own Keys (BYOK) allows you to use your personal API keys from various providers with Requesty.”
- [claimed-docs] “Access 600+ models through one API with intelligent routing, real-time analytics and centralized governance.”
Kong AI Gateway's AI Vault lets provider keys be stored in external secret backends and referenced by AI Model Providers/Agents instead of literal values, and AI Auth Strategy lets client apps authenticate via an IdP or Kong-managed auth without handling provider keys directly — both point toward the platform-engineer being able to shield raw provider keys from consumers. However, the evidence never explicitly describes a workflow for minting distinct gateway-managed keys per team/app, rotating them, or a consumer/key-auth flow tied to AI traffic. Missing for 10: explicit documentation of issuing/rotating per-team or per-app gateway-managed API keys, and any admin UI/API walkthrough for that key-minting process.
- [claimed-docs] “An AI Vault entity lets you register an external secret backend (AWS Secrets Manager, HashiCorp Vault, environment variables, or others) so …”
- [claimed-docs] “Authenticate enterprise users through an existing identity provider (Okta, Azure AD, Google, or any OIDC-compliant IdP) without managing key…”
- [claimed-docs] “Reference an AI Auth Strategy on your AI Agent entities to authenticate A2A clients, and attach the Request Size Limiting Policy to limit re…”
Observability — seeing what the system is doing — logs, metrics, traces, alertsObservability
Seeing what the system is doing — logs, metrics, traces, alerts
Integrations
developerExport gateway logs and traces to my own observability stack
weight 1 · round to Kong AI GatewayRequestynone0/10Requesty documents an internal analytics dashboard for usage visibility, but there is no evidence of any export mechanism (webhooks, OpenTelemetry, log/trace export API, or third-party observability integrations like Datadog/Grafana) that would let a developer send gateway logs/traces to their own observability stack.
- [claimed-docs] “Requesty's analytics dashboard gives you complete visibility into your AI usage across all models and providers. Track costs, requests, toke…”
Kong AI Gateway documents exporting OpenTelemetry (OTLP) metrics for generative AI, MCP, and A2A traffic via an OpenTelemetry AI Policy, plus detailed audit logs capturing request/response payloads, token usage, latency, and cost — enabling integration with a developer's own observability stack. Missing for 10: explicit trace export (spans/tracing) documentation and independent/hands-on confirmation of exporting to third-party backends like Datadog or Grafana.
- [claimed-docs] “AI Gateway can export OpenTelemetry (OTLP) metrics for generative AI, MCP, and A2A traffic through an OpenTelemetry AI Policy.”
- [claimed-docs] “You can use these metrics to: Track LLM request latency and upstream provider processing time * Monitor token consumption ... * Calculate AI…”
- [claimed-docs] “AI Gateway logs capture detailed information about the request and response payloads, token usage, model details, latency, and cost metrics.”
Logs
platform engineerInspect logged requests and responses with latency, token counts, and cost attached
weight 3 · round to Kong AI GatewayRequesty's analytics dashboard docs confirm tracking of costs, requests, tokens, cache savings, and latency in real-time, which covers most of the story, but there is no evidence of inspecting individual logged request/response payload content (bodies) rather than aggregate metrics. missing for 10: evidence of per-request log inspection with full request/response bodies, screenshots or independent corroboration of the dashboard UI, and confirmation of per-call cost/latency drill-down rather than aggregate analytics.
- [claimed-docs] “Requesty's analytics dashboard gives you complete visibility into your AI usage across all models and providers. Track costs, requests, toke…”
Kong AI Gateway docs explicitly describe audit logs capturing request/response payloads, token usage, model details, latency, and cost metrics, plus usage analytics dashboards and OTel metrics for latency, tokens, and cost. missing for 10: independent/hands-on corroboration of the logging UI in practice and detail on log retention/query interface.
- [claimed-docs] “See usage analytics for requests, tokens, errors, and latency”
- [claimed-docs] “You can use these metrics to: Track LLM request latency and upstream provider processing time * Monitor token consumption ... * Calculate AI…”
- [claimed-docs] “AI Gateway logs capture detailed information about the request and response payloads, token usage, model details, latency, and cost metrics.”
- [claimed-docs] “AI Gateway calculates that figure for every request to a large language model (LLM) provider, matching what the provider actually bills.”
- [claimed-docs] “AI Gateway can export OpenTelemetry (OTLP) metrics for generative AI, MCP, and A2A traffic through an OpenTelemetry AI Policy.”
Openness — open source, data portability, and self-hosting storiesOpenness
Open source, data portability, and self-hosting stories
ai-native userDo everything through the API that I can do in the UI
weight 2 · round to Kong AI GatewayRequesty's core inference path (chat completions, model routing) is fully API-driven via an OpenAI-compatible endpoint, and features like fallback/load-balancing policies, BYOK, and guardrails are described as usable at request time. However, there's no evidence of a management/admin API for configuring these policies, keys, or viewing analytics programmatically, and the OpenAPI spec probe returned 404s across all candidate paths, suggesting dashboard-only configuration for policy setup and analytics viewing. missing for 10: a documented admin/management API for configuring fallback policies, load balancing, guardrails, BYOK keys, and pulling analytics data programmatically; a published OpenAPI spec confirming full API parity with the dashboard.
- [claimed-docs] “Fallback Policies automatically retry your requests with different models if one fails, ensuring your application stays reliable even when i…”
- [claimed-docs] “Load Balancing Policies distribute your requests across multiple models based on weights you define. Perfect for A/B testing, gradual rollou…”
- [claimed-docs] “Bring Your Own Keys (BYOK) allows you to use your personal API keys from various providers with Requesty.”
- [claimed-docs] “Guardrails scan AI request content for sensitive information before it reaches a model provider.”
- [claimed-docs] “Requesty's analytics dashboard gives you complete visibility into your AI usage across all models and providers. Track costs, requests, toke…”
- [probe] “PROBE openapi: all candidate paths 404 (https://docs.requesty.ai/openapi.json, https://docs.requesty.ai/swagger.json, https://docs.requesty.…”
Kong exposes AI Gateway configuration via the Konnect API (e.g. list-ai-gateways endpoint) and the kongctl CLI, and docs explicitly note that 'the same capabilities' configurable via plugins can be set on Services/Routes rather than only the UI, implying strong API-driven configuration. However there is no explicit statement of full UI/API parity, and probes for a discoverable OpenAPI spec returned 404s, leaving completeness unverified. Missing for 10: an explicit parity statement or comprehensive OpenAPI spec confirming every UI action (e.g. dashboards, analytics views) is also API-accessible, and independent confirmation of no UI-only features.
- [claimed-docs] “list-ai-gateways summary: List AI Gateways description: Returns a list of AI Gateways in the organization.”
- [claimed-docs] “This page shows you how to configure the same capabilities with AI plugins on Services and Routes.”
- [probe] “official CLI documented at https://developer.konghq.com/kongctl/”
- [probe] “PROBE openapi: all candidate paths 404 (https://developer.konghq.com/openapi.json, https://developer.konghq.com/swagger.json, https://develo…”
ai-native userExport all of my data in open formats and leave
weight 3 · round drawnRequestynone0/10The evidence pack covers routing, caching, guardrails, analytics dashboard, and pricing, but nothing describes exporting usage data, logs, or configuration in open formats or a data-portability/account-deletion path. This is a fair question for a gateway that stores analytics/usage data, so absence of evidence yields 'none' rather than 'na'.
- [claimed-docs] “Requesty's analytics dashboard gives you complete visibility into your AI usage across all models and providers. Track costs, requests, toke…”
Kong AI Gatewaynone0/10Kong AI Gateway/Konnect is a SaaS-hosted control plane with configuration, logs, and analytics data, so data portability/export-and-leave is a fair axis to ask, but the evidence pack contains no mention of a bulk data export feature, open-format export of configs/logs/analytics, or a documented migration-out path in open standards. Evidence only covers observability (metrics, logs) and a CLI conversion extension for migrating configs between versions, not exporting all user data to leave the platform.
- [claimed-docs] “walks you through migrating an existing configuration using the kongctl AI Gateway conversion extension”
- [claimed-docs] “You can use these metrics to: Track LLM request latency and upstream provider processing time * Monitor token consumption ... * Calculate AI…”
- [claimed-docs] “AI Gateway logs capture detailed information about the request and response payloads, token usage, model details, latency, and cost metrics.”
ai-native userRead the product's source under an open license
weight 2 · round drawnRequestynone0/10No evidence of an open-source license or public source repository for Requesty; it appears to be a closed commercial SaaS gateway with only documentation and API access exposed. Missing for 10: any mention of open-source licensing, public code repository, or license file.
Kong AI Gatewaynone0/10Kong AI Gateway is a commercial/enterprise product built on Kong Gateway; the evidence pack contains only product documentation, API references, and probes for docs/CLI—no mention of source code availability or an open-source license for AI Gateway itself. Kong Gateway OSS is a separate product; nothing here confirms AI Gateway's source is published under an open license.
ai-native userSelf-host the core product
weight 3 · round to Kong AI GatewayRequestynone0/10Requesty is presented as a hosted cloud gateway/router service (EU routing, hosted dashboard, per-usage pricing) with no evidence of a self-hosted deployment option, open-source repo, or on-prem package. No documentation mentions self-hosting the core routing product.
Docs reference configuring AI Gateway capabilities 'on-prem' via plugins on Services and Routes, and a quickstart script to spin up a demo instance, implying self-hosted deployment is possible. However, there's no detailed self-hosting guide, licensing/open-source clarification, or infrastructure requirements documented in the evidence pack. Missing for 10: explicit self-hosting/installation documentation, open-source license details, infrastructure/deployment guides beyond a demo quickstart.
- [claimed-docs] “This page shows you how to configure the same capabilities with AI plugins on Services and Routes.”
- [claimed-docs] “You can use the quickstart script to get a demo instance of AI Gateway running almost instantly.”
Privacy posture — data-handling and privacy storiesPrivacy posture
Data-handling and privacy stories
ai-native userChoose where my data is stored (region/residency)
weight 2 · round to RequestyRequesty offers an explicit EU-routing option (Frankfurt, AWS eu-central-1) keeping processing and storage within the EU, giving users some data-residency choice. However, evidence only shows one alternative region (EU vs default), not a broader set of selectable regions/residency options. Missing for 10: documentation of additional region choices (e.g., US-only, APAC), enterprise data-residency controls, and independent verification of enforcement.
- [claimed-docs] “Route your AI traffic through Requesty's EU infrastructure in Frankfurt, Germany (AWS eu-central-1). All processing and storage by Requesty …”
Kong AI Gateway documentation mentions an on-prem/self-hosted configuration option (configure-on-prem.md), which implies some ability to control where the gateway (and thus data in transit) runs, but there is no explicit documentation of region selection, data residency guarantees, or storage location controls for logs/analytics/cost data. missing for 10: explicit region/residency configuration options, documented data storage location controls, compliance statements about data residency.
- [claimed-docs] “This page shows you how to configure the same capabilities with AI plugins on Services and Routes.”
- [claimed-docs] “AI Gateway logs capture detailed information about the request and response payloads, token usage, model details, latency, and cost metrics.”
ai-native userPrevent my data from being used to train AI models
weight 3 · round drawnRequestynone0/10The evidence pack covers routing, caching, guardrails, EU data residency, and BYOK, but contains no statement about a no-training policy or data opt-out for model training, so this claim is unevidenced.
Kong AI Gatewaynone0/10Kong AI Gateway is an enterprise routing/governance layer for LLM traffic, so control over how provider data is used is a fair question, but the evidence pack contains no mention of training-data opt-out, zero-retention guarantees, or contractual terms preventing model providers from using proxied data for training — only general DLP/PII redaction and content-safety features are documented, which don't address this specific claim.
ai-native userControl data retention and deletion
weight 2 · round drawnRequestynone0/10The evidence pack shows EU data-residency routing and guardrails for sensitive content scanning, but nothing about data retention periods, deletion controls, or user-initiated data purge/export mechanisms. Missing for 10: documented retention policy, deletion/erasure API or dashboard control, retention configuration options.
- [claimed-docs] “Route your AI traffic through Requesty's EU infrastructure in Frankfurt, Germany (AWS eu-central-1). All processing and storage by Requesty …”
- [claimed-docs] “Guardrails scan AI request content for sensitive information before it reaches a model provider.”
Kong AI Gatewaynone0/10Evidence shows Kong AI Gateway logs request/response payloads, token usage, and costs (docs-10), and offers DLP/PII removal (docs-20) and semantic caching (docs-11), but none of the evidence describes configurable data retention periods or deletion/purge controls for logs, caches, or audit trails. Missing for 10: explicit retention policy settings, log/cache deletion or expiry controls, data subject deletion workflow.
- [claimed-docs] “AI Gateway logs capture detailed information about the request and response payloads, token usage, model details, latency, and cost metrics.”
- [claimed-docs] “Apply safety and DLP policies to prevent toxic content and remove personally identifiable information.”
- [claimed-docs] “AI Semantic Cache - Plugin | Kong Docs”
ai-native userOpt out of telemetry and usage tracking
weight 2 · round drawnRequestynone0/10The evidence pack shows analytics/usage dashboards and EU data routing but contains no mention of a telemetry/usage-tracking opt-out setting or privacy control for disabling data collection. As a hosted API gateway that logs requests, this is a fair axis to expect documentation on, but none is present.
- [claimed-docs] “Requesty's analytics dashboard gives you complete visibility into your AI usage across all models and providers. Track costs, requests, toke…”
- [claimed-docs] “Route your AI traffic through Requesty's EU infrastructure in Frankfurt, Germany (AWS eu-central-1). All processing and storage by Requesty …”
Routing resilience — stories about routing resilience in this arenaRouting resilience
Stories about routing resilience in this arena
Fallbacks
platform engineerConfigure automatic fallback to another model or provider when one fails
weight 3 · round to RequestyRequesty's Fallback Policies feature explicitly documents automatic retry to a different model/provider when the primary fails (timeout, rate limit, error), directly matching the story. Missing for 10: independent/hands-on corroboration beyond vendor docs and detail on configuration granularity (e.g., how fallback chains are set up in the dashboard/API).
- [claimed-docs] “Fallback Policies automatically retry your requests with different models if one fails, ensuring your application stays reliable even when i…”
- [claimed-docs] “Your request goes to the primary model first. If it fails (timeout, rate limit, error, etc.), the router immediately tries the next model in…”
Kong AI Gateway documents load balancing across multiple LLM models/providers, which implies routing resilience, but the evidence never explicitly describes an automatic fallback mechanism triggered by provider failure (e.g., retry-on-error, circuit breaking, or failover behavior). Missing for 10: explicit fallback-on-failure documentation, failure detection/health-check mechanism, and any hands-on confirmation that fallback actually occurs when a provider errors out.
- [claimed-docs] “AI Gateway provides load balancing capabilities to distribute requests across multiple LLM models.”
- [claimed-docs] “The core of AI Gateway is the ability to serve AI Models from various AI Model Providers via a provider-agnostic API.”
Load balancing
platform engineerLoad-balance traffic across providers, deployments, or keys by weight, latency, or cost
weight 2 · round drawnRequesty documents weight-based Load Balancing Policies that distribute requests across multiple models/providers, plus session affinity via trace_id/user_id, and separate Fallback Policies for failure-driven rerouting. However, there is no evidence of latency-based or cost-based load balancing—only weight is explicitly supported. missing for 10: latency-based routing/balancing, cost-based routing/balancing, explicit per-key (not just per-model/provider) balancing.
- [claimed-docs] “Load Balancing Policies distribute your requests across multiple models based on weights you define. Perfect for A/B testing, gradual rollou…”
- [claimed-docs] “Requests with the same trace_id or user_id always go to the same model.”
- [claimed-docs] “Fallback Policies automatically retry your requests with different models if one fails, ensuring your application stays reliable even when i…”
- [claimed-docs] “Your request goes to the primary model first. If it fails (timeout, rate limit, error, etc.), the router immediately tries the next model in…”
Kong AI Gateway explicitly documents load-balancing across multiple LLM models/providers (docs-17), and it also tracks and calculates per-request LLM costs (docs-18) and latency metrics (docs-9), which are the raw signals needed for cost- or latency-aware routing. However, the evidence pack never confirms the specific load-balancing algorithms (e.g., weighted, latency-based, cost-based) are selectable options, so it's unclear whether these criteria can actually be used as balancing strategies. Missing for 10: explicit documentation of weight-based, latency-based, and cost-based load-balancing algorithms/configuration options, and independent confirmation these modes work as described.
- [claimed-docs] “AI Gateway provides load balancing capabilities to distribute requests across multiple LLM models.”
- [claimed-docs] “AI Gateway calculates that figure for every request to a large language model (LLM) provider, matching what the provider actually bills.”
- [claimed-docs] “You can use these metrics to: Track LLM request latency and upstream provider processing time * Monitor token consumption ... * Calculate AI…”
Policy routing
ai-native userMy agent can switch models mid-task by policy — cost, capability, or availability — through gateway routing rules
weight 3 · round to RequestyRequesty's gateway supports fallback policies (retry with different models on failure/rate limit/error), load-balancing policies (distribute across models by weight, sticky routing by trace/user), and access to 300+/600+ models via a single API, directly enabling policy-based mid-task model switching for cost, capability, or availability reasons. missing for 10: no independent/hands-on corroboration of dynamic policy switching in a live agent task, and no explicit example combining cost+capability+availability rules in one policy config.
- [claimed-docs] “Fallback Policies automatically retry your requests with different models if one fails, ensuring your application stays reliable even when i…”
- [claimed-docs] “Your request goes to the primary model first. If it fails (timeout, rate limit, error, etc.), the router immediately tries the next model in…”
- [claimed-docs] “Load Balancing Policies distribute your requests across multiple models based on weights you define. Perfect for A/B testing, gradual rollou…”
- [claimed-docs] “Requests with the same trace_id or user_id always go to the same model.”
- [claimed-docs] “Using the Requesty integration, you can: Use 300+ models while coding, giving you flexibility to choose the best model for each task.”
- [claimed-docs] “Access 600+ models through one API with intelligent routing, real-time analytics and centralized governance.”
Kong AI Gateway documents load balancing across multiple LLM models (covering availability-based routing) and per-request cost calculation, which together imply some policy-driven model selection, but there is no explicit documentation of switching models based on capability or triggering mid-task, dynamic policy switches tied to cost or availability thresholds. Missing for 10: explicit cost/capability-based routing policy documentation, mid-task dynamic model switching evidence, and independent/hands-on corroboration of this routing-resilience behavior.
- [claimed-docs] “AI Gateway provides load balancing capabilities to distribute requests across multiple LLM models.”
- [claimed-docs] “AI Gateway calculates that figure for every request to a large language model (LLM) provider, matching what the provider actually bills.”
- [claimed-docs] “AI Gateway can perform intelligent request routing, caching, and content filtering using semantic similarity queries.”
Rate limits
platform engineerSmooth provider rate limits by spreading traffic across keys and queuing or throttling requests
weight 2 · round to Kong AI GatewayRequesty's Fallback Policies explicitly retry on rate-limit errors by switching models, and Load Balancing Policies distribute requests across multiple models/providers by weight, which can smooth rate-limit pressure; BYOK lets users supply multiple provider keys. However, there is no documented queuing/throttling mechanism or explicit per-key round-robin/rate-limit-aware routing across multiple keys for the same provider. Missing for 10: explicit request queuing/throttling controls, documented multi-key rotation within a single provider, and rate-limit-specific load-balancing logic (vs. general failure retries).
- [claimed-docs] “Fallback Policies automatically retry your requests with different models if one fails, ensuring your application stays reliable even when i…”
- [claimed-docs] “Your request goes to the primary model first. If it fails (timeout, rate limit, error, etc.), the router immediately tries the next model in…”
- [claimed-docs] “Load Balancing Policies distribute your requests across multiple models based on weights you define. Perfect for A/B testing, gradual rollou…”
- [claimed-docs] “Bring Your Own Keys (BYOK) allows you to use your personal API keys from various providers with Requesty.”
Kong AI Gateway documents load balancing across multiple LLM models/providers and a dedicated 'AI Rate Limiting Advanced' plugin for enforcing rate limits, which together support spreading traffic and throttling. However, there is no explicit documentation of spreading traffic across multiple API keys for the same provider or of request queuing behavior when limits are hit. Missing for 10: explicit key-rotation/multi-key load balancing, request queuing mechanics, and any hands-on/independent verification of these features working together.
- [claimed-docs] “AI Gateway provides load balancing capabilities to distribute requests across multiple LLM models.”
- [claimed-docs] “AI Rate Limiting Advanced - Plugin | Kong Docs”
- [claimed-docs] “AI Gateway calculates that figure for every request to a large language model (LLM) provider, matching what the provider actually bills.”
Retries
platform engineerSet automatic retry policies for transient provider errors
weight 2 · round to RequestyRequesty's Fallback Policies explicitly retry requests on a different model when the primary fails due to timeout, rate limit, or error, which directly addresses transient provider errors. However, this is model-fallback rather than a configurable retry policy (e.g., retry counts, backoff intervals, retry-same-provider-first) explicitly for transient errors. missing for 10: documentation of configurable retry counts/backoff on the same provider before failover, independent/hands-on confirmation of retry behavior, and explicit mention of 'transient error' handling distinct from full model fallback.
- [claimed-docs] “Fallback Policies automatically retry your requests with different models if one fails, ensuring your application stays reliable even when i…”
- [claimed-docs] “Your request goes to the primary model first. If it fails (timeout, rate limit, error, etc.), the router immediately tries the next model in…”
Kong AI Gateway documents load balancing across multiple LLM models/providers (docs-17), which implies some failover capability, but the evidence never explicitly describes configurable automatic retry policies for transient provider errors (e.g., retry counts, backoff, error-code triggers). Missing for 10: explicit retry-policy documentation, retry configuration options, and evidence of behavior on transient errors specifically.
- [claimed-docs] “AI Gateway provides load balancing capabilities to distribute requests across multiple LLM models.”
- [claimed-docs] “The core of AI Gateway is the ability to serve AI Models from various AI Model Providers via a provider-agnostic API.”
Streaming tools — stories about streaming tools in this arenaStreaming tools
Stories about streaming tools in this arena
Streaming
developerStream token-by-token responses through the gateway from any provider
weight 3 · round to Kong AI GatewayRequesty claims full OpenAI-SDK compatibility with no code changes (drop-in base_url swap), which implies standard SDK streaming (stream=True) would work across any of its 300+ models, but no evidence pack item explicitly confirms streaming/token-by-token behavior or documents a streaming example. missing for 10: explicit streaming documentation/example, confirmation across multiple providers, and any independent/hands-on verification of streaming behavior.
- [claimed-docs] “If you're already using the OpenAI SDK, point it at Requesty and you're done. No SDK changes, no new client to learn.”
- [claimed-docs] “base_url="https://router.requesty.ai/v1", # was: https://api.openai.com/v1”
- [claimed-docs] “Access 600+ models through one API with intelligent routing, real-time analytics and centralized governance.”
Kong AI Gateway has a dedicated streaming.md doc confirming token-by-token streaming from models is supported and rendered in real time, layered on top of its provider-agnostic API supporting multiple LLM providers. Missing for 10: independent/hands-on confirmation of streaming behavior across every provider and any details on streaming with proxy-added policies (e.g., guardrails) applied mid-stream.
- [claimed-docs] “most models can stream each word (or sets of words and tokens) back to the client. This allows the chat response to be rendered in real time…”
- [claimed-docs] “The core of AI Gateway is the ability to serve AI Models from various AI Model Providers via a provider-agnostic API.”
Tool calling
developerRequest structured JSON-schema outputs across providers
weight 1 · round to RequestyRequesty explicitly documents structured outputs, stating it makes 'every supported model speak structured JSON — from simple json_object mode to strict, schema-enforced json_schema mode,' directly addressing cross-provider JSON-schema support via its unified OpenAI-compatible API. Missing for 10: independent/hands-on verification across multiple providers and no explicit list of which of the 300+ models fully honor strict schema enforcement.
- [claimed-docs] “Requesty makes every supported model speak structured JSON — from simple json_object mode to strict, schema-enforced json_schema mode.”
- [claimed-docs] “Access 600+ models through one API with intelligent routing, real-time analytics and centralized governance.”
- [claimed-docs] “If you're already using the OpenAI SDK, point it at Requesty and you're done. No SDK changes, no new client to learn.”
Kong AI Gatewaynone0/10The evidence pack covers provider-agnostic proxying, streaming, load balancing, cost tracking, and safety policies, but no document mentions structured/JSON-schema output enforcement or normalization across providers. This is a fair capability to expect from an AI gateway, but no evidence supports it.
developerMake tool and function calls across different providers with a consistent schema
weight 3 · round drawnRequesty's OpenAI-compatible unified API (docs-1, docs-2, docs-16) implies that developers can use the same OpenAI SDK request format—including tool/function-call schema—across 300+/600+ models, and structured-outputs docs (docs-14) show JSON/schema-enforced output support across providers. However, there is no explicit documentation or example showing tool/function-call payloads being translated or normalized for non-OpenAI-native providers, nor any hands-on confirmation that function calling works uniformly across all providers. Missing for 10: explicit function/tool-calling schema documentation, cross-provider tool-call examples, and independent verification of consistent behavior across providers.
- [claimed-docs] “If you're already using the OpenAI SDK, point it at Requesty and you're done. No SDK changes, no new client to learn.”
- [claimed-docs] “base_url="https://router.requesty.ai/v1", # was: https://api.openai.com/v1”
- [claimed-docs] “Requesty makes every supported model speak structured JSON — from simple json_object mode to strict, schema-enforced json_schema mode.”
- [claimed-docs] “Access 600+ models through one API with intelligent routing, real-time analytics and centralized governance.”
Kong AI Gateway documents a provider-agnostic API for LLM traffic and an AI MCP Proxy/Server that converts REST APIs into MCP tools, suggesting a consistent tool-calling interface across providers and models. However, there is no explicit documentation showing normalization of function/tool-call schemas (e.g., translating OpenAI-style function calls to Anthropic tool-use format) for the same request across different providers. Missing for 10: explicit cross-provider function-call schema translation examples, hands-on/independent verification of consistent tool-call behavior across providers.
- [claimed-docs] “The core of AI Gateway is the ability to serve AI Models from various AI Model Providers via a provider-agnostic API.”
- [claimed-docs] “AI MCP Proxy bridges Kong-managed Services to the Model Context Protocol, converting REST APIs into MCP tools or fronting upstream MCP serve…”
- [claimed-docs] “Turn any API into an MCP server using the AI MCP Server entity. This approach does not require an LLM and provides full control over product…”
- [claimed-docs] “Govern LLM, MCP, and agent-to-agent (A2A) traffic with the same Kong AI Gateway.”
Unified api — stories about unified api in this arenaUnified api
Stories about unified api in this arena
Catalog
developerBrowse or query a catalog of available models with pricing and context-window metadata
weight 2 · round drawnRequestynone0/10Evidence confirms Requesty routes across 300–600+ models via a unified API, but nothing in the pack documents a browsable/queryable model catalog exposing pricing or context-window metadata (no /models endpoint, no models page, and the OpenAPI probe returned 404s). missing for 10: a models catalog page or API endpoint, per-model pricing data, per-model context-window metadata.
- [claimed-docs] “Access 600+ models through one API with intelligent routing, real-time analytics and centralized governance.”
- [claimed-docs] “Using the Requesty integration, you can: Use 300+ models while coding, giving you flexibility to choose the best model for each task.”
- [probe] “PROBE openapi: all candidate paths 404 (https://docs.requesty.ai/openapi.json, https://docs.requesty.ai/swagger.json, https://docs.requesty.…”
Kong AI Gatewaynone0/10Evidence shows Kong AI Gateway supports multi-provider routing and calculates per-request costs, but there is no documented catalog/endpoint for browsing or querying available models with pricing and context-window metadata — the closest evidence (model-cost-management, list-ai-gateways API) covers cost calculation per request and listing gateway instances, not a model metadata catalog.
- [claimed-docs] “AI Gateway calculates that figure for every request to a large language model (LLM) provider, matching what the provider actually bills.”
- [claimed-docs] “list-ai-gateways summary: List AI Gateways description: Returns a list of AI Gateways in the organization.”
- [claimed-docs] “The core of AI Gateway is the ability to serve AI Models from various AI Model Providers via a provider-agnostic API.”
Compatibility
developerPoint existing OpenAI-compatible code at the gateway by changing only the base URL and key
weight 3 · round to RequestyDocs explicitly show swapping only base_url (to router.requesty.ai/v1) and API key while keeping OpenAI SDK unchanged, directly matching the story. Missing for 10: independent/hands-on third-party confirmation beyond vendor docs and a formal OpenAPI spec (probe found 404s).
- [claimed-docs] “If you're already using the OpenAI SDK, point it at Requesty and you're done. No SDK changes, no new client to learn.”
- [claimed-docs] “base_url="https://router.requesty.ai/v1", # was: https://api.openai.com/v1”
- [probe] “PROBE llms.txt: HTTP 200 at https://docs.requesty.ai/llms.txt # Requesty > Requesty is a unified LLM gateway and OpenAI-compatible API for …”
Kong AI Gateway is documented as a provider-agnostic API layer that proxies LLM requests (chat/completions endpoints) with streaming support, which implies OpenAI-style compatibility, but no evidence explicitly confirms that existing OpenAI SDK code works unmodified by only swapping base URL and API key. missing for 10: explicit doc/example showing an OpenAI SDK client redirected via base_url with no code changes, and confirmation that request/response schemas exactly mirror OpenAI's format.
- [claimed-docs] “The core of AI Gateway is the ability to serve AI Models from various AI Model Providers via a provider-agnostic API.”
- [claimed-docs] “most models can stream each word (or sets of words and tokens) back to the client. This allows the chat response to be rendered in real time…”
- [claimed-docs] “This page shows you how to configure the same capabilities with AI plugins on Services and Routes.”
One endpoint
developerCall many model providers through one consistent API
weight 3 · round drawnRequesty is explicitly documented as an OpenAI-compatible unified API/gateway supporting 300+ (or 600+) models via a single base_url swap, with routing, fallback, load balancing, and BYOK across providers, all first-party documented and corroborated by an llms.txt probe describing it as a unified LLM gateway. Missing for 10: independent/hands-on third-party verification beyond docs, and no public OpenAPI spec found (404s on probe).
- [claimed-docs] “If you're already using the OpenAI SDK, point it at Requesty and you're done. No SDK changes, no new client to learn.”
- [claimed-docs] “base_url="https://router.requesty.ai/v1", # was: https://api.openai.com/v1”
- [claimed-docs] “Bring Your Own Keys (BYOK) allows you to use your personal API keys from various providers with Requesty.”
- [claimed-docs] “Access 600+ models through one API with intelligent routing, real-time analytics and centralized governance.”
- [probe] “PROBE llms.txt: HTTP 200 at https://docs.requesty.ai/llms.txt # Requesty > Requesty is a unified LLM gateway and OpenAI-compatible API for …”
Kong AI Gateway explicitly provides a provider-agnostic API to route to many LLM providers, with load balancing and unified logging/observability across providers, matching the core unified-API story. Missing for 10: independent/hands-on developer corroboration of consistent request/response schema across providers, and no explicit list of exact supported providers in evidence.
- [claimed-docs] “The core of AI Gateway is the ability to serve AI Models from various AI Model Providers via a provider-agnostic API.”
- [claimed-docs] “AI Gateway provides load balancing capabilities to distribute requests across multiple LLM models.”
- [claimed-docs] “You can use these metrics to: Track LLM request latency and upstream provider processing time * Monitor token consumption ... * Calculate AI…”
- [claimed-docs] “AI Gateway calculates that figure for every request to a large language model (LLM) provider, matching what the provider actually bills.”
Not comparable on these axes
ai-native userSubscribe to events via webhooks
weight 2 · not comparableRequestynone0/10No evidence anywhere in the pack of webhook subscription support for events (e.g., request completion, budget alerts, usage thresholds); the docs cover routing, fallback, caching, analytics, MCP gateway, etc., but no webhook/event-subscription feature is mentioned.
Kong AI Gatewayn/aKong AI Gateway is an API/AI traffic gateway product, not an event-driven system for end users to subscribe to via webhooks; its role is proxying, governing, and observing LLM/MCP/A2A traffic, not offering webhook-based event subscription as a user-facing feature. No evidence in the pack even mentions webhooks or event subscription, and this axis is a category mismatch for a gateway of this kind.
ai-native userSet up automations that run autonomously in the background
weight 2 · not comparableRequestyn/aRequesty is an LLM gateway/router product for API traffic management (routing, fallback, load balancing, caching), not an automation or agent-orchestration platform; there is no concept of user-defined background-running autonomous automations in this product category.
Kong AI Gatewayn/aKong AI Gateway is a proxy/control-plane for governing LLM, MCP, and A2A traffic (rate limiting, observability, auth, routing) — it does not itself host or execute autonomous background automations/agents; that is a different product category (agent orchestration/runtime platforms).
ai-native userDelegate tasks to a built-in AI assistant inside the product
weight 3 · not comparableRequestyn/aRequesty is an LLM routing/gateway product (unified API, fallback, load balancing, caching, analytics) rather than an end-user product with a built-in AI assistant persona to delegate tasks to; this axis is a category error for its type.
ai-native userOperate the product with natural-language commands
weight 2 · not comparableRequestyn/aRequesty is an LLM routing/gateway API product configured via SDKs, config files, and a dashboard, not an interactive assistant meant to be operated via natural-language commands; this axis is a category mismatch for an infrastructure/API product.
Kong AI Gatewaynone0/10Kong AI Gateway's documentation covers proxying LLM/CLI/A2A/MCP traffic, observability, and cost tracking, but there is no evidence that the gateway itself can be configured or operated via natural-language commands (its control plane relies on kongctl CLI and declarative config, not NL commands).
- [probe] “official CLI documented at https://developer.konghq.com/kongctl/”
- [claimed-docs] “walks you through migrating an existing configuration using the kongctl AI Gateway conversion extension”
ai-native userTest against a sandbox environment without touching production data
weight 1 · not comparableRequestyn/aRequesty is an LLM routing/gateway product for production API traffic, not a sandboxed testing/data environment; the sandbox-vs-production-data story is a category error for this kind of product.
Kong AI Gatewaynone0/10The evidence pack only shows a quickstart script for spinning up a demo instance and general AI Gateway features (proxying, observability, policies) but no dedicated sandbox/staging environment or test-mode capability that isolates from production data. No mention of environment separation, mock upstreams, or a documented sandbox mode.
ai-native userSchedule recurring jobs or workflows
weight 2 · not comparableRequestyn/aRequesty is an LLM routing/gateway API, not a workflow orchestration or job-scheduling product; scheduling recurring jobs is outside its category's scope.
Kong AI Gatewayn/aKong AI Gateway is an API/traffic gateway product for proxying, governing, and observing AI/LLM/A2A/MCP traffic; it is not a workflow/job scheduling or automation-orchestration product. Scheduling recurring jobs or workflows is outside its category — no evidence pack item relates to cron-like scheduling or workflow orchestration, confirming this is a wrong-axis question rather than a missing feature.
ai-native userVersion, review, and roll back my automations
weight 1 · not comparableRequestyn/aRequesty is an LLM gateway/router product, not an automation-building tool with workflows to version, review, or roll back; this story is a category error for its product type.
Kong AI Gatewaynone0/10The evidence pack covers AI Gateway's traffic proxying, observability, security, and provider-agnostic API features, but contains no mention of versioning, reviewing, or rolling back gateway configurations or 'automations' (e.g., no changelog, approval workflow, or rollback mechanism is documented). The only tangential item is a config migration extension (kongctl conversion tool), which addresses format conversion, not version control or rollback. missing for 10: version history/changelog for AI Gateway configs, review/approval workflow for changes, rollback mechanism for policies or entities.
- [claimed-docs] “walks you through migrating an existing configuration using the kongctl AI Gateway conversion extension”