Arize Phoenix vs Traceloop
Arize Phoenix
arize.comTraceloop
traceloop.comSide by side
| Figure | Arize Phoenix | Traceloop |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | Yes |
| Web | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 3 |
| Spec score · Llm Monitoring | 8.1 (#12) | 8.1 (#14) |
| Spec score · AI Agent Observability Tools | 8.0 (#19) | 8.1 (#18) |
| Deployment | Phoenix can run locally or be self-hosted using Docker, Kubernetes, or a cloud environment. | Traceloop says it supports cloud, on-premises, air-gapped, and other restricted deployments, including Kubernetes environments. |
| Evaluations | Teams can score traces and spans with LLM-based evaluators, code checks, or human labels. | Traceloop describes built-in quality checks for faithfulness, relevance, and safety, and says users can train custom evaluators on their data. |
| Integrations | Documented integrations include frameworks and providers such as LlamaIndex, LangChain, DSPy, OpenAI, Bedrock, and Anthropic. | OpenLLMetry supports providers including OpenAI, Anthropic, Bedrock, Gemini, Cohere, Mistral, Ollama and Vertex AI. |
| Support | The Phoenix documentation points users to a Slack community for questions and developer discussion. | The Enterprise plan includes dedicated Slack support, according to the pricing page. |
| Tracing | It captures model calls, retrieval, tool use, and custom logic in traces that can be sent over OpenTelemetry. | One-line instrumentation provides visibility into prompts, responses, latency and request execution traces. |
Plans
In their own words
Arize Phoenix
Arize Phoenix is free, open-source software for tracing, evaluating, debugging, and improving AI applications. It captures model calls, retrieval, tool use, and custom logic as OpenTelemetry traces. Teams can score traces and spans with LLM evaluators, code checks, or human labels. Prompt tools provide versioning, side-by-side testing, and call replay, while dataset experiments compare evaluation results across application versions. Documented integrations include LlamaIndex, LangChain, DSPy, OpenAI, Bedrock, and Anthropic, plus evaluation libraries such as Harbor, Ragas, Cleanlab, and MLflow. Phoenix runs locally or can be self-hosted with Docker, Kubernetes, or a cloud environment; Arize says self-hosting keeps traces in the user’s environment. Self-hosted authentication supports OAuth2 identity providers with an OpenID Connect well-known endpoint. The Phoenix plan costs nothing and leaves trace spans, ingestion, projects, and retention under user management. The project is ELv2 licensed and built by Arize AI and the open-source community.