AGENT OBSERVABILITY

Ship AI agents faster,with confidence

Evaluate, improve, and trace your AI agents with offline experimentation and production observability in one platform.

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Supports leading models, frameworks, and agent frameworks

Benefits

Everything you need to monitor at scale

Move from prototype to production faster in one platform. Validate quality before release, operate with enterprise-grade tracing and security, and troubleshoot at every layer of the stack without switching tools.

Battle-Tested Tracing

Instrument once and run the same Datadog tracer from development through production. Built on the same tracing technology trusted by 60% of the Fortune 500 and leading AI labs, AI teams get dependable tracing as agent usage scales.

Enterprise-Grade Controls

Confidently run AI in production with precise alerting, role-based access control, sensitive data protection, HIPAA compliance, and the governance teams need to reduce risk without slowing releases.

Complete Application Context

Iterate faster with visibility across requests, services, and upstream and downstream dependencies to pinpoint failures. Correlate agent behavior with backend performance and end-user impact in one shared platform.

One trace connects your AI system from backend services to end-user experience

Datadog brings application monitoring, agent observability, and digital experience into one continuous flow. When an issue occurs, teams can investigate the same request from service activity to agent reasoning to customer impact to quickly find the root cause and keep their AI systems running smoothly.

BUILT FOR YOUR ROLE

Bring every team together in one platform

Give every team involved in AI delivery the context they need to move faster, reduce risk, and operate reliably — from prompt iteration to production response.

Ship improvements backed by evidence

Test prompt, model, and tool changes against real production data before rollout. Trace every agent step in production, compare configurations side by side, and debug failures without stitching together separate tools.

Build versioned datasets from production traces
Run experiments across prompts, models, and agent configurations
Inspect execution graphs, tool decisions, latency, and token usage
Move from playground testing to production RCA in one workflow

Turn evaluation into a repeatable system

Move beyond ad hoc spot checks with structured datasets, automated evaluators, and human review. Measure model and agent quality over time, catch drift earlier, and make tradeoffs between accuracy, cost, and latency with more confidence.

Create and version golden datasets from real interactions
Run out-of-the-box and custom evaluators aligned to your KPIs
Use human review and annotation to label outputs at scale
Compare quality, cost, and latency across releases

Keep AI systems reliable without adding another silo

LLM Observability brings AI builders into the same platform SRE and DevOps teams already use. That shared context makes it easier to correlate agent behavior with services, infrastructure, and user experience, so everyone can troubleshoot faster and operate the full stack more reliably.

Correlate LLM spans with APM services, infra signals, and RUM sessions
Share one tracer and one workflow across development and production
Detect latency, cost, and quality issues earlier with actionable monitors
Cut MTTR by tracing failures across the full application stack

Prove ROI while reducing production risk

Get the visibility leaders need across performance, quality, cost, and user impact in one place. Validate changes before rollout, monitor production health continuously, and scale AI programs with stronger governance and fewer surprises.

See cost, quality, latency, and reliability side by side
Give teams evidence-based validation before launch
Reduce business and compliance risk with built-in controls
Align AI initiatives to customer experience and platform health

Features

Your development toolkit for the AI agent era

Iterate fast

Datasets from production traces

Turn real production traces into versioned datasets you can test against. Capture the exact scenarios your system handles so changes get validated on real behavior.

Compare prompts and models

Run experiments that compare prompts, models, and configurations against the same data. See which version performs best before anything reaches users.

Improve with human feedback

Fold real interactions and human feedback into every iteration. Refine system behavior before you ship, not after users hit the gaps.

Evaluate quality

Built-in and custom evaluators

Start with built-in evaluators or define custom ones tied to your KPIs. Measure what matters to your team instead of generic scores.

Annotate and review outputs

Label and grade outputs with annotations and human review. Bring expert judgment into evaluation where automated checks fall short.

Catch unsafe outputs

Catch hallucinations, prompt injection attempts, and PII exposure as they happen. Track quality trends across releases so regressions never slip through.

Monitor behavior

Trace every step

Follow every request across prompts, retrieval steps, tool calls, and agent decisions. See exactly how your system reached each response.

Track latency and usage

Monitor latency, token usage, retries, and errors at every step. Spot the slow or heavy calls without guessing where they hide.

Pinpoint failures fast

Find bottlenecks, failures, and surprise costs with full execution context. Move from a symptom to the exact step that caused it.

Unify context

Correlate with your stack

Connect agent performance to the services and infrastructure underneath. See when a slow database or starved GPU is really the problem.

Link to user sessions

Tie response time and quality back to real user sessions. Know how agent behavior actually lands for the people using it.

One platform, no switching

Keep tracing, experiments, and evaluations in one place. Debug faster when you're not jumping between four different tools.

Explore

Try Agent Observability in your browser

Want to start locally?

Trace your AI apps and coding agents on your machine, free and no signup needed.

Start tracing locally

Pricing

Priced for startups, built for enterprise

Scale production-grade AI agents with enterprise-grade controls — starting for free.

How we price

Per LLM spans

Per LLM spans — not tool spans, workflow spans, or retrieval spans

Why it matters

Pay for actual AI work

You pay for the actual AI work, not every surrounding step in the workflow. That makes pricing simpler to forecast as agents become more complex.

Pay for what you use

Pricing by Tier

1. Additional on-demand usage is billed after the first 100K LLM spans 2. Retention add-ons extend traces to 30, 60, or 90 days and experiments to 6, 9, or 12 months 3. Sensitive Data Scanner is included and scales with LLM usage

Every package includes our full agent engineering platform, so teams at every growth stage get what they need to compete. Ship faster with end-to-end tracing, datasets, experiments, prompt workflows, evaluations, annotations, production monitoring, custom dashboarding, CLI and MCP access – all available to you from day one.

Customers

Fast-growing teams ship production-ready AI with Datadog

Teams who improve quality, move faster, and deploy AI safely at scale. Join 33,000+ organizations that trust Datadog to keep their infrastructure running

400%

faster MTTR

40%

lower token usage per task

15%

faster deployment

AI Cybersecurity

Datadog LLM Observability gives us complete visibility into our agents' reasoning so we can reduce cost, improve reliability, and ship with confidence.

Read the customer story

AI financial copilot

We’ve improved response accuracy and reduced latency, ensuring faster, more reliable insights for our customers.

Read the customer story

AI Property Manager

Helped us ensure high model performance and quality, and allowed us to expand functionality quickly and safely.

Read the customer story

Install

Get Started In Minutes

Instrument your application with one prompt via AI assistants

Install MCP

Install the MCP (Model Context Protocol) server in your terminal

claude mcp add --transport http datadog-onboarding-us1 "https://mcp.datadoghq.com/api/unstable/mcp-server/mcp?toolsets=onboarding" && claude /mcp

Run Prompt header

Copy this prompt into your terminal

Run Prompt

Add Datadog LLM Observability to my project

Install MCP

Install the MCP (Model Context Protocol) server in Cursor

Run Prompt

Add Datadog LLM Observability to my project

Set Up Manually

Install the SDK

pip install ddtrace

Prefix your Python start command with ddtrace-run

DD_SITE=<SITE> \
DD_LLMOBS_ENABLED=1 \
DD_LLMOBS_ML_APP=<APPLICATION_NAME> \
DD_API_KEY=<API_KEY>  \
ddtrace-run <your application command>

Install the SDK

npm install dd-trace

Prefix your Node start command with ddtrace-run

DD_SITE=<SITE> \
DD_LLMOBS_ENABLED=1 \
DD_LLMOBS_ML_APP=<APPLICATION_NAME> \
DD_API_KEY=<API_KEY> \
NODE_OPTIONS="--import dd-trace/initialize.mjs" <your application command>

Install the SDK

wget -O dd-java-agent.jar 'https://dtdg.co/latest-java-tracer'

Prefix your Java start command with ddtrace-run

java -javaagent:/path/to/dd-java-agent.jar \
-Ddd.site=<SITE> \
-Ddd.llmobs.enabled=true \
-Ddd.llmobs.ml.app=<APPLICATION_NAME> \
-Ddd.api.key=<API_KEY> \
-jar path/to/your/app.jar

FAQ

Frequently Asked Questions

What is Agent Observability?

Datadog Agent Observability helps teams evaluate, improve, and trace AI agents across development and production in one platform. It connects experimentation, evaluations, and production observability so teams can ship faster with more confidence.

What is an LLM span?

An LLM span is one call to an LLM provider such as OpenAI or Anthropic. One agent workflow can create multiple LLM spans, and Datadog bills only on those LLM spans rather than every surrounding span in the workflow.

What does Datadog Agent Observability include?

It includes end-to-end LLM tracing, datasets, experiments, a testing playground, offline and online evaluations, prompt workflows, human review and annotation, and production monitoring. Every package includes the full workflow rather than gating core capabilities by plan.

Which models, frameworks, and languages does Agent Observability support?

Agent Observability supports leading models, frameworks, and agent frameworks including OpenAI, Anthropic, Gemini, Vertex AI, LangChain, CrewAI, Pydantic, Bedrock, LiteLLM, and Strands Agents. Teams can instrument applications in Python, Node.js, or Java, and use OpenTelemetry or the HTTP API for other environments.

How quickly can I get started?

Most teams can get started in minutes using SDK instrumentation or AI-assisted setup. Auto-instrumentation can capture traces for common LLM providers and orchestration frameworks, which reduces manual setup.

How long is Agent Observability data retained?

On-demand Free and Pro plans have a 15-day trace, span and experiment data retention. When committing to M2M or Annual contracts, trace and span data are retained for 15 days. Experiment results are retained for 90 days.

Retention add-ons extend traces and spans to 30, 60, or 90 days and extend experiments to 6, 9, or 12 months.

Datasets have a separate 3-year retention and are versioned so you can rerun experiments against the same baseline and compare results over time.

Can I instrument a custom LLM stack or nonstandard language?

Yes. If your application can emit spans through OpenTelemetry or the HTTP API, you can instrument custom frameworks and other languages beyond the standard SDKs. That gives teams a path to bring nonstandard AI stacks into the same workflow.

How does pricing work?

Free includes up to 40K LLM spans per month. Pro starts at $160 per month and includes 100K LLM spans.

Additional on demand usage is billed after the first 100K LLM spans. Retention add ons are billed per 10K LLM spans. M2M and annual commitments are discounted.

What do evals cost?

There is no separate product fee for offline or online evaluations. Every plan includes the full evaluation workflow.

If an eval run makes LLM calls, those calls count as LLM spans. You are not charged a separate eval fee on top of that.

How do evaluations, monitoring, and alerts work together?

Teams can build datasets from traces, run experiments and evaluators before release, then monitor quality, latency, cost, and failures in production using the same system. That closes the loop between pre-production improvement and live production response.

What security, compliance, and data governance controls are included?

Agent Observability includes sensitive data scanning and redaction, role-based access control, precise alerting, and enterprise-grade controls designed to help teams operate AI safely in production. Sensitive Data Scanner is built in and scales with usage.

How is Datadog Agent Observability different from other tools?

Datadog connects development and production in one workflow, so teams can experiment with real production data, validate changes before rollout, and trace issues across the full application stack. It also unifies AI agent behavior with backend services and user experience, which reduces tool switching and speeds root cause analysis.

Resources & Learning

Guides, research, and technical content to help teams build, evaluate, and operate AI agents with confidence.

Whitepaper

State of AI Engineering

See how engineering teams are moving from AI prototypes to production systems, where quality and reliability break down, and what high-performing teams do differently as they scale.

blog

Build better review workflows with Annotation Queue

Learn how structured human review helps teams label outputs faster, improve dataset quality, and create a tighter feedback loop between experimentation and production.

documentation

Evaluations

Learn how to run out-of-the-box and custom evaluators, compare experiment results, and monitor quality over time using the same workflow.

documentation

Instrumentation

Follow quickstarts for Python, Node.js, Java, OpenTelemetry, or the HTTP API to get traces flowing quickly and bring your AI system into Datadog.