BurnLens vs Langfuse
Observability vs AI economics control · Updated May 2026
TL;DR
Langfuse is an LLM observability platform: tracing, prompt versioning, evaluations, and cost reporting at the observability layer. BurnLens is an AI economics control plane: spend attribution, outcome economics and runtime controls at the infrastructure layer. Langfuse tells you what happened inside the application; BurnLens helps explain and control what the work cost. They are complements more than competitors.
Feature comparison
| BurnLens | Langfuse | |
|---|---|---|
| Sits in the request path | Yes — HTTP proxy | No — SDK observer |
| Hard-cap budgets (blocks upstream call) | Yes — HTTP 429 | No — reports and alerts only |
| Install method | pip install burnlens, one env var | SDK integration in every call site |
| Cost attribution | Per request via headers | Per trace via SDK metadata |
| Trace tree visualization | No | Yes — full nested trace |
| Prompt management | No | Yes — versioned prompts |
| LLM-as-judge evaluations | No | Yes |
| Local-first storage | Yes — SQLite | Requires Postgres + ClickHouse |
| Self-hosted complexity | One pip install | Docker Compose with 4 services |
When to pick BurnLens
You need to stop spend, not just measure it.Langfuse's cost analytics are comprehensive, but they observe — they do not enforce. If a customer's API key triggers a loop that burns $5,000 overnight, Langfuse will show you the spike the next morning. BurnLens returns 429 once recorded spend reaches the configured cap; simultaneous requests can overshoot, so it is a guardrail rather than an absolute ceiling.
You want zero code changes. Langfuse requires wrapping every LLM call with its SDK or using its OpenTelemetry instrumentation. BurnLens needs one environment variable; your existing SDK code is untouched.
You don't want to operate Postgres + ClickHouse. Langfuse self-hosting requires a real database stack. BurnLens runs on local SQLite; the optional cloud sync is a single Railway service.
When to pick Langfuse
You need application-level tracing.Multi-step agents, RAG pipelines, and tool-using workflows benefit from Langfuse's trace trees. BurnLens sees individual HTTP requests, not the parent-child structure of an agent step graph.
You need prompt and evaluation tooling. Versioned prompts, A/B tests, LLM-as-judge scoring, dataset management — Langfuse handles these. BurnLens does not.
Use them together
The two tools compose cleanly:
# 1. BurnLens enforces the budget
pip install burnlens
burnlens start
export OPENAI_BASE_URL=http://localhost:8420/proxy/openai/v1
# 2. Langfuse instruments the app
pip install langfuse
# wrap your LLM calls with @observe() — they route through BurnLens automaticallyEach LLM call passes through BurnLens (cost tracked + capped) and is observed by Langfuse (traced + evaluated). No coupling between the two tools; either can be removed without affecting the other.
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