Langfuse gives product teams a control room for LLM features. Collect traces with spans for prompts, tools, and model calls; attach user, version, and region tags; and analyze behavior over time. Create datasets from real traffic, run evals, and compare variants side by side. Costs, latency, and errors stay visible so you can tune prompts, choose models, and roll out improvements with guardrails and confidence. SDKs and permissive licenses make self-hosting and audits straightforward for modern stacks.
Instrument your app to capture inputs, outputs, errors, and timings for every step in a flow. Group calls by user, release, or tenant to spot regressions and hotspots quickly. Custom dimensions track business context like plan or market, and filters isolate trends without exporting raw logs. Hierarchies group spans under sessions so one view shows a user journey, and links connect related traces across microservices. Sampling policies preserve rare errors, and redaction rules mask fields before they reach storage.
Score outputs with rubrics or model judges and compare candidates on the same dataset. Route a slice of traffic to challengers and promote winners automatically when thresholds are met. Significance hints, confidence intervals, and dashboards reduce debates by grounding choices in shared evidence. Judges measure coherence, groundedness, and safety, while rubric graders track policy fit or brand voice across versions. Traffic splits test prompts or models on live routes, and guardrails halt losers when metrics slip.
Version prompts with diffs, attach notes, and roll back when performance dips to earlier, proven variants. Turn production samples into reproducible datasets for offline testing and CI checks so regressions get caught early. Templates separate variables from copy, and guard conditions prevent risky changes from bypassing review in critical paths. Datasets capture inputs and expected references for repeatable checks, and diffs reveal exactly what changed between revisions so blame and fixes are precise.
Track token usage, per-call cost, and latency percentiles across models, routes, and customers. Alerts trigger on anomalies so you respond before users notice. Budgets and caps protect spend during experiments, and cohort views reveal how regions or plans experience speed and quality. Dashboards surface p50, p95, and outlier paths, while feature and tenant breakdowns expose expensive routes ready for optimization. Exports feed BI tools so finance aligns with engineering on reality, not assumptions.
Redact sensitive fields at ingestion, scope access by role, and configure retention to meet compliance standards. SDKs and connectors ship telemetry to warehouses and notebooks for deeper analysis, enabling unified reporting. Self-hosting and SSO support enterprise requirements, and webhooks open tickets or send chat alerts when evals fail so owners respond promptly. Role scopes limit who can view raw content, and retention windows keep data only as long as needed across jurisdictions and policies.
Recommended for teams running LLM products in production who need visibility and proof. Langfuse ties prompts, metrics, and versions together so stakeholders trust changes. Engineers move faster because debugging, evaluation, and rollout control live in one place instead of scattered dashboards and scripts. Compliance and finance gain a single source of truth for cost and content risk, while engineering focuses on fixes backed by evidence that accelerate iteration and reduce uncertainty across releases.
Without observability, LLM work drifts on anecdotes and breaks under load. Langfuse centralizes traces, evals, and costs, turning trial-and-error into measurable progress. The result is fewer regressions, predictable spend, and a disciplined loop from idea to rollout where winners are obvious and reversible. When issues appear, traces link to exact prompts and contexts so fixes target causes, not symptoms, shortening root-cause analysis and reducing operational fire drills.
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