# Coolhand vs Datadog

> Datadog's LLM Observability module gives you a single pane of glass across your whole stack. Coolhand diagnoses
> agent issues and opens the fix as a PR. Here's how they fit.

## What is Datadog LLM Observability?

Datadog LLM Observability (marketed as "Agent Observability") is a module inside Datadog's broader platform, not a
standalone product. It maps agent decision paths — tool calls, handoffs, loops — evaluates output quality, flags
prompt-injection and security issues, and unifies all of it with whatever else you're already running in Datadog:
APM, infrastructure monitoring, and real user monitoring. That unification, not any single LLM-specific feature, is
Datadog's real pitch.

To be precise about auto-remediation: Datadog's LLM Observability module itself doesn't fix anything. Datadog
separately sells a general-purpose code-fixing agent called Bits Code elsewhere in its product line, but it isn't
wired into agent-trace data — it works off APM, error tracking, and security findings across any service, not
specifically your LLM agents.

## What Coolhand actually does

Coolhand watches your production AI agents continuously. When something breaks — a hard error, a quality
regression, a spike in cost — it diagnoses the root cause against your actual code, drafts the fix, and opens it
as a pull request in your repo. Nothing merges without a human reviewing it first. Alongside that, an open-source
skill audits your codebase for places to capture feedback that's already happening — edits, approvals,
corrections — instead of asking you to build a new annotation queue. Cost and quality dashboards then show whether
all of this is actually working, in dollars and quality-trend terms, not just "traces logged."

Where Coolhand excels: it's the only thing in this loop that turns a diagnosed problem into a reviewable code
change on its own, continuously and without per-issue manual triggering. It doesn't need an annotation team, a
dedicated eval engineer, or someone babysitting a dashboard — the loop runs in the background and only asks for
your attention when there's a PR to review.

That's the gap Datadog's LLM Observability leaves open: it will tell you an agent misbehaved and correlate it
against your infrastructure, but nothing in that module writes the fix. Coolhand closes that gap directly, tied to
the same agent-trace data Datadog surfaces.

## Feature comparison

| Capability | Datadog | Coolhand |
|---|---|---|
| Primary purpose | Unify agent/LLM traces with the rest of your observability stack | Diagnose production agent issues and ship the fix as a PR |
| Tracing depth | Deep — agent decision graphs, loop detection, cross-stack correlation with APM/infra | Request-level logs built for diagnosis, not a dedicated trace explorer |
| Evaluation | Built-in and custom evaluators, security/prompt-injection scanning | Correctness and sentiment evaluators feeding the diagnosis loop |
| Human feedback | Not a core focus of the LLM Observability module | Passive capture from your app's existing UI — no queue to build or staff |
| Opens a PR with a fix | No, not from LLM Observability itself — Bits Code exists but isn't triggered by agent-trace data | Yes — opens a real PR in your repo, triggered directly by agent production issues |
| Ingestion | Python/Node/Java SDKs, OpenTelemetry, HTTP API | Ruby/Python/Node SDKs and provider proxies; no OpenTelemetry endpoint yet |
| Self-hostable server | No — SaaS platform only | No — managed service only (SDKs, CLI, and widget are open source) |
| ROI reporting | Cost/latency dashboards, cross-stack correlation; ROI framing is on you | Cost-per-outcome and quality-trend dashboards built in |
| Pricing entry point | Free up to 40k LLM spans/month, then $160/month Pro | Free up to 10M tokens/week |

## When you need both

If you're already running Datadog for infrastructure and APM, adding LLM Observability is a natural extension —
you get agent traces in the same dashboards, correlated with the rest of your stack, without standing up a
separate tool. You need Coolhand once you want that agent-specific signal turned into an actual code change.
Datadog will tell you an agent looped, called the wrong tool, or tripped a quality evaluator. It won't draft the
prompt or tool-call fix and open the PR; that's still an engineer's job unless Coolhand is doing it.

## How to use them together

Let Datadog stay the single pane of glass for your whole stack — infra, APM, and agent traces together, with
alerting routed through whatever on-call setup you already have. Point Coolhand's SDKs at the same production
traffic so it's watching the agent-specific signal in parallel. When Coolhand proposes a fix, use Datadog's
cross-stack view to confirm the surrounding infra and deploy context before merging. Datadog tells you where an
issue sits relative to everything else running; Coolhand tells you what to change in the agent itself and hands
you the PR to do it.

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Source: [coolhandlabs.com/beyond-observability/datadog](https://coolhandlabs.com/beyond-observability/datadog)