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Datadog for AI

AI you can actually
see, trust, and run.

AI moves fast and breaks quietly. We use Datadog to give you full visibility into your AI workloads, and we use Datadog's own AI to run your cloud leaner. Two sides of the same platform, one Powered by Datadog partner.

Talk to us Datadog services
LLM & agents
Observability & evaluation
GPU fleets
Health, utilisation & cost
AI-native ops
24×7 with Bits AI & Watchdog
Powered by Datadog
World's first accredited MSP
Two sides of the platform
Datadog for AI
LLM Observability · Agent tracing · GPU Monitoring · Cost & quality evaluation
AI for Datadog
Bits AI SRE · Watchdog · Security Analyst · MCP & agents
We implement the first for your team and use the second inside Critical Support.
Datadog for AI · most important

Visibility into AI workloads you can act on

You're building AI features, agents, and models. Getting them to production is one thing, knowing they're reliable, safe, and not quietly burning money is another. That's where Datadog comes in, and where we help you implement it properly.

LLM & Agent Observability

Modern AI apps are chains of prompts, retrieval steps, tool calls, and model responses and when something goes wrong, the failure is buried somewhere in that chain. Datadog LLM Observability traces every step of an agent or LLM request, so you can see exactly where latency, errors, runaway token costs, or bad outputs come from. It continuously evaluates quality, catching hallucinations, prompt-injection attempts, unsafe responses, and exposed sensitive data and lets you test prompt, model, and logic changes against real production data before you ship them. It works with the models and frameworks teams actually use: OpenAI, Anthropic, Gemini, Bedrock, LangChain, CrewAI, and more.

How we help: we instrument your AI stack, set up the evaluations that matter for your use case, and connect AI behaviour to the rest of your services and infrastructure, so AI quality becomes something you can measure and govern, not guess at. This is the focus of our four-week AI Observability Accelerator.

LLM tracing Agent observability Quality evaluation Cost visibility Sensitive data scanning

GPU Monitoring

AI runs on expensive, scarce hardware and most teams have no clear view of whether their GPUs are healthy, well-used, or quietly idle. Datadog GPU Monitoring gives you one view across your whole fleet, whether it's in the cloud, on-prem, or with a neocloud provider, linking GPU health, utilisation, and cost back to the workloads and teams using them. You can right-size and forecast capacity, reclaim GPUs stuck on zombie processes, catch thermal throttling and hardware errors (ECC/XID) before they cascade, and break down spend by team to stop the waste.

How we help: we set up fleet-wide GPU visibility and the alerting that protects your training and inference workloads, so you scale AI on infrastructure you can actually account for, by performance and by cost.

Fleet-wide visibility Utilisation & health Cost attribution ECC/XID error alerting Cloud, on-prem & neocloud

Together, these take you from "the AI works on my machine" to "the AI is observable, evaluated, and cost-controlled in production." That's the bar we hold AI to.

AI for Datadog · how we operate

AI-native managed operations

We don't just monitor your systems, we operate them. And we use Datadog's own AI capabilities as force multipliers, so our engineers spend their time on judgement and improvement, not on grinding through noise. This is what AI-native managed operations actually looks like.

Bits AI SRE

Datadog's Bits AI SRE is an autonomous investigation agent, grounded in large volumes of real-world incident data, that triages alerts and proposes likely root causes.

How we use it: as a first-line investigator inside Critical Support, it runs in parallel the moment an alert fires, so our on-call engineers arrive at an incident with context already gathered, not a blank screen.

Watchdog

Watchdog is Datadog's machine-learning engine that automatically surfaces anomalies and probable root causes across metrics, logs, and traces, no manual thresholds required.

How we use it: it helps us catch the issues that don't trip a static alert, so problems get seen before they become incidents.

Bits AI Security Analyst

An AI agent that autonomously triages and investigates security signals, effectively a first-line SOC analyst.

How we use it: to accelerate security signal triage in the environments we operate, so genuine threats surface faster and noise gets filtered.

MCP Server & Bits AI Agents

Datadog's MCP (Model Context Protocol) server connects AI assistants and agents securely to Datadog data and APIs, and Bits AI provides a conversational interface to query observability data and take action.

How we use it: we integrate Datadog's MCP server into our own AI-native operations tooling, so our agents can query live observability data, generate runbooks, and automate investigation steps, safely and auditably.

Agent Directory. Datadog's Agent Directory catalogues the AI agents that work with the platform. As AI agents start operating inside production environments, they need to be observed and governed like any other workload and we build that in from the start.

Datadog Advanced Partner and the world's first "Powered by Datadog" accredited MSP- Datadog isn't a tool we resell, it's the operational backbone we run on, AI included.

FAQ

Can you help us monitor our own AI and LLM applications?

Yes, LLM and agent observability is a core part of what we do, including continuous evaluation for hallucinations, safety, prompt injection, and cost. We instrument your AI stack, connect it to the rest of your services in Datadog, and set up the evaluations that matter for your use case. See the AI Observability Accelerator for a four-week, fixed-scope delivery.

Do you support GPU monitoring for AI training and inference?

Yes, across cloud, on-prem, and neocloud GPU fleets. Datadog GPU Monitoring covers hardware health, utilisation, thermal and ECC/XID error detection, cost attribution by team, and capacity forecasting. We set up the fleet-wide visibility and alerting to protect your training and inference workloads.

What does "AI-native operations" actually mean?

It means we use Datadog's own AI inside our managed service rather than treating AI as a separate layer. In practice: Bits AI SRE investigates alerts in parallel when they fire, Watchdog surfaces anomalies that don't trip static thresholds, Bits AI Security Analyst triages security signals, and Datadog's MCP server lets our agents query live observability data safely. Our engineers focus on judgement and improvement, not on manual noise reduction.

Which AI models and frameworks does Datadog support?

OpenAI, Anthropic, Gemini, Amazon Bedrock, LangChain, CrewAI, and more. Datadog's LLM Observability integrates with the models and orchestration frameworks teams actually use. We'll map what Datadog supports to your specific stack and instrument accordingly.

Is our AI and observability data kept secure?

Yes, we operate to ISO 27001 and Cyber Essentials Plus, and Datadog includes sensitive-data scanning to automatically redact PII from LLM prompts and completions before they are stored. EU data residency is available for customers who require it. You retain direct access to your Datadog environment at all times.

Building AI, or running it in production?

Whether you need eyes on your AI workloads or an operations partner that runs Datadog with AI built in, let's talk.

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