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Traceloop

AI observability and workflow platform for monitoring experiments and tracking model performance.

Our take: Traceloop is an LLM-specific observability tool that traces prompts, responses, latency, and quality through its open-source OpenLLMetry SDK.

Best forDevelopers
ReviewedPricing: Freemium
7.6 out of 105 criteria · see breakdown
Try TraceloopFree up to 50K spans/mo; Enterprise custom
The bottom line

The go-to open-standards LLM observability layer for AI teams that want vendor-neutral traces and automated quality gates.

Our score

Reviewed by the Conversion Gems editorial team. Hover a facet to focus it; click to pin.Tap a facet to focus it.

What it is, and why we rate it

One line each. Open a row for detail.

What it really isTraceloop — LLM observability and reliability platform built on OpenTelemetry.
Our takeTraceloop is an LLM-specific observability tool that traces prompts, responses, latency, and quality through its open-source OpenLLMetry SDK.
Traceloop offers a Free Forever tier (up to 50K spans/month) plus custom Enterprise pricing, making it freemium. Acquired by ServiceNow in March 2026, the product remains focused on helping AI teams ship reliable LLM applications with automated CI/CD quality gates and real-time drift detection.
Why we rate itOpenTelemetry-native architecture and multi-language support (Python, TypeScript, Go, Ruby) make Traceloop the least lock-in-heavy LLM observability option on the market.
The OpenLLMetry OSS library plugs into 25+ existing observability platforms, so teams with Datadog or Grafana already in-house can adopt it without a new SaaS dashboard.
The catchThe free tier's 24-hour data retention makes it impractical for real production debugging — you'll hit Enterprise (opaque, contact-sales pricing) with no self-serve paid middle tier.
ServiceNow's 2026 acquisition also introduces roadmap uncertainty for independent users.

Best / not good for

Best for
  • LLM app teams wanting open-standards tracing via OpenTelemetry without vendor lock-in
  • MLOps teams enforcing automated quality gates and drift detection in CI/CD pipelines
  • Enterprises requiring SOC 2 and HIPAA-compliant AI application observability
Not good for
  • Teams monitoring traditional ML models (non-LLM) — focus is entirely on language models
  • Solo developers on the free tier who need more than 24 hours of trace history
  • Organizations wanting a fully self-hosted open-source stack — dashboards require the managed cloud service
Trade-offs

Friction report

Trade-offs to know before signing up.

  1. Time to value
    Fast: one line of code instruments an LLM app and surfaces traces in the dashboard immediately.
  2. Scale breakpoint
    At >50K spans/month the free tier ends with no self-serve paid option — teams must contact sales for custom Enterprise pricing.
  3. Walled garden
    Low: OpenTelemetry-based instrumentation and open-source SDK mean instrumentation is portable; switching away does not require rewriting tracing code.
Still a fit?
If none of these block you, start on the free plan.
Try Traceloop

Frequently Asked Questions

How can Traceloop assist with AI monitoring and observability?

Traceloop assists with AI monitoring and observability by tracking model performance, errors, and execution metrics in real-time.

How can Traceloop help with AI observability?

Traceloop helps with AI observability by monitoring model performance, logging outputs, and providing insights for debugging.

How can Traceloop help monitor AI model performance?

Traceloop helps monitor AI model performance by providing logging, metrics tracking, and anomaly detection.

How can Traceloop support AI model monitoring?

Traceloop supports AI model monitoring by tracking performance, metrics, and operational health.

How does Traceloop support monitoring and observability of AI models?

Traceloop supports monitoring and observability of AI models by tracking performance, logging events, and providing detailed analytics.

Alternatives

  • Step up

    Datadog LLM Observability for enterprise-grade unified monitoring across the full technology stack.

  • Lighter alternative

    OpenLLMetry (self-hosted, Apache 2.0) for teams that want pure OSS tracing wired into an existing observability backend.

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Tags

#LLMOps#LLMObservability#ModelEvaluation

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