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How Qdrant’s scores are calculated

The full audit trail, recomputed from the verdict data at build time through the same code that produced the leaderboard: verdict × quality × story weight per cell, cells sum to dimension scores, dimensions blend into the PA Score. Every number on the product page is reproducible from this page alone; for why the formula looks like this, see the methodology.

verdict factors: full ×1.0 · partial ×0.6 · disputed ×0.3 · none ×0.0 · n/a excluded from both sides · cell points = weight × quality × factor · cell max = weight × 10

PA Score32/100

Agent-ready 57.3 × 0.30 = 17.19

API quality 5.1 × 0.20 = 1.02

Openness 59.2 × 0.20 = 11.84

Built-in AI 8.6 × 0.15 = 1.29

Automation 6.0 × 0.15 = 0.90

(17.19 + 1.02 + 11.84 + 1.29 + 0.90) ÷ (0.30 + 0.20 + 0.20 + 0.15 + 0.15) = 32.24 ÷ 1.00 = 32.2

Scores are stored to 1 decimal; the product page’s pills round to whole numbers for display. Each dimension below shows the stories, verdicts, and cited evidence behind its number.

Agent-ready57.3/100×0.30 of the PA blend

Outside-in: can YOUR agent reach and drive this product — API, MCP, CLI, headless runs, agent docs.

Point an agent at llms.txt or agent-oriented docsweight 2

2 (weight) × 8 (quality) × 1.0 (full) = 16.0 of 20 max

  • [probe] https://qdrant.tech/llms.txt“PROBE llms.txt: HTTP 200 at https://qdrant.tech/llms.txt # https://qdrant.tech/ ## Overall Summary > Qdrant is an Open-Source Vector Search Engine written in Rust. It provides f”
  • [github] https://github.com/qdrant/qdrant“Install these skills to empower your agent in making critical engineering decisions for optimal vector search performance, such as quantization, sharding, tenant isolation, hybrid search, model migration, and more.”
  • [github] https://github.com/qdrant/qdrant“Qdrant provides a collection of ready-to-use agent skills that bring Qdrant's vector search capabilities directly into your AI coding assistant.”

Run the product headlessly / in CI for automationweight 2

2 (weight) × 6 (quality) × 0.6 (partial) = 7.2 of 20 max

  • [claimed-docs] https://qdrant.tech/documentation/quickstart/“docker run -p 6333:6333 -p 6334:6334 \ -v "$(pwd)/qdrant_storage:/qdrant/storage:z" \ qdrant/qdrant”
  • [claimed-docs] https://qdrant.tech/documentation/quickstart/“client.create_collection( collection_name="test_collection", vectors_config=VectorParams(size=4, distance=Distance.DOT), )”
  • [claimed-docs] https://qdrant.tech/documentation/quickstart/“docker run -p 6333:6333 -p 6334:6334 -v "$(pwd)/qdrant_storage:/qdrant/storage:z" qdrant/qdrant”
  • [github] https://github.com/qdrant/qdrant“Qdrant offers the following client libraries to help you integrate it into your application stack”
  • [community] https://news.ycombinator.com/item?id=39101682“We've been using Qdrant for over a year with 10s of millions of items, lots of daily inserts/deletions. A couple of gotchas but generally predictable and scalable; team focuses on the right features.”
  • [community] https://news.ycombinator.com/item?id=38611033“We've been using qdrant in production for over a year. It's excellent and the team are very responsive to the few issues we've had. Qdrant does one job and scales well.”

Plug MCP servers into this product so it can use their toolsweight 3

n/a — not applicable to this product: excluded from numerator and denominator

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Connect an agent via an official MCP serverweight 3

3 (weight) × 8 (quality) × 1.0 (full) = 24.0 of 30 max

  • [probe] https://qdrant.tech/documentation/qdrant-mcp-server/“official MCP server documented at https://qdrant.tech/documentation/qdrant-mcp-server/”
  • [github] https://github.com/qdrant/qdrant“Install these skills to empower your agent in making critical engineering decisions for optimal vector search performance, such as quantization, sharding, tenant isolation, hybrid search, model migration, and more.”
  • [github] https://github.com/qdrant/qdrant“Qdrant provides a collection of ready-to-use agent skills that bring Qdrant's vector search capabilities directly into your AI coding assistant.”

Use an official CLIweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Drive the product through a documented public APIweight 3

3 (weight) × 8 (quality) × 1.0 (full) = 24.0 of 30 max

  • [github] https://github.com/qdrant/qdrant“It provides a production-ready service with a convenient API to store, search, and manage points—vectors with an additional payload.”
  • [github] https://github.com/qdrant/qdrant“Qdrant offers the following client libraries to help you integrate it into your application stack”
  • [github] https://github.com/qdrant/qdrant“Go client ... Rust client ... JavaScript/TypeScript client ... Python client ... .NET/C# client ... Java client”
  • [claimed-docs] https://qdrant.tech/documentation/quickstart/“client.create_collection( collection_name="test_collection", vectors_config=VectorParams(size=4, distance=Distance.DOT), )”
  • [claimed-docs] https://qdrant.tech/documentation/quickstart/“docker run -p 6333:6333 -p 6334:6334 \ -v "$(pwd)/qdrant_storage:/qdrant/storage:z" \ qdrant/qdrant”
  • [probe] https://qdrant.tech/openapi.json“PROBE openapi: all candidate paths 404 (https://qdrant.tech/openapi.json, https://qdrant.tech/swagger.json, https://qdrant.tech/api/openapi.json, https://qdrant.tech/.well-known/openapi.json)”
  • [community] https://news.ycombinator.com/item?id=35844724“After testing numerous open source vector databases, Qdrant is the best option: docs are clear, easy to build from source in Rust (~30 min), small portable binaries, clean and uncluttered compared to alternatives, devs are technically capable.”

Issue scoped/least-privilege API credentials for an agentweight 2

2 (weight) × 8 (quality) × 1.0 (full) = 16.0 of 20 max

  • [claimed-docs] https://qdrant.tech/documentation/security/“Qdrant supports three types of API key: **Admin API Key**... **Read-Only API Key**... **Granular Access API Keys**”
  • [claimed-docs] https://qdrant.tech/documentation/security/“Qdrant supports API key authentication (including read-only API keys for query-only consumers and granular access API keys with per-collection read/write scoping), network binding, TLS for encrypted connections, and audit logging for compliance.”
  • [claimed-docs] https://qdrant.tech/documentation/security/“Qdrant supports API key authentication ... network binding, TLS for encrypted connections, and audit logging for compliance.”

Build against official SDKsweight 2

2 (weight) × 8 (quality) × 1.0 (full) = 16.0 of 20 max

  • [github] https://github.com/qdrant/qdrant“Qdrant offers the following client libraries to help you integrate it into your application stack”
  • [github] https://github.com/qdrant/qdrant“Go client ... Rust client ... JavaScript/TypeScript client ... Python client ... .NET/C# client ... Java client”
  • [claimed-docs] https://qdrant.tech/documentation/quickstart/“client.create_collection( collection_name="test_collection", vectors_config=VectorParams(size=4, distance=Distance.DOT), )”
  • [community] https://news.ycombinator.com/item?id=38611033“Just played with qdrant using its Python client. Pretty smooth onboarding experience, though having to generate embeddings client-side rather than in the DB felt somewhat besides the point.”
  • [community] https://news.ycombinator.com/item?id=35844724“After testing numerous open source vector databases, Qdrant is the best option: docs are clear, easy to build from source in Rust (~30 min), small portable binaries, clean and uncluttered compared to alternatives, devs are technically capable.”

Subscribe to events via webhooksweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Agent-ready = 103.2 ÷ 180 × 100 = 57.3

API quality5.1/100×0.20 of the PA blend

The programmable surface once an agent is there — machine-readable spec, interactive docs, sandbox, versioning discipline.

Explore an interactive API reference with runnable examplesweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

  • [probe] https://qdrant.tech/openapi.json“PROBE openapi: all candidate paths 404 (https://qdrant.tech/openapi.json, https://qdrant.tech/swagger.json, https://qdrant.tech/api/openapi.json, https://qdrant.tech/.well-known/openapi.json)”
  • [claimed-docs] https://qdrant.tech/documentation/quickstart/“client.create_collection( collection_name="test_collection", vectors_config=VectorParams(size=4, distance=Distance.DOT), )”
  • [claimed-docs] https://qdrant.tech/documentation/quickstart/“docker run -p 6333:6333 -p 6334:6334 \ -v "$(pwd)/qdrant_storage:/qdrant/storage:z" \ qdrant/qdrant”

Download a machine-readable API spec (OpenAPI or equivalent)weight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

  • [probe] https://qdrant.tech/openapi.json“PROBE openapi: all candidate paths 404 (https://qdrant.tech/openapi.json, https://qdrant.tech/swagger.json, https://qdrant.tech/api/openapi.json, https://qdrant.tech/.well-known/openapi.json)”
  • [probe] https://qdrant.tech/documentation/.md“PROBE docs-md: HTTP 404 at https://qdrant.tech/documentation/.md”

Test against a sandbox environment without touching production dataweight 1

1 (weight) × 6 (quality) × 0.6 (partial) = 3.6 of 10 max

  • [claimed-docs] https://qdrant.tech/documentation/quickstart/“docker run -p 6333:6333 -p 6334:6334 \ -v "$(pwd)/qdrant_storage:/qdrant/storage:z" \ qdrant/qdrant”
  • [community] https://news.ycombinator.com/item?id=39101682“One of the big advantages of Qdrant is how easy it is to do a POC because it allows an 'in-memory' version similar to sqlite. Milvus by comparison needs a fairly intricate docker-compose setup.”
  • [claimed-docs] https://qdrant.tech/documentation/“No token limits ... No payment method required”

Rely on versioned APIs with a documented deprecation policyweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

  • [probe] https://qdrant.tech/openapi.json“PROBE openapi: all candidate paths 404 (https://qdrant.tech/openapi.json, https://qdrant.tech/swagger.json, https://qdrant.tech/api/openapi.json, https://qdrant.tech/.well-known/openapi.json)”
  • [probe] https://qdrant.tech/documentation/.md“PROBE docs-md: HTTP 404 at https://qdrant.tech/documentation/.md”

API quality = 3.6 ÷ 70 × 100 = 5.1

Openness59.2/100×0.20 of the PA blend

Can you leave, inspect, or self-host — data export, open source, portability.

Do everything through the API that I can do in the UIweight 2

2 (weight) × 6 (quality) × 0.6 (partial) = 7.2 of 20 max

  • [github] https://github.com/qdrant/qdrant“It provides a production-ready service with a convenient API to store, search, and manage points—vectors with an additional payload.”
  • [github] https://github.com/qdrant/qdrant“Go client ... Rust client ... JavaScript/TypeScript client ... Python client ... .NET/C# client ... Java client”
  • [community] https://news.ycombinator.com/item?id=39101682“I've been using Qdrant. Can't speak highly enough of the core functionality. It's fast, good accuracy, easy to use. Wish finding/updating points were easier, and the UI could be better.”
  • [probe] https://qdrant.tech/openapi.json“PROBE openapi: all candidate paths 404 (https://qdrant.tech/openapi.json, https://qdrant.tech/swagger.json, https://qdrant.tech/api/openapi.json, https://qdrant.tech/.well-known/openapi.json)”

Export all of my data in open formats and leaveweight 3

3 (weight) × 5 (quality) × 0.6 (partial) = 9.0 of 30 max

  • [claimed-docs] https://qdrant.tech/documentation/snapshots/“Snapshots are `tar` archive files that contain data and configuration of a specific collection on a specific node at a specific time.”
  • [claimed-docs] https://qdrant.tech/documentation/snapshots/“Snapshots are tar archive files that contain data and configuration of a specific collection on a specific node at a specific time.”
  • [community] https://news.ycombinator.com/item?id=38611033“I like their pricing page and business model: Apache-2.0 license, free tier with a free forever 1GB cluster for trying out, no credit card required.”
  • [github] https://github.com/qdrant/qdrant“Qdrant offers the following client libraries to help you integrate it into your application stack”
  • [github] https://github.com/qdrant/qdrant“Go client ... Rust client ... JavaScript/TypeScript client ... Python client ... .NET/C# client ... Java client”

Read the product's source under an open licenseweight 2

2 (weight) × 8 (quality) × 1.0 (full) = 16.0 of 20 max

  • [github] https://github.com/qdrant/qdrant“Qdrant offers the following client libraries to help you integrate it into your application stack”
  • [github] https://github.com/qdrant/qdrant“Go client ... Rust client ... JavaScript/TypeScript client ... Python client ... .NET/C# client ... Java client”
  • [community] https://news.ycombinator.com/item?id=38611033“I like their pricing page and business model: Apache-2.0 license, free tier with a free forever 1GB cluster for trying out, no credit card required.”
  • [community] https://news.ycombinator.com/item?id=35844724“After testing numerous open source vector databases, Qdrant is the best option: docs are clear, easy to build from source in Rust (~30 min), small portable binaries, clean and uncluttered compared to alternatives, devs are technically capable.”

Self-host the core productweight 3

3 (weight) × 9 (quality) × 1.0 (full) = 27.0 of 30 max

  • [claimed-docs] https://qdrant.tech/documentation/quickstart/“docker run -p 6333:6333 -p 6334:6334 \ -v "$(pwd)/qdrant_storage:/qdrant/storage:z" \ qdrant/qdrant”
  • [claimed-docs] https://qdrant.tech/documentation/scaling/distributed_deployment/“Qdrant supports a distributed deployment mode. In this mode, multiple Qdrant services communicate with each other to distribute the data across the peers”
  • [claimed-docs] https://qdrant.tech/documentation/deploy-intro/“Qdrant Private Cloud allows you to manage Qdrant database clusters in any Kubernetes cluster on any infrastructure.”
  • [claimed-docs] https://qdrant.tech/documentation/deploy-intro/“Deploy Qdrant on any infrastructure. Get requirements, configuration options, and GPU setup guides.”
  • [community] https://news.ycombinator.com/item?id=35844724“After testing numerous open source vector databases, Qdrant is the best option: docs are clear, easy to build from source in Rust (~30 min), small portable binaries, clean and uncluttered compared to alternatives, devs are technically capable.”
  • [community] https://news.ycombinator.com/item?id=39101682“We've been using Qdrant for over a year with 10s of millions of items, lots of daily inserts/deletions. A couple of gotchas but generally predictable and scalable; team focuses on the right features.”
  • [community] https://news.ycombinator.com/item?id=38611033“I like their pricing page and business model: Apache-2.0 license, free tier with a free forever 1GB cluster for trying out, no credit card required.”

Openness = 59.2 ÷ 100 × 100 = 59.2

Built-in AI8.6/100×0.15 of the PA blend

Inside-out: how agentic the product itself is for its users — built-in assistants, autonomous features.

Get AI-generated insights and suggestions from my data inside the productweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Set up automations that run autonomously in the backgroundweight 2

n/a — not applicable to this product: excluded from numerator and denominator

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Delegate tasks to a built-in AI assistant inside the productweight 3

3 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 30 max

  • [github] https://github.com/qdrant/qdrant“Install these skills to empower your agent in making critical engineering decisions for optimal vector search performance, such as quantization, sharding, tenant isolation, hybrid search, model migration, and more.”
  • [github] https://github.com/qdrant/qdrant“Qdrant provides a collection of ready-to-use agent skills that bring Qdrant's vector search capabilities directly into your AI coding assistant.”
  • [probe] https://qdrant.tech/documentation/qdrant-mcp-server/“official MCP server documented at https://qdrant.tech/documentation/qdrant-mcp-server/”

Operate the product with natural-language commandsweight 2

2 (weight) × 5 (quality) × 0.6 (partial) = 6.0 of 20 max

  • [probe] https://qdrant.tech/documentation/qdrant-mcp-server/“official MCP server documented at https://qdrant.tech/documentation/qdrant-mcp-server/”
  • [github] https://github.com/qdrant/qdrant“Install these skills to empower your agent in making critical engineering decisions for optimal vector search performance, such as quantization, sharding, tenant isolation, hybrid search, model migration, and more.”
  • [github] https://github.com/qdrant/qdrant“Qdrant provides a collection of ready-to-use agent skills that bring Qdrant's vector search capabilities directly into your AI coding assistant.”

Built-in AI = 6.0 ÷ 70 × 100 = 8.6

Automation6.0/100×0.15 of the PA blend

Depth of automation primitives — rules, scheduling, bulk operations, webhooks.

Perform bulk operations across many items at onceweight 2

2 (weight) × 3 (quality) × 0.6 (partial) = 3.6 of 20 max

  • [community] https://news.ycombinator.com/item?id=39101682“We've been using Qdrant for over a year with 10s of millions of items, lots of daily inserts/deletions. A couple of gotchas but generally predictable and scalable; team focuses on the right features.”
  • [claimed-docs] https://qdrant.tech/documentation/quickstart/“client.create_collection( collection_name="test_collection", vectors_config=VectorParams(size=4, distance=Distance.DOT), )”
  • [github] https://github.com/qdrant/qdrant“Go client ... Rust client ... JavaScript/TypeScript client ... Python client ... .NET/C# client ... Java client”

Define rules that trigger actions automatically on eventsweight 3

3 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 30 max

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Schedule recurring jobs or workflowsweight 2

n/a — not applicable to this product: excluded from numerator and denominator

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Version, review, and roll back my automationsweight 1

1 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 10 max

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Automation = 3.6 ÷ 60 × 100 = 6.0