Your LLM calls don't live in isolation. Neither should your observability.
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LLM observability tools like Arize Phoenix, LangSmith, Langfuse, and Helicone are excellent at what they do: tracing LLM calls, tracking token usage, measuring costs, and evaluating output quality. But your LLM calls do not live in isolation. They depend on API gateways, databases feeding context, vector stores serving embeddings, and the services running all of it. IAPM correlates LLM telemetry with application health in a single 3D spatial view. This page is an honest look at where IAPM adds value and where these tools excel.
Most teams run two observability stacks for AI apps. One for LLM, one for everything else.
IAPM brings both into one spatial view.
The Blind Spot in LLM Observability
Your LLM calls depend on your application. Your LLM tools cannot see it.
- LLM-only tools see the LLM call, but not the API gateway, the vector store, the embedding service, or the database feeding context.
- When latency spikes, is it the model, the retrieval pipeline, or the database behind it? LLM-only tools cannot answer that question.
- Two observability stacks: most teams run a traditional APM tool for application health and an LLM tool for AI workloads. Two dashboards. No correlation between them.
One spatial view. LLM calls in the context of your entire system.
Application Monitoring + LLM in One Spatial View
See your LLM calls in the context of your entire application stack.
- 3D spatial topology: Your LLM services, the APIs that call them, the vector databases they query, and the relationships between all of them.
- When a RAG pipeline slows down, you see the entire chain: the API request, the vector store query, the LLM call, and the response, all in spatial context.
- Tessa understands both your LLM workloads and your application stack. She traces a slow completion back to a database connection pool exhaustion three services upstream, then fixes it.
LLM observability that sees the whole system
IAPM vs LLM Observability Tools
Application-level correlation is the gap.
Each LLM observability tool excels at the LLM layer. Arize Phoenix has the best evaluation framework with 40+ auto-instrumentations. LangSmith has deep LangChain integration. Langfuse offers open-source prompt management. Helicone provides simple proxy-based integration. None of them provide application monitoring, service topology, or cross-signal correlation with your full application stack. IAPM fills that gap.
| Capability | IAPM | Arize Phoenix | LangSmith | Langfuse | Helicone |
|---|---|---|---|---|---|
| LLM Tracing | Yes | Yes | Yes | Yes | Yes |
| Application Monitoring (APM) | Included (via OTel) | No | No | No | No |
| Service Topology / Maps | 3D Spatial | No | No | No | No |
| Cross-Signal Correlation | LLM + App + Traces | LLM only | LLM only | LLM only | LLM only |
| AI with Codebase Access | Tessa (bundled) | No | No | No | No |
| LLM Evaluation Framework | Basic | Best-in-class | Strong | Good | Basic |
| Prompt Management | Via codebase | Yes | Playground | Yes | Basic |
| OpenTelemetry Native | Exclusive | OTel support | Proprietary SDK | OTel support | Proxy-based |
Tessa fixes code. You review it. You own it.
| Scenario | LLM-Only Tool | IAPM |
|---|---|---|
| RAG latency spike | Shows LLM call took longer | Shows vector DB connection pool exhausted, causing queuing upstream of LLM call |
| LLM cost spike | Shows token count increased | Correlates cost with traffic patterns, identifies which service generates excessive calls |
| Degraded output quality | Evaluation scores flag the issue | Traces to retrieval pipeline returning stale context due to upstream dependency |
| Code fix | No codebase access | Tessa navigates to source, makes the fix. You review, you own it. |
Why Application Context Matters for LLM Apps
The bottleneck is rarely the model.
A typical RAG application involves an API gateway, an embedding service, a vector database, a retrieval layer, an LLM API call, post-processing, and a response. When latency spikes, LLM-only tools show you the LLM call took longer. But was it actually the LLM?
In IAPM's 3D topology, you see the entire pipeline spatially. Tessa can identify that the vector database connection pool is exhausted, causing queuing upstream of the LLM call. The LLM itself is fine. The bottleneck is three services away.
One platform for LLM and application observability
Choosing the Right Tool
Honest guidance on when to use what.
- If you only need LLM evaluation and prompt testing: Arize Phoenix or LangSmith may be the right fit. They have deeper LLM-specific evaluation capabilities than IAPM today.
- If you need LLM observability AND application monitoring: IAPM replaces two tools with one. Your LLM traces, application traces, and metrics in a single spatial view.
- If you need to correlate LLM performance with system health: Only IAPM connects LLM call latency to upstream application bottlenecks in 3D topology.
- If you need AI that fixes code: Tessa works across both LLM and application code. LLM tools have no codebase access.
| Capability | IAPM (Analyze) | LLM Tool + APM Tool |
|---|---|---|
| LLM Observability | Included | LLM tool (separate bill) |
| Application Monitoring (APM) | Included | APM tool (separate bill) |
| Service Topology | Included (3D spatial) | APM tool only (if available) |
| AI Assistant | Included (Tessa) | Not available in LLM tools |
| Cross-Signal Correlation | LLM + App unified | No correlation between tools |
| Estimated Monthly (40 nodes) | $1,575 | Multiple bills, no unified view |
IAPM pricing from immersivefusion.com/pricing. $45/node/month (Analyze tier). All prices USD.
Your OTel instrumentation works with IAPM out of the box
Already Using an LLM Tool? Add Application Context.
Keep your LLM tool for evaluation. Add IAPM for everything else.
- Dual-destination: Your OTel Collector fans out telemetry to both IAPM and your LLM tool simultaneously.
- No code changes: If you already use OTel SDKs, your instrumentation does not change.
- Complement or replace: Use IAPM alongside your LLM tool, or consolidate when ready. Your choice.
- Exit guarantee: If IAPM is not right for you, change one endpoint URL. Your instrumentation stays exactly the same.
Dual-Destination Collector Config
exporters:
otlp/iapm:
endpoint: "https://otlp.iapm.app"
headers:
API-Key: "YOUR-API-KEY"
otlp/llm-tool:
endpoint: "your-llm-tool:4317"
service:
pipelines:
traces:
exporters: [otlp/iapm, otlp/llm-tool]
metrics:
exporters: [otlp/iapm]Standard OTel Collector config. LLM traces go to both. Application metrics go to IAPM.
Detailed tool-by-tool comparisons
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