Datadog

Drive the product through a documented public API G

Agent access

Connect an agent via an official MCP server G

Agent access

Delegate tasks to a built-in AI assistant inside the product G

Agentic features

Plug MCP servers into this product so it can use their tools G

Agent access

Point an agent at llms.txt or agent-oriented docs G

Agent access

Use an official CLI G

Agent access

Get AI-generated insights and suggestions from my data inside the product G

Agentic features

Run the product headlessly / in CI for automation G

Agent access

Build against official SDKs G

Agent access

Operate the product with natural-language commands G

Agentic features

Set up automations that run autonomously in the background G

Agentic features

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

Api quality

Rely on versioned APIs with a documented deprecation policy G

Api quality

Explore an interactive API reference with runnable examples G

Api quality

Issue scoped/least-privilege API credentials for an agent G

Agent access

Subscribe to events via webhooks G

Agent access

Test against a sandbox environment without touching production data G

Api quality

Alert on any telemetry signal with routing, grouping, and silencing of notifications C

Alerting

Collect metrics, logs, and traces in one platform and pivot between them with shared context C

Signals

Have an external agent query metrics, logs, and traces through documented APIs to debug production C

Agent integration

Send telemetry directly over OTLP with first-class OpenTelemetry support C

Otel

Analyze telemetry ad hoc with a documented query language C

Query language

Define rules that trigger actions automatically on events G

Have the platform's AI investigate an alert or error and propose a probable root cause C

Ai investigation

Define dashboards and alerts as code (JSON models, Terraform, or API) and provision them repeatably C

As code

See what my observability spend is, attribute it to teams or services, and catch usage spikes before the bill C

Cost

Export all of my data in open formats and leave G

Prevent my data from being used to train AI models G

Self-host the core product G

Instrument hosts, containers, Kubernetes, and cloud services through vendor-maintained agents and integrations C

Instrumentation

Define SLOs with error budgets and burn-rate alerts C

Slos

Jump from a trace span to its correlated logs and metrics to debug a request end to end C

Correlation

Build shareable dashboards with rich visualization types and template variables C

Dashboards

Correlate regressions with deploys and configuration changes via release or change tracking C

Change tracking

Declare and track incidents with timelines, on-call schedules, and escalation policies C

Incidents

Do everything through the API that I can do in the UI G

Get AI-generated summaries of incidents and alert context for responders C

Ai investigation

Instrument once with open standards and switch backends without re-instrumenting my code C

Otel

Ask questions of my telemetry in natural language and get a real query or chart back C

Ai querying

Group and filter by high-cardinality fields (user id, request id) without pre-aggregating or defining indexes first C

Analysis

Perform bulk operations across many items at once G

Control trace/log sampling and retention tiers to manage data volume deliberately C

Sampling

See application errors grouped into issues with stack traces, release tracking, and regression detection C

Errors

Choose where my data is stored (region/residency) G

Control data retention and deletion G

Opt out of telemetry and usage tracking G

Point alert notifications at webhooks that trigger automated remediation or agents G

Alert automation

Read the product's source under an open license G

Run the full observability stack self-hosted in production with documented architecture and upgrade path G

Self host

Schedule recurring jobs or workflows G

Enable anomaly or outlier detection that surfaces problems without hand-written thresholds C

Alerting

Predict costs from transparent published per-signal pricing without talking to sales G

Cost

Spin up a local or dev instance of the platform to test instrumentation and dashboards C

Local dev

Version, review, and roll back my automations G