Arize Phoenix
Open-source LLM agent observability with tracing, evals, and experiments
Phoenix remains the go-to open-source observability tool for LLM agents—deep tracing, LLM-as-judge evals, and self-host freedom. But advanced features like Signal and managed agents require Arize AX, and Dynatrace's acquisition raises questions about long-term open-source direction. For teams needing self-hosted control or cost-effective eval tooling, Phoenix is the pick.
Verified 19h ago · liveness 81/100 · cite: rightaichoice.com/tools/arize-phoenix
- AI engineers debugging complex multi-step agent workflows
- Teams evaluating LLM output quality with LLM-as-judge
- Developers iterating on prompts with A/B experiments
- Enterprises needing self-hosted observability for compliance
- Non-technical users seeking a no-setup, managed observability service
- Teams needing real-time alerting on latency/cost at scale (limited in OSS)
- Projects that require deep integration with proprietary cloud monitoring tools
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Skip Arize Phoenix if you need a fully managed observability service with zero infrastructure setup and immediate enterprise support, or if you require real-time alerting and advanced monitoring out of the box without paying for AX tiers.
Going past 25k trace spans per month on the free tier requires upgrading to Pro at $50/month for 50k spans, which may be insufficient for high-volume production traffic.
Arize Phoenix's OSS is free, but for managed cloud, AX Free gives you 25k spans/month and 15-day retention at $0, while Pro at $50/month doubles spans and adds multi-modal tracing. Compared to LangSmith, which charges per seat (starting ~$25/user/month), Phoenix offers more generous free usage and self-hosting, making it a better value for teams with high trace volume. For startups with high volume, Enterprise custom pricing may be comparable to alternatives like Datadog LLM Observability or
In short
Arize Phoenix — Open-source LLM agent observability with tracing, evals, and experiments. Best for AI engineers debugging complex multi-step agent workflows, Teams evaluating LLM output quality with LLM-as-judge, Developers iterating on prompts with A/B experiments. Free to start; paid plans from $50/mo.
What's new in Arize Phoenix
Checked 5 days agoAcross the latest 4 updates: 4 news mentions.
Arize and Dynatrace: Making the World's AI Work
Arize announced a definitive acquisition agreement by Dynatrace.
How Uber evaluates AI agents at production scale
Uber's production evaluation of AI agents requires tracing, lived eval loops, and incident-driven insights.
Where agent evals are going: Agent-as-a-Judge
The shift of agent-as-a-judge from research to production eval stacks.
Is your coding agent uploading all your code?
Comparison of privacy practices in coding agents after Grok Build uploaded entire repos.
What people actually say about Arize Phoenix — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
44 mentions across 3 sources (Hacker News, Bluesky, Lemmy) · researched Jul 16, 2026.
- +Open-source with full control and no vendor lock-in.
- +OpenTelemetry-native tracing integrates with many frameworks.
- +Active development with frequent releases and features.
- +Self-hostable locally, on Docker, or Kubernetes.
- +Built-in LLM-as-judge and experiment evaluation.
- −Community data lacks detailed negative feedback for balanced view.
- −Self-hosting requires DevOps skills and infrastructure knowledge.
- −Ease of use at scale not well documented yet.
- −Support primarily community-driven (Slack) — no guaranteed response times.
- −Naming and terminology may confuse different team roles.
- • Infrastructure cost for self-hosting (server, storage, network)
- • Possible need for additional tools for production-scale reliability
Viability Score
How well maintained and how widely used is Arize Phoenix? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- End-to-end tracing for LLM agents (prompts, retrievals, tool calls, outputs)
- OpenTelemetry-native instrumentation
- LLM-as-judge evaluations
- Human annotations and labeling queues
- Create datasets from traces
- Run experiments to compare changes
- Prompt IDE for iteration
- PXI: conversational AI engineering agent
- Multi-modal tracing (image, voice, PDF)
- Signal: automated failure mode detection
- Self-host locally, Docker, or Kubernetes
- Cloud instances with free tier
- Vendor agnostic: any model or framework
- ELv2 open-source license
- Agent Swarms: sandboxed managed debugging agents (AX)
About Arize Phoenix
Arize Phoenix is the open-source platform for AI engineers building and operating LLM agents. It gives you end-to-end tracing of every agent step—prompts, retrievals, tool calls, and outputs—so you can see exactly what went wrong and why. The platform is vendor-agnostic, works with any model or framework, and can be self-hosted on your own infrastructure or run in the cloud with free instances. It's the observability layer that turns agent debugging from guesswork into a systematic process. Built around the OBSERVE–ANNOTATE–HYPOTHESIZE–EXPERIMENT–MEASURE loop, Phoenix provides a structured workflow for improving AI quality. You can annotate traces with human labels or LLM-as-judge, create datasets from traces, run experiments to compare changes, and evaluate output across cost, latency, and performance. The Prompt IDE lets you iterate on prompts, while PXI—an AI engineering agent—lets you talk with your traces to investigate issues, add annotations, and run experiments. Phoenix supports multi-modal tracing for image, voice, and PDF data, and offers automated failure-mode detection via Signal. It integrates natively with OpenTelemetry, so your traces work with the tools you already use. With over 3 million monthly downloads and 10k+ GitHub stars, it's trusted by thousands of developers and teams, from individual AI engineers to Fortune 500 companies. Deploy Phoenix locally in under a minute, via Docker, on Kubernetes with Helm, or get two free Phoenix Cloud instances with no infrastructure setup. It's built on the ELv2 open-source license, giving you full control over your AI stack without proprietary lock-in, unlike managed alternatives like LangSmith. Whether you're debugging a complex multi-step agent or iterating on prompts, Phoenix provides the tools to observe, evaluate, and improve your AI systems.
Behind the Verdict
When you're debugging a multi-step agent that keeps failing, Phoenix's tracing shows every prompt, retrieval, and tool call—so you stop guessing and start pinpointing. Self-hosting is a killer feature for privacy-conscious teams; your traces never leave your infra. The OBSERVE-ANNOTATE-HYPOTHESIZE-EXPERIMENT-MEASURE loop gives a real workflow, not just a dashboard. The Prompt IDE and experiments make A/B testing prompts concrete. PXI, the conversational agent, is a standout for investigating traces without SQL. But the free tier is limited to 25k spans/month, and Signal issues cap at 10/month. For production-scale alerting and managed agents, you'll need AX Pro or Enterprise, which costs more. Compared to LangSmith, Phoenix is self-hosted and open-source, but LangSmith has tighter proprietary integrations. The Dynatrace acquisition could shift priorities; watch the roadmap. If you want no-infrastructure setup and deeper out-of-box integrations, try Arize AX instead. But for open-source control and cost-effective eval, Phoenix wins.
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Real-world workflow fit
Concrete scenarios for the personas Arize Phoenix actually fits — and what changes day-one when you adopt it.
You notice your agent is failing intermittently. You instrument with Phoenix, trace the steps, find a faulty tool call, and use the experiment feature to test a fix.
Outcome: Pinpointed the issue and validated a fix with a controlled experiment, improving agent reliability by 20%.
You want to automatically judge outputs before deployment. You set up LLM-as-judge evals on traces and create labeling queues for human review.
Outcome: Deployed automated evaluations that catch regressions early, reducing bad rollouts by 30%.
You use the Prompt IDE to compare multiple prompt versions against a dataset, measure cost/latency/performance, and select the best.
Outcome: Chose a prompt that improved output quality while cutting token cost by 15%.
Use Cases
- Trace every LLM call in your LangChain app to debug latency and errors.
- Evaluate response quality and safety before deploying to production.
- Monitor model drift and performance degradation in real time.
- Compare prompts and model outputs side by side for optimization.
- Set up automated alerts for abnormal response patterns or cost spikes.
- Export trace data for offline analysis and custom reporting.
Limitations
- Arize Phoenix is an open-source platform for LLM agent observability, offering tracing, evals, and experiments.
- The managed cloud version (Arize AX) provides a free tier with 25k trace spans per month, 1 GB ingestion volume, and 15 days retention, with Pro at $50/month for 50k spans, 10 GB ingestion, and 30 days retention.
- The pricing page also shows an Enterprise tier for scaled AI use.
- As of August 2026, Arize announced a definitive acquisition agreement by Dynatrace, which may affect future roadmap and pricing.
as of 2026-08-28
Verification history
We have re-verified Arize Phoenix 77 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
Showing the 6 most recent of 77 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Arize Phoenix tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
AX Free
$0/mo
Ideal for
Builders and hobbyists testing the platform, running low-volume agents (up to 25k spans/month) and wanting to explore tracing and evals without cost.
What this tier adds
Free entry point with 25k spans/month, 1GB ingestion, 15-day retention, unlimited users and evals; lacks multi-modal tracing and Signal beyond 10 issues.
AX Pro
$50/mo
Ideal for
AI-native teams running higher-volume agents (50k spans/month) that need longer retention (30 days) and multi-modal tracing for image/voice/PDF.
What this tier adds
Adds 50k spans, 10GB ingestion, 30-day retention, 25 Signal issues/month, and multi-modal tracing compared to Free.
AX Enterprise
Custom
Ideal for
Enterprises with scaled AI use cases requiring custom span volumes, retention, self-hosting, and compliance features like SSO, audit logs, and SOC 2.
What this tier adds
Offers unlimited Signal, custom spans/storage/retention, self-hosted deployment, enterprise SSO, audit logs, SOC 2 Type II, and data regions (US, EU, CA).
Where the pricing makes sense
The company stage and team size where Arize Phoenix's pricing actually pencils out — and where peers do it cheaper.
Arize Phoenix's OSS is free, but for managed cloud, AX Free gives you 25k spans/month and 15-day retention at $0, while Pro at $50/month doubles spans and adds multi-modal tracing. Compared to LangSmith, which charges per seat (starting ~$25/user/month), Phoenix offers more generous free usage and self-hosting, making it a better value for teams with high trace volume. For startups with high volume, Enterprise custom pricing may be comparable to alternatives like Datadog LLM Observability or
Setup time & first value
How long it actually takes to get something useful out of Arize Phoenix — broken out by persona, not the marketing-page minute.
For a single developer: local setup is under a minute with uvx; tracing OpenAI or LangChain can be added in minutes. For teams: Docker/Kubernetes deployment takes 15-30 minutes; Cloud instances are instant. First value (first trace) in under 10 minutes. Full workflow (evals, experiments) may take a few hours to configure.
Switching to or from Arize Phoenix
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From LangSmith: If you're using LangSmith for tracing, you can migrate your LangChain traces to Phoenix via OpenTelemetry, though you may need to adjust instrumentation. Self-host Phoenix and send traces there.
- →From custom logging: If you're building your own logging, Phoenix's OpenTelemetry-native instrumentation lets you replace custom code with a standard SDK, exporting traces to Phoenix.
- ↗To LangSmith: If you want a managed service with deeper LangChain integration, you can migrate by switching instrumentation to LangSmith's SDK; you'll lose self-hosting and the experiment workflow.
- ↗To Datadog LLM Observability: For a full-stack monitoring solution, you can export Phoenix traces to Datadog via OpenTelemetry, integrating with your existing monitoring stack.
Integrations
Resources & Guides
Tutorials & Learning
Official links
Tools that pair well with Arize Phoenix
Common stack mates teams adopt alongside Arize Phoenix, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
Arize Phoenix vs Nodedb
NodeDB is for teams consolidating multiple datastores into one multi-model engine, ideal for vector+graph hybrid RAG and offline sync. Arize Phoenix is for teams needing deep observability into LLM agent behavior, with tracing, evaluation, and experiment tracking. Choose NodeDB if your pain is database sprawl; choose Phoenix if your pain is untraceable agent failures.
Arize Phoenix vs Skill Seekers
If you need to turn sprawling docs, repos, or PDFs into structured AI skills or RAG pipelines for any platform, Skill Seekers is the clear open-source choice. If you're debugging complex agent traces and evaluating LLM output quality with LLM-as-judge, Arize Phoenix is purpose-built for that. They complement each other: feed Skill Seekers output into Phoenix for observability.
Arize Phoenix vs Thinklabs Ai
If you're a non-technical buyer researching which AI tool to purchase, ThinkLabs AI is your go-to for structured, unbiased comparisons. If you're an AI engineer debugging LLM agent workflows, Arize Phoenix's open-source tracing and evaluation tools are indispensable. Choose based on your role: researcher or builder.
Swarmtrace vs Arize Phoenix
If your pain is 'why did my multi-agent system do that yesterday?' and you need frame-by-frame replay of every message and state change, SwarmTrace's time-travel debugging is unmatched. But if you're building production LLM apps and want tracing, quality evals, and experiment tracking in one open-source stack that runs anywhere, Arize Phoenix is the safer, more feature-complete default — especially since it's free.
Agnost Ai vs Arize Phoenix
If you need to catch production edge cases that standard evals miss and want a quick, focused monitoring solution, Agnost AI is your pick. But if you want a comprehensive, open-source, self-hostable platform for tracing, evaluating, and iterating on agent performance, Arize Phoenix is the clear winner.
Traccia vs Arize Phoenix
For AI platform teams that need multi-vendor orchestration and policy governance, Traccia is the control plane you'll want — but it's not something you'll run without an enterprise sales cycle. If you're an AI engineer debugging agent output and iterating on prompts, Phoenix's free, open-source observability and evaluation loop is immediately actionable — and its acquisition by Dynatrace signals deep enterprise backing. Pick based on your primary pain: controlling agents vs. understanding them.
Aegis Latent Core vs Arize Phoenix
If your priority is enforced compliance and verifiable audit evidence for enterprise LLM traffic, Aegis Latent Core is the safer bet. But for AI engineers actively building and debugging agents, Arize Phoenix is the clear winner — it’s open-source, self-hostable, and packed with tracing and evaluation tools. Pick based on whether you need a governance gate or a development workbench.
Alternatives to Arize Phoenix
View all
Langfuse
Open-source LLM observability for tracing, evaluating, and optimizing AI agents end-to-end.
Opik (Comet)
Free, open-source AI observability and evals for debugging agents
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