Phoenix
Open-source observability and evaluation for AI agents
The leading open-source option for agent observability and evaluation. If data privacy and vendor independence are critical, Phoenix is hard to beat. Teams preferring a fully managed SaaS with less operational overhead may find Datadog or LangSmith more convenient.
- AI engineers debugging complex agent workflows with multiple steps
- Teams needing to evaluate and improve LLM output quality systematically
- Organizations requiring self-hosted observability to maintain data privacy
- Developers building vendor-agnostic AI systems wanting to avoid lock-in
- Teams wanting a fully managed SaaS with minimal setup and operational overhead
- Users needing advanced alerting and monitoring beyond trace visualization
- Projects requiring frequent updates or auto-scaling without Kubernetes expertise
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Skip Phoenix if you need a fully managed, no-code observability solution or if your team lacks the technical resources to self-host or manage Docker/Kubernetes deployments.
Span overage: Community capped at 1M spans/month, Team at 500K — exceeding requires custom pricing
Phoenix’s Community tier is $0/mo (open-source) offering up to 1M spans/month with self-hosting. The Team tier is also $0/mo for managed cloud but caps at 500K spans. Enterprise is custom. Compared to Datadog (starting at ~$15/host/month) or LangSmith (usage-based), Phoenix is significantly cheaper for smaller teams but may require more operational effort.
In short
Phoenix — Open-source observability and evaluation for AI agents. Best for AI engineers debugging complex agent workflows with multiple steps, Teams needing to evaluate and improve LLM output quality systematically, Organizations requiring self-hosted observability to maintain data privacy. Free to use.
Viability Score
How likely is Phoenix to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Trace visibility for agent steps (prompts, retrievals, tool calls, outputs)
- LLM-as-judge evaluation for relevance, toxicity, quality scoring
- Dataset creation from traces for reproducible testing
- Experiment management and regression benchmarking
- Built-in Prompt IDE for iterative prompt optimization
- Self-hosted deployment on local, Docker, Kubernetes
- Phoenix Cloud managed hosting option
- Vendor-agnostic support for any model/framework
- Native OpenTelemetry integration
- OpenInference specification for LLM telemetry
- Human annotation and automated labeling
- Ghost trajectories to simulate alternative agent paths
- Eval-as-you-test for early quality feedback
- ELv2 open-source license
- One-click integration with LlamaIndex
About Phoenix
Phoenix, by Arize AI, is an open-source platform purpose-built for AI agent observability and evaluation. It gives AI engineers full traceability into every agent step—prompts, retrievals, tool calls, and outputs—so you can debug and improve quality systematically. The platform includes LLM-as-judge evaluation for relevance, toxicity, and quality scoring, plus dataset creation from traces for reproducible testing. A built-in Prompt IDE enables iterative prompt optimization, while ghost trajectories let you simulate alternative agent paths. Phoenix supports any model or framework, integrates natively with OpenTelemetry, and offers self-hosted deployment (local, Docker, Kubernetes) or Phoenix Cloud. Unlike proprietary alternatives, it's vendor-agnostic and prioritizes data privacy, making it a strong choice for production AI systems demanding full control.
Behind the Verdict
Phoenix is a compelling choice for AI teams that need full visibility into complex agent workflows without sacrificing data privacy. Its open-source nature and vendor-agnostic design mean you can run it anywhere—on-prem, Kubernetes, or in the cloud—and it works with any model or framework. The trace-based observability is fine-grained, capturing every prompt, retrieval, and tool call, which is invaluable for debugging multi-step agents. The built-in LLM-as-judge evaluations and dataset creation from traces enable systematic quality improvement. However, this power comes with trade-offs: setting up self-hosted Phoenix requires some DevOps effort, and the tool doesn't yet offer advanced alerting or auto-scaling without Kubernetes. For teams wanting a turnkey SaaS with minimal setup, Datadog or LangSmith might be a better fit. In practice, we'd reach for Phoenix when we need maximum control over data and evaluation pipelines, especially in regulated environments. Where it bites is the operational overhead of self-hosting—small teams might find the learning curve steep. Compared to LangSmith, Phoenix is more open and flexible, but LangSmith offers tighter integration with LangChain and a more polished SaaS experience. Ultimately, Phoenix is the strongest open-source option for agent observability if you're willing to invest in deployment.
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Real-world workflow fit
Concrete scenarios for the personas Phoenix actually fits — and what changes day-one when you adopt it.
You suspect your chatbot agent is calling the wrong tool in production. You open Phoenix's trace view, inspect each step (prompt, tool call, output), and add a relevance evaluation to score the tool selection.
Outcome: Within minutes, you identify that the prompt was missing context. You fix the prompt in the Prompt IDE and run an experiment to validate the improvement before deploying.
You have a dataset of user queries and want to test whether a new system prompt reduces hallucination rates. You create a dataset from existing traces, run a batch experiment evaluating both prompts with LLM-as-judge, and compare metric scores.
Outcome: The experiment shows a 20% reduction in hallucination scores. You select the new prompt and deploy it with confidence, having reproducible results.
Use Cases
- Monitor live LLM responses for hallucinations and bias
- Compare prompt versions to optimize response quality
- Debug latency and token usage in production LLM pipelines
- Set up automated evaluations as part of CI/CD for AI features
- Trace end-to-end calls across LangChain, LlamaIndex, and custom chains
- Audit LLM outputs for compliance and safety
- Run A/B tests on prompt changes across model providers
Models Under the Hood
as of 2026-07-05
Limitations
- The free community version is limited to 1M spans per month and requires self-hosting.
- The managed cloud version caps at 500K spans, which may not suit high-volume production deployments without upgrading to a paid tier.
- Advanced features like custom dashboards and RBAC are behind the enterprise plan.
- The tool requires some technical setup (Python SDK, Docker for self-hosting), which may be a barrier for less technical teams.
- No built-in LLM guardrails beyond evaluation metrics—teams must define their own.
as of 2026-06-24
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 Phoenix tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Community
$0/mo
Ideal for
Individual developers or small teams experimenting with agent observability who are comfortable self-hosting and don't need managed cloud support.
What this tier adds
Free self-hosted tier with up to 1M spans per month and community support via Slack.
Team
$0/mo
Ideal for
Mid-size teams evaluating agent behavior in a managed cloud environment with up to 500K spans/month, who need shareable dashboards and email support.
What this tier adds
Free managed cloud hosting with 500K spans/month, shareable dashboards, and Slack+email support — no installation needed.
Enterprise
Custom
Ideal for
Large organizations deploying AI agents in production at scale, requiring unlimited spans, dedicated support, SSO, and on-premise deployment.
What this tier adds
Custom pricing with unlimited spans, dedicated SLA, SSO/SAML, RBAC, and custom integrations including on-premise deployment.
Where the pricing makes sense
The company stage and team size where Phoenix's pricing actually pencils out — and where peers do it cheaper.
Phoenix’s Community tier is $0/mo (open-source) offering up to 1M spans/month with self-hosting. The Team tier is also $0/mo for managed cloud but caps at 500K spans. Enterprise is custom. Compared to Datadog (starting at ~$15/host/month) or LangSmith (usage-based), Phoenix is significantly cheaper for smaller teams but may require more operational effort.
Setup time & first value
How long it actually takes to get something useful out of Phoenix — broken out by persona, not the marketing-page minute.
For a developer familiar with Docker: 15 minutes to spin up Phoenix locally, 30 minutes to integrate the OpenTelemetry SDK. For teams needing Kubernetes deployment: 1-2 hours to set up Helm chart. Managed Cloud (Team tier) can be operational in under 10 minutes via API key.
Switching to or from 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: export your traces via LangSmith API, then use Phoenix's import datasets utility to migrate evaluation data.
- →From Datadog: use the OpenTelemetry collector to send telemetry to both Datadog and Phoenix concurrently during a transition period.
- ↗To Datadog: export your trace data via Phoenix's Python SDK, then reformat for Datadog's trace ingestion endpoint.
- ↗To LangSmith: manually recreate datasets and evaluations, as Phoenix does not offer a direct export format for LangSmith.
Integrations
Resources & Guides
Tutorials & Learning
Official links
Tools that pair well with Phoenix
Common stack mates teams adopt alongside Phoenix, with the specific reason each pairing earns its keep.
Alternatives to Phoenix
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