Datadog observes what happened. Gostly makes it possible to know what will happen, by replaying the recorded upstream byte-equivalent before you deploy.
The honest framing
Feed deterministic pre-deploy regression signal into Datadog, not vs. Datadog. These tools live in different parts of the lifecycle and the teams using both stop comparing them within a week.
Datadog LLM Experiments (in preview through 2026) is the observability layer for agentic systems running in production. Agent runs become traces, LLM calls become spans, tool calls become spans, and the whole tree is queryable through the same Datadog surface your engineering team already uses for APM. Anomaly detection over agent metrics — token spend, tool-call rate, latency outliers — is a real strength. If your concern is “the agent is running in production and I need to know when something breaks,” Datadog is the conservative, mature choice.
Gostly answers the question that comes before that. When the agent has not yet shipped — when the code is sitting in a pull request, when the prompt template has changed, when the tool schema has been refactored — there is no production trace to observe. There are only the unit tests, the integration tests, and the agent eval suite. Each of those needs a substrate to run against. Live upstream APIs are unreliable (rate-limited, mutating, sometimes outright unavailable in CI). LLM-synthesised mocks are not reproducible (different shape on every call).
Gostly is that substrate. The proxy records real upstream responses, redacts them, and replays them byte-equivalent on every CI run. Same agent trajectory, same tool observations, same regression signal. When the agent ships and starts producing Datadog traces in production, both layers are present: Gostly bounded what could happen before deploy; Datadog reports what is happening now.
| Feature | Datadog LLM Experiments | Gostly |
|---|---|---|
| Production observability for agent runs | first-class — the wedge | No |
| Pre-deploy regression testing of agent trajectories | No | first-class |
| Byte-equivalent replay of recorded upstream responses | No | Yes |
| Span & trace correlation across agent + LLM + tool | shipping (preview) | partial — proxy spans only |
| Anomaly detection on agent metrics | shipping | No |
| Recorded upstream behavior, not synthesized | n/a | Yes |
| Self-hostable mock substrate | No | single Rust binary or K8s |
| MCP server for agent introspection | No | Team tier — shipped |
| Integrates with existing APM dashboards | native | exports OpenTelemetry spans |
| Catches the failure before the agent runs in prod | No | Yes |
| Catches the failure when the agent runs in prod | Yes | No |
Datadog wins decisively on production observability scale and integration depth. Gostly wins decisively on pre-deploy reproducibility. There is no overlap to fight over.
Choose Datadog when
Choose Gostly when
Pre-deploy: the agent runs in CI against a Gostly mock library. Recorded upstream responses, byte-equivalent replay. Every PR exercises the same trajectory; regressions surface as a diff in the recorded library or a test that now fails. The agent never touches the real upstream during the test run.
Post-deploy: the agent ships to production. Real tool calls go out, real responses come back, and Datadog LLM Experiments captures the whole trace. Anomaly detection alerts on agent runs that diverge from the production baseline. Operators get the context they need to debug.
The two layers exchange information: a production incident caught by Datadog becomes a recording captured by Gostly, which becomes a regression test that runs on every PR going forward. The feedback loop closes inside CI, before the next deploy.
Byte-equivalent replay of your real APIs. Recorded upstream behaviour, not synthesized. Pipes into Datadog (or anywhere) via OpenTelemetry.
Evaluating for a team of 3+? We’d love to talk before you commit.