Arize Phoenix

Arize Phoenix is an observability and evaluation workspace for AI applications, covering model, agent, retrieval, and tool behavior. It suits engineering teams that need to inspect and improve complex pipelines while choosing where their data runs. The main trade-off is control: Phoenix is free, but teams operate their own usage, storage, and retention boundaries.

Overview

Phoenix follows an application beyond its final answer. It records model calls, retrieval steps, tool use, and custom logic as traces that can be sent through OpenTelemetry, giving developers a workflow-level view of failures and regressions. Those traces can then be scored, replayed, and compared rather than treated as isolated outputs.

Its documented ecosystem covers LlamaIndex, LangChain, DSPy, OpenAI, Bedrock, and Anthropic. Evaluation integrations include Harbor, Ragas, Cleanlab, and MLflow, which helps teams keep existing instrumentation and assessment practices. Arize AI and the open-source community build the project, whose page describes an ELv2 license.

Key features

Tracing and scoring

Phoenix supports LLM, agent, and retrieval tracing, including cost and token-cost tracking. Teams can score traces and individual spans with LLM-based evaluators, code checks, or human labels. That combination is useful when automated grading needs to be checked against human judgment, though the team must define and maintain those evaluators.

Prompts and experiments

Prompt versioning, side-by-side testing, and call replay expose how a prompt revision changes outputs. Dataset experiments go further: teams can rerun the same dataset through different application versions and compare evaluation results. This makes Phoenix a practical choice for iterative quality work, not just incident inspection.

Deployment and control

Run Phoenix locally, or self-host it with Docker, Kubernetes, or a cloud environment. Self-hosting keeps traces in the user’s environment and supports authentication, including OAuth2 identity providers with an OpenID Connect well-known configuration endpoint. That control fits organizations with data-governance requirements, but it also puts deployment and operations on the customer.

Pricing

Phoenix: free. The plan is free and open source, supports self-hosting, and leaves trace spans, ingestion, projects, and retention under user management. There are no published seat, ingestion, project, or retention quotas in this plan; the practical limit is whatever the team’s infrastructure and policies can sustain. Choose it when you want full control and can run the service yourself. Look elsewhere if you want a provider-managed service with fixed capacity and retention terms.

Platforms

Phoenix is offered through an API and web interface, with local and self-hosted deployment options. Docker, Kubernetes, and cloud-environment deployment cover different operational preferences, while OpenTelemetry provides the trace-ingestion path.

Who it's for

Phoenix is a strong fit for AI application developers, platform teams, and evaluators working across multiple model providers or agent frameworks. It is especially suitable where trace data must remain in a controlled environment and where prompt changes need repeatable dataset comparisons. Small teams gain a broad free workflow, but should be prepared to handle infrastructure, authentication, ingestion, and retention themselves.

Pros and cons

Pros

• End-to-end visibility: model calls, retrieval, tools, and custom logic appear in one trace workflow.
• Flexible evaluation: LLM judges, code checks, and human labels can be combined.
• Useful iteration loop: prompt replay, versioning, and dataset reruns connect changes to measured results.
• Deployment control: local and self-hosted options keep traces in the team’s environment.
• Broad integrations: support for major frameworks, providers, and evaluation libraries reduces the need to replace existing tools.

Cons

• Operational responsibility: self-hosting means the team manages deployment, capacity, authentication, and retention.
• User-managed limits: free usage has no provider-set quotas, so infrastructure costs and data growth remain the team’s concern.
• License consideration: the project is described as ELv2 licensed, which organizations should review against their distribution and commercial policies.

Alternatives

For a wider category comparison, see AI LLM Observability Tools, LLM Observability Tools, Model Monitoring Software, Machine Learning Model Monitoring Software, AI LLM Evaluation Tools, and AI Agent Observability Tools.

• OpenLIT is the better fit for unlimited self-hosted usage, users, projects, and environments at no charge.
• W&B Weave suits teams wanting a freemium cloud plan with 1 GB per month of ingestion, 5 GB of storage, and built-in evaluations, tracing, and scorers.
• LangWatch is an alternative for teams seeking an Apache 2.0 open-source option with a free Developer tier.
• Opik fits teams that want open-source observability and evaluation software that can run locally, alongside a free cloud option.
• Agenta is aimed at teams comfortable with explicit free-tier limits such as two members, 5,000 agent runs per month, and one-week trace retention.
• Arize AX is preferable when a managed SaaS free tier with 25,000 spans per month, 1 GB ingestion, and 15-day retention is the priority.
• Braintrust offers a free starter allowance of 1 GB processed data, 10,000 scores, and 14-day retention.
• Confident AI is another evaluation-focused option with a free plan capped at two seats, one project, five weekly test runs, and 1 GB-month of trace spans.

Verdict

Choose Phoenix if your team wants one free, adaptable system for tracing, evaluating, debugging, prompt iteration, and dataset experiments, with deployment and trace data under your control. Its breadth and framework integrations are the main reasons to pick it. Look elsewhere when you prefer a managed service with provider-defined quotas, retention, and operational support instead of running those responsibilities yourself.