Open-source LLM tracing with application-level context.
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Langfuse is a strong open-source LLM observability platform. Good prompt management, cost tracking, evaluation features, and a growing community. We respect the open-source commitment. This page is an honest look at where IAPM takes a different approach, where Langfuse excels, and how you can evaluate both.
Open standards. Application-level context. AI-powered diagnosis.
Open-Source LLM Tracing Without the Full Picture
Great prompt tracking. No application-level correlation.
- Genuinely open source (MIT license) with a solid self-hosting story. Prompt management, cost tracking, and evaluation are well-designed for LLM-specific workflows.
- But LLM traces without application context tell an incomplete story. When your generative AI feature slows down, is it the model provider, the retrieval pipeline, or the application running your embedding service?
- Langfuse sees the LLM calls. It does not see the application underneath, the service topology around them, or the cross-signal correlations that reveal root cause.
Same open philosophy. Application-level scope.
Open Philosophy, Application-Level Scope
OpenTelemetry-native. Application monitoring included. AI diagnosis built in.
- Built exclusively on OpenTelemetry with no proprietary agents. IAPM shares Langfuse's commitment to open standards.
- 3D spatial topology: See your LLM services alongside the APIs, databases, and application they depend on.
- When something breaks, Tessa diagnoses across all layers and proposes the fix. Application-level correlation that Langfuse does not provide.
Managed application observability. Zero self-hosting burden.
Architecture: How We Differ
Managed application observability vs self-hosted LLM tracing.
Langfuse gives you open-source LLM tracing that you host and operate. IAPM gives you managed application observability with dependency correlation that reveals whether your LLM issue is actually an application dependency issue.
For teams that want full control over their LLM tracing data, Langfuse's self-hosted option is a real differentiator. For teams that want application-level correlation without the operational burden, IAPM delivers.
| Aspect | IAPM | Langfuse |
|---|---|---|
| Scope | Application monitoring (APM) + LLM observability | LLM observability only |
| Open Source | OpenTelemetry-native (open standard instrumentation) | Open-source platform (MIT license) |
| Deployment | Managed SaaS (zero operational burden) | Self-hosted or Langfuse Cloud |
| Visualization | 3D spatial topology + web dashboards | Trace views, prompt dashboards |
| Service Topology | Auto-discovered 3D service map | No service topology |
| Application Metrics | Application metrics via OTel correlated with traces | No application-level metrics |
| Prompt Management | Via codebase workspace (Tessa) | Prompt versioning, management UI |
| Cost Tracking | Token and cost telemetry via OTel | Built-in LLM cost tracking |
| Cross-Signal Correlation | Traces + metrics + logs unified in spatial view | LLM traces only |
Tessa fixes code. You review it. You own it.
| Capability | Tessa (IAPM) | Langfuse |
|---|---|---|
| AI Diagnosis | Cross-signal anomaly detection with spatial context | No AI diagnosis |
| Codebase Access | Full workspace: read, search, rename, modify | No codebase access |
| Code Fixes | Fixes code in your workspace. You review, you own it. | No code changes |
| Root Cause Analysis | Application-level: LLM + app + dependencies | LLM trace-level only |
| Accountability Model | Human on the loop | Manual debugging by engineer |
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 LLM costs spike unexpectedly, Tessa does not just show you the token counts. She correlates the cost increase with traffic patterns, identifies which service is generating excessive LLM calls, traces it to the code path, and proposes the fix.
Langfuse gives you cost tracking dashboards to investigate manually. Tessa closes the gap between "I see the cost spike" and "the fix is ready for review."
One platform for LLM + application monitoring. One price. Zero ops.
Pricing: Managed Application Observability vs Self-Hosted LLM-Only
The true cost of self-hosting.
- One platform, not three: IAPM includes LLM observability, APM, and AI diagnosis. Langfuse covers the LLM layer only.
- No operational burden: IAPM is a managed service. No infrastructure to provision, no upgrades to manage, no "monitoring the monitor."
- Predictable pricing: Nodes x tier price = monthly cost. No per-event or per-observation charges.
- AI included: Tessa is included in every paid tier. No separate AI add-on to budget for.
| Capability | IAPM | Langfuse |
|---|---|---|
| LLM Observability | Included | Self-hosted free / Cloud: Hobby free, Core $29/mo, Pro $199/mo |
| Application Monitoring (APM) | Included | Not available (requires separate tool) |
| APM / Distributed Tracing | Included | Not available (requires separate tool) |
| AI Assistant | Included (Tessa) | Not available |
| Operational Burden | Zero (managed SaaS) | Self-hosted: upgrades, scaling, backups, monitoring the monitor |
| Application Observability Total | $45/node/month (Analyze) | Langfuse + APM tool + ops cost |
IAPM pricing from immersivefusion.com/pricing. Langfuse pricing from langfuse.com/pricing. Verify current pricing before purchase. All prices USD.
OpenTelemetry bridges both worlds
Already Using Langfuse? Add Application Context.
Keep Langfuse for prompts. Add IAPM for application-level depth.
- Keep Langfuse for prompts: If you rely on Langfuse's prompt management, keep it running. IAPM adds the application observability layer.
- Eliminate self-hosting burden: Move your observability to a managed service. Focus your team on building, not operating monitoring infrastructure.
- Dual-destination: Langfuse supports OTel trace ingestion. Your OTel Collector fans out telemetry to both IAPM and Langfuse simultaneously.
- 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/langfuse:
endpoint: "your-langfuse-instance:4317"
service:
pipelines:
traces:
exporters: [otlp/iapm, otlp/langfuse]
metrics:
exporters: [otlp/iapm]Standard OTel Collector config. LLM traces go to both. Application metrics go to IAPM.
Ready for LLM Tracing with Application Context?
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