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Phoenix Arize

Arize Phoenix is an open-source LLM observability platform for tracing, evaluation, and prompt iteration.

Our take: Phoenix is Arize AI's open-source observability layer for generative AI and LLM applications — covering traces, evals, experimentation, and prompt management.

Best forML Engineers
ReviewedPricing: Freemium
8.1 out of 105 criteria · see breakdown
Try PhoenixFree (OSS / self-host); cloud from $50/mo
The bottom line

The go-to open-source LLM tracing and eval tool — free to self-host, affordable cloud tier for teams.

Our score

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

Community ratings

Shown for context only. Our score is independent.

G2
4.2 out of 5
20+ reviews

4.2 / 5 aggregate · across 1 source

Third-party ratings shown verbatim; aggregate weighted by review volume.

What it is, and why we rate it

One line each. Open a row for detail.

What it really isArize Phoenix — open-source LLM observability platform for tracing, evaluation, and prompt iteration.
Our takePhoenix is Arize AI's open-source observability layer for generative AI and LLM applications — covering traces, evals, experimentation, and prompt management.
Arize Phoenix is freemium, OSS-first, and focused on LLM and agent pipelines. The actual paid entry point is $50/mo (AX Pro) with a permanently free open-source self-hosted tier and a free cloud tier.
Why we rate itRare combination of a genuinely useful open-source core and a well-priced managed tier.
OTEL-native design means no lock-in, and the built-in eval harness lets teams close the loop between production traces and offline experiment runs.
The catchThe free AX cloud tier caps out at 25k spans/month — production LLM apps can hit that quickly, forcing an upgrade or self-hosting overhead.
Deep features (online evals, compliance) are enterprise-only.

Best / not good for

Best for
  • LLM and RAG application developers who need span-level tracing out of the box
  • Teams that want self-hosted AI observability with zero per-span cost
  • AI/ML engineers running offline evaluations and prompt experiments
Not good for
  • Traditional ML model monitoring (Arize's other AX product covers that better)
  • Non-technical teams needing a no-code observability dashboard
  • Enterprises requiring guaranteed SLAs or HIPAA compliance without a custom contract
Trade-offs

Friction report

Trade-offs to know before signing up.

  1. Time to value
    Fast: pip install and a one-line SDK call gives traces in minutes; cloud signup is instant.
  2. Scale breakpoint
    25k spans/mo free cloud ceiling is hit quickly by high-traffic LLM apps; self-hosting requires container infrastructure at scale.
  3. Walled garden
    Low: OpenTelemetry-native export, open-source core, and data portability mean minimal lock-in.
Still a fit?
If none of these block you, start on the free plan.
Try Phoenix

Frequently Asked Questions

How can Phoenix Arize help monitor and analyze AI model performance?

Phoenix Arize helps monitor and analyze AI model performance with real-time insights, metrics, and error tracking.

How can Phoenix Arize help monitor AI model performance?

Phoenix Arize helps monitor AI model performance by tracking metrics, detecting drift, and providing analytics dashboards.

How does Phoenix Arize support AI model monitoring and observability?

Phoenix Arize supports AI model monitoring and observability, providing metrics, alerts, and performance insights.

How does Phoenix Arize assist in AI monitoring and observability?

Phoenix Arize assists in AI monitoring and observability by tracking model performance, drift, and anomalies.

How does Phoenix Arize assist in monitoring and observability of AI models?

Phoenix Arize assists in monitoring and observability of AI models by providing error detection, performance metrics, and visualization tools.

Alternatives

  • Step up

    Arize AX Enterprise for SLA, SOC2/HIPAA compliance, and dedicated support at scale.

  • Lighter alternative

    Langfuse (open-source LLM observability) for teams wanting a simpler self-hosted alternative.

Showing 1–2 of 2

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Tags

#LLMOps#LLMObservability#ModelEvaluation

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