Key features
- OTel tracing: span-level visibility for APAC LLM chains, RAG, and agent workflows
- Evaluation metrics: RAG relevance, hallucination, and custom APAC LLM-as-judge scoring
- Dataset curation: export APAC production traces as labeled evaluation examples
- Web UI: interactive span explorer for APAC LLM debugging without code changes
- CI/CD integration: APAC quality regression checks in deployment pipelines
- Open-source: self-hosted APAC deployment with cloud-managed option
Best for
- APAC AI engineering teams building RAG pipelines or agent workflows who need span-level LLM observability and automated quality evaluation — particularly teams debugging retrieval quality issues or measuring prompt improvement impact on APAC production output quality.
Limitations to know
- ! Primary focus on evaluation quality — APAC production alerting needs separate tools
- ! OTel instrumentation required — APAC teams must add Phoenix SDK to application code
- ! LLM-as-judge evaluation costs APAC LLM API calls for automated scoring
About Arize Phoenix
Arize Phoenix is an open-source LLM observability and evaluation platform — providing OpenTelemetry-based tracing for LLM applications and agent workflows with a web UI for span-level debugging, dataset curation, and automated evaluation. APAC AI engineering teams use Phoenix to understand why their LLM applications produce incorrect or inconsistent outputs and to measure quality improvements systematically.
Phoenix's tracing architecture captures every LLM call, retrieval, and tool invocation in an agent workflow as a hierarchical span tree — APAC developers can see the complete execution path of a RAG pipeline (query embedding → vector search → context retrieval → LLM generation) with inputs, outputs, latency, and token counts at each step. This span-level visibility makes diagnosing APAC retrieval failures and hallucination sources much faster than log-based debugging.
Phoenix's evaluation framework runs automated quality metrics over traced APAC LLM outputs — including relevance scoring for RAG retrieval quality, hallucination detection, toxicity, and custom LLM-as-judge evaluations for APAC domain-specific quality criteria. APAC teams can run Phoenix evaluations as part of CI/CD pipelines to catch quality regressions before APAC production deployment.
Phoenix's dataset curation tools allow APAC AI teams to export traced examples into evaluation datasets — when APAC users report incorrect outputs, teams can flag those traces, export them as labeled evaluation examples, and track whether subsequent APAC model or prompt improvements resolve the issue. This closes the APAC quality improvement loop between production monitoring and offline evaluation.
Beyond this tool
Where this category meets practice depth.
A tool only matters in context. Browse the service pillars that operationalise it, the industries where it ships, and the Asian markets where AIMenta runs adoption programs.
Other service pillars
By industry