Helicone vs Arize Phoenix
Detailed side-by-side comparison to help you choose the right tool
Helicone
🔴DeveloperAI observability
open-source LLM observability and gateway platform
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FreeArize Phoenix
🔴DeveloperBusiness Analytics
Open-source LLM observability and evaluation platform built on OpenTelemetry. Self-host for free with comprehensive tracing, experimentation, and quality assessment for AI applications.
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FreeFeature Comparison
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💡 Our Take
Choose Helicone for hosted SaaS observability with a free tier and proxy-based integration that works in 5 minutes. Choose Arize Phoenix if you want a fully open-source, OpenTelemetry-native tracing solution with strong RAG and embedding analysis features. Phoenix wins for ML practitioners doing deep retrieval debugging; Helicone wins for engineers needing cost dashboards out of the box.
Helicone - Pros & Cons
Pros
- ✓Open-source positioning and one-line integration reduce adoption friction
- ✓Pricing is more transparent than many LLM observability tools
- ✓Combines observability and gateway controls in one product
Cons
- ✗Hobby plan limits retention to 7 days and ingestion to 10 logs/min
- ✗Advanced compliance and Slack support start at the $799/month Team tier
- ✗Teams needing deep eval workflows may still compare against Braintrust or Langfuse
Arize Phoenix - Pros & Cons
Pros
- ✓Fully open source and free to self-host, with no seat-based pricing, trace volume caps, or feature gating — a major advantage over LangSmith and other commercial competitors.
- ✓Built on OpenTelemetry and OpenInference standards, so instrumentation is portable and traces can be exported to other OTel backends without vendor lock-in.
- ✓Broad framework coverage with auto-instrumentation for LangChain, LlamaIndex, CrewAI, Haystack, DSPy, OpenAI, Anthropic, Bedrock, LiteLLM, and more — minimal code changes required to start tracing.
- ✓Comprehensive built-in evaluators (hallucination, relevance, toxicity, QA correctness, RAG metrics) plus a flexible framework for writing custom LLM-as-a-judge evals.
- ✓Backed by Arize AI, a well-resourced company with a commercial enterprise product, giving the open-source project sustained engineering investment and frequent releases.
- ✓Strong support for RAG debugging and agent tracing, including embedding visualization, UMAP clustering, and step-by-step inspection of tool calls and retrieval steps.
Cons
- ✗Self-hosting requires operational effort — running Postgres, managing storage growth from high-volume traces, and handling upgrades are non-trivial for small teams without DevOps capacity.
- ✗UI and workflows have a steeper learning curve than polished SaaS alternatives like LangSmith, especially for users new to OpenTelemetry concepts like spans and traces.
- ✗Rapid release cadence occasionally introduces breaking changes to SDKs, integrations, or UI, requiring teams to pin versions and test carefully before upgrading.
- ✗Documentation, while extensive, can lag behind the latest features, and some advanced workflows (custom evaluators, dataset versioning, annotation APIs) require reading source code or GitHub issues.
- ✗Enterprise features like SSO, RBAC, audit logging, and SLAs are reserved for the paid Arize AX platform rather than the open-source Phoenix core.
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