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 2026-10-10) 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.
• Phantoms: when a call goes out and nothing ever reports back from the other side, or a consumer goes quiet while messages keep arriving, DeepCube draws the missing side where it should be, so you see what is missing, not only what is there.

Architecture: How We Differ

Application-level correlation vs LLM-only tracing.

DeepCube provides spatial observability across distributed tracing, metrics, logs, and LLM observability in a single spatial environment. When your LLM latency spikes, DeepCube 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. DeepCube shows you the LLM call in the context of the entire request lifecycle and the application dependencies around it.

AI: Tessa vs Alyx, PXI and Signal

Both ship AI agents. The difference is what they can see.

Arize ships real AI agents: Alyx in AX, PXI in Phoenix (beta), and Signal, which proposes fixes on every plan; on Enterprise, Signal opens fix PRs against an attached GitHub repo. Working in your code is table stakes now, and Tessa does it too: it works in your connected codebase workspace and proposes the fix, and you review and own it. The difference is what the AI can see. When your RAG pipeline degrades, Tessa correlates the latency spike with the vector database query time and the application metrics around it, in one spatial view, because DeepCube carries the application telemetry Arize does not.

Phoenix and AX give you excellent LLM-layer visibility and agents that work inside it. DeepCube puts that LLM layer inside the whole application, and Tessa works across both.

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 2026-10-10) make it fast to capture rich LLM spans across OpenAI, Anthropic, LangChain, LlamaIndex, and more.
• Alyx (Arize AX), a built-in AI engineering agent (source, verified 2026-08-18) that investigates traces, authors evals and custom metrics, curates datasets and runs prompt experiments without leaving the platform, on every plan including Free. Phoenix has its own equivalent, PXI, in beta.

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: DeepCube and Phoenix can be complementary. Keep Phoenix for evals, add DeepCube 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. DeepCube 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 DeepCube is not right for you, change one endpoint URL. Your instrumentation stays exactly the same.