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How Datadog’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 Score27/100

Agent-ready 45.8 × 0.30 = 13.74

API quality 13.7 × 0.20 = 2.74

Openness 7.2 × 0.20 = 1.44

Built-in AI 38.9 × 0.15 = 5.83

Automation 19.5 × 0.15 = 2.92

(13.74 + 2.74 + 1.44 + 5.83 + 2.92) ÷ (0.30 + 0.20 + 0.20 + 0.15 + 0.15) = 26.68 ÷ 1.00 = 26.7

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-ready45.8/100×0.30 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://docs.datadoghq.com/llms.txt“PROBE llms.txt: HTTP 200 at https://docs.datadoghq.com/llms.txt # Datadog documentation > Documentation index for the Datadog observability platform. Covers infrastructure monitoring,”
  • [claimed-docs] https://docs.datadoghq.com/mcp_server/“Datadog MCP Server”
  • [probe] https://docs.datadoghq.com/mcp_server/“official MCP server documented at https://docs.datadoghq.com/mcp_server/”

Run the product headlessly / in CI for automationweight 2

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

  • [probe] https://github.com/DataDog/datadog-ci“official CLI documented at https://github.com/DataDog/datadog-ci”
  • [claimed-docs] https://docs.datadoghq.com“CI Visibility”
  • [claimed-docs] https://docs.datadoghq.com/api/latest/“API Reference”

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

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

  • [claimed-docs] https://docs.datadoghq.com/mcp_server/“Datadog MCP Server”
  • [probe] https://docs.datadoghq.com/mcp_server/“official MCP server documented at https://docs.datadoghq.com/mcp_server/”
  • [claimed-docs] https://docs.datadoghq.com/bits_ai/“Bits AI”

Connect an agent via an official MCP serverweight 3

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

  • [claimed-docs] https://docs.datadoghq.com/mcp_server/“Datadog MCP Server”
  • [probe] https://docs.datadoghq.com/mcp_server/“official MCP server documented at https://docs.datadoghq.com/mcp_server/”

Use an official CLIweight 2

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

  • [probe] https://github.com/DataDog/datadog-ci“official CLI documented at https://github.com/DataDog/datadog-ci”

Drive the product through a documented public APIweight 3

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

  • [claimed-docs] https://docs.datadoghq.com/api/latest/“API Reference”
  • [probe] https://github.com/DataDog/datadog-ci“official CLI documented at https://github.com/DataDog/datadog-ci”
  • [probe] https://docs.datadoghq.com/mcp_server/“official MCP server documented at https://docs.datadoghq.com/mcp_server/”

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

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

  • [claimed-docs] https://docs.datadoghq.com/mcp_server/“Datadog MCP Server”
  • [claimed-docs] https://docs.datadoghq.com/api/latest/“API Reference”

Build against official SDKsweight 2

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

  • [claimed-docs] https://docs.datadoghq.com/api/latest/“API Reference”
  • [claimed-docs] https://docs.datadoghq.com/opentelemetry/“OpenTelemetry in Datadog”
  • [probe] https://github.com/DataDog/datadog-ci“official CLI documented at https://github.com/DataDog/datadog-ci”
  • [claimed-docs] https://docs.datadoghq.com/mcp_server/“Datadog MCP Server”

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 = 96.2 ÷ 210 × 100 = 45.8

API quality13.7/100×0.20 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

  • [claimed-docs] https://docs.datadoghq.com/api/latest/“API Reference”
  • [probe] https://docs.datadoghq.com/openapi.json“PROBE openapi: all candidate paths 404 (https://docs.datadoghq.com/openapi.json, https://docs.datadoghq.com/swagger.json, https://docs.datadoghq.com/api/openapi.json, https://docs.datadoghq.com/.well-known/openapi.json)”

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

2 (weight) × 4 (quality) × 0.6 (partial) = 4.8 of 20 max

  • [claimed-docs] https://docs.datadoghq.com/api/latest/“API Reference”
  • [probe] https://docs.datadoghq.com/openapi.json“PROBE openapi: all candidate paths 404 (https://docs.datadoghq.com/openapi.json, https://docs.datadoghq.com/swagger.json, https://docs.datadoghq.com/api/openapi.json, https://docs.datadoghq.com/.well-known/openapi.json)”

Test against a sandbox environment without touching production dataweight 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

Rely on versioned APIs with a documented deprecation policyweight 2

2 (weight) × 4 (quality) × 0.6 (partial) = 4.8 of 20 max

  • [claimed-docs] https://docs.datadoghq.com/api/latest/“API Reference”

API quality = 9.6 ÷ 70 × 100 = 13.7

Openness7.2/100×0.20 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

  • [claimed-docs] https://docs.datadoghq.com/api/latest/“API Reference”
  • [probe] https://github.com/DataDog/datadog-ci“official CLI documented at https://github.com/DataDog/datadog-ci”
  • [probe] https://docs.datadoghq.com/mcp_server/“official MCP server documented at https://docs.datadoghq.com/mcp_server/”
  • [claimed-docs] https://docs.datadoghq.com/bits_ai/“Bits AI”
  • [claimed-docs] https://docs.datadoghq.com/watchdog/“Datadog Watchdog™”

Export all of my data in open formats and leaveweight 3

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

  • [claimed-docs] https://docs.datadoghq.com/opentelemetry/“OpenTelemetry in Datadog”
  • [claimed-docs] https://docs.datadoghq.com/api/latest/“API Reference”

Read the product's source under an open licenseweight 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

Self-host the core productweight 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

Openness = 7.2 ÷ 100 × 100 = 7.2

Built-in AI38.9/100×0.15 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) × 7 (quality) × 1.0 (full) = 14.0 of 20 max

Set up automations that run autonomously in the backgroundweight 2

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

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

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

Operate the product with natural-language commandsweight 2

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

  • [claimed-docs] https://docs.datadoghq.com/bits_ai/“Bits AI”
  • [claimed-docs] https://docs.datadoghq.com/mcp_server/“Datadog MCP Server”
  • [probe] https://docs.datadoghq.com/mcp_server/“official MCP server documented at https://docs.datadoghq.com/mcp_server/”

Built-in AI = 35.0 ÷ 90 × 100 = 38.9

Automation19.5/100×0.15 of the PA blend

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

Perform bulk operations across many items at onceweight 2

2 (weight) × 4 (quality) × 0.6 (partial) = 4.8 of 20 max

  • [claimed-docs] https://docs.datadoghq.com/api/latest/“API Reference”
  • [probe] https://github.com/DataDog/datadog-ci“official CLI documented at https://github.com/DataDog/datadog-ci”
  • [claimed-docs] https://docs.datadoghq.com/mcp_server/“Datadog MCP Server”

Define rules that trigger actions automatically on eventsweight 3

3 (weight) × 6 (quality) × 0.6 (partial) = 10.8 of 30 max

  • [claimed-docs] https://docs.datadoghq.com/monitors/“Monitors”
  • [claimed-docs] https://docs.datadoghq.com/watchdog/“Datadog Watchdog™”
  • [claimed-docs] https://docs.datadoghq.com/incident_response/incident_management/“Incident Management”
  • [claimed-docs] https://docs.datadoghq.com/api/latest/“API Reference”

Schedule recurring jobs or workflowsweight 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

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 = 15.6 ÷ 80 × 100 = 19.5