AgentOps vs Arize Phoenix
A side-by-side comparison of AgentOps and Arize Phoenix, two Observability tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | AgentOps | Arize Phoenix |
|---|---|---|
| Category | Observability | Observability |
| Pricing | FREEMIUM | FREEMIUM |
| License (differs) | Open core | Proprietary |
| Deployment | Hybrid | Hybrid |
| Platforms (differs) | Web, API | API, Web |
| Model support | Model-agnostic | Model-agnostic |
| Vendor (differs) | AgentOps | Arize AI |
The honest brief
AgentOps
Purpose-built for multi-step agents — session replay with time-travel debugging and per-run cost tracking, not just flat LLM-call logging.
- Open-source MIT SDK, two-line setup
- 400+ LLM and framework integrations
- Records every LLM call, tool use, decision
- Agent benchmarking and evaluation
- Free tier to start
- Python/TypeScript SDK-centric
- Full analytics rely on the hosted dashboard
- Younger than general-purpose APM tools
Arize Phoenix
Spins up inside a Jupyter notebook and is sharpest at RAG debugging — finding the bad chunk that poisoned retrieval.
- Source-available, runs locally
- Strong RAG/retrieval debugging
- OpenTelemetry-based tracing
- Notebook-friendly
- Less polished than hosted SaaS evals
- Production scale leans on Arize cloud
- Setup effort for full pipelines
- Smaller than LangSmith ecosystem