LLM observability meets application-level visibility.

Best-in-Class LLM Evaluation, Zero Application Context

When your LLM latency spikes, where is the bottleneck?

• 40+ auto-instrumentations (source: github.com/Arize-ai/openinference, verified 2026-06-01) covering OpenAI, Anthropic, LangChain, LlamaIndex, and more. Phoenix delivers deep LLM-specific visibility.
• But LLM calls do not execute in a vacuum. They depend on API gateways, vector databases, embedding services, and the application underneath.
• When your RAG pipeline slows down, is it the model, the retrieval layer, the database feeding context, or a noisy neighbor? Phoenix cannot answer that because it only sees the LLM layer.

See Your LLM Calls in Full System Context

Topology as your primary investigation surface.

• 3D spatial topology: Your LLM services, the APIs that call them, the vector databases they query, and the relationships between all of them.
• When something breaks, you see it in context: which service is affected, what it depends on, where the problem propagates.
• No context switching between your LLM tracing tool and your application monitoring. One environment. Application-level visibility.

Architecture: How We Differ

Application-level correlation vs LLM-only tracing.

IAPM provides spatial observability across distributed tracing, metrics, logs, and LLM observability in a single spatial environment. When your LLM latency spikes, IAPM shows you whether the bottleneck is the model provider, your API gateway, the vector database, or an upstream dependency.

Phoenix shows you the LLM call. IAPM shows you the LLM call in the context of the entire request lifecycle and the application dependencies around it.

AI: Tessa vs Manual Investigation

Human on the loop.

Tessa accesses your codebase workspace, diagnoses from 3D topology, and makes the fix. You review, you own it. When your RAG pipeline degrades, Tessa does not just show you the LLM trace. She correlates the latency spike with the vector database query time, checks the application metrics, identifies the root cause, and proposes a code fix.

Phoenix gives you excellent LLM trace visibility to investigate manually. Tessa closes the gap between "I see the problem" and "the fix is ready for review."

Pricing: Application Observability Value

One platform vs LLM tool + APM tool + ops cost.

Where Phoenix Excels

Honest comparison: if LLM quality loops are your primary job, Phoenix earns its place.

• Best-in-class evaluation framework: hallucination, toxicity, relevance, and custom evals are Phoenix's core strength. A team whose main job is iterating on LLM output quality gets real depth here.
• 40+ OpenInference auto-instrumentations (source: github.com/Arize-ai/openinference, verified 2026-06-01) make it fast to capture rich LLM spans across OpenAI, Anthropic, LangChain, LlamaIndex, and more.
• Alyx (Arize AX) for prompt optimization (source: arize.com/blog/alyx-2-0, verified 2026-06-01) automates prompt-variant experiments, so prompt-tuning workflows stay inside the platform.

If your primary need is tightening the LLM prompt quality loop, you may legitimately prefer Phoenix's depth there. That is not a contradiction with spatial observability: IAPM and Phoenix can be complementary. Keep Phoenix for evals, add IAPM for full application context.

Already Using Phoenix? Add Application Context.

OpenTelemetry makes it simple.

• Keep Phoenix for evals: If you love Phoenix's evaluation framework, keep using it. IAPM adds the application monitoring layer Phoenix does not cover.
• Zero re-instrumentation: Phoenix is built on OpenTelemetry via OpenInference. Your existing OTel instrumentation works with both.
• Run both side by side: Compare the experience. When you see the value of correlating LLM performance with application health, you will understand why application-level context matters.
• Exit guarantee: If IAPM is not right for you, change one endpoint URL. Your instrumentation stays exactly the same.