Langfuse
Langfuse is an open‑source LLM observability platform for building, monitoring, debugging, and cost‑tracking large‑language‑model applications. It captures full traces of prompts, model inputs/outputs, and metadata so teams can reproduce behavior, evaluate changes, and control spending.
It is aimed at engineering teams running LLMs in production or at scale, especially those using multiple model providers or custom models. Langfuse can be self‑hosted for privacy and control or consumed as a managed cloud service.
Use Cases
- Product engineering: Instrument requests and generations to debug failures, latency, and regressions across sessions and users.
- FinOps and cost control: Track token usage and attribute costs by request, model, workspace, or project to optimize model selection.
- Quality and PM: Inspect conversations, filter by cohorts, and analyze user journeys without digging through logs.
- Compliance and auditing: Maintain reproducible traces of prompts, outputs, and versions for investigations and reviews.
- Multi‑model fleets: Operate across OpenAI, Anthropic, Google, and custom/self‑hosted models without lock‑in.
- Prompt and model iteration: Version prompts, run evaluations on datasets, and compare models or templates before rollout.
Strengths
- End‑to‑end tracing: Capture requests, generations, and metadata to reproduce and debug production issues.
- Cost and usage visibility: Token‑level tracking and per‑model attribution to identify expensive routes and reduce bills.
- Broad provider support: Works with major providers and custom models for hybrid or on‑prem deployments.
- Flexible deployment: Self‑host on Docker Compose, Kubernetes (Helm), or cloud VMs, or use Langfuse Cloud.
- SDKs and API: Client SDKs (e.g., Python, JS) and REST APIs simplify instrumentation and ingestion.
- Session analytics: Aggregations, filters, and timelines to understand user flows and pinpoint regressions.
- Prompt management: Store, diff, and version prompts for controlled experiments and safe rollbacks.
- Evaluation and datasets: Attach datasets, run evaluations, and track metrics over time for A/B testing.
- Alerts and monitoring: Detect anomalies, error spikes, or cost surges and alert the team.
- Security options: RBAC and SSO available in enterprise tiers for compliance‑focused environments.
- UI for collaboration: Non‑engineers can explore traces and metrics via a web interface.
- Active project: Frequent releases and changelog updates (e.g., new model support such as O3/Pro).
Limitations
- Managed pricing: Team/enterprise tiers with SSO/RBAC and support start around $500/month, which may be high for small teams.
- Self‑hosting overhead: Production‑grade ops (Kubernetes, backups, upgrades, compliance) require SRE capacity.
- Feature gating: Some enterprise capabilities (advanced RBAC, SSO, compliance guarantees, SLAs) are paid.
- Focused scope: Optimized for LLM runtime observability, not full ML lifecycle (training orchestration, feature stores).
- Instrumentation effort: You must add SDKs and event capture to apps; shallow logging alone won’t unlock insights.
Final Thoughts
Langfuse provides focused, domain‑specific observability for LLM applications with strong tracing and cost attribution. Its open‑source core lowers barriers for teams that can self‑host, while the managed cloud offers convenience for those prioritizing speed.
Practical advice: start by instrumenting your highest‑traffic LLM routes, add cost tags per feature, version prompts from day one, and define a small set of health metrics with alerts (latency, failure rate, cost per request). Use datasets and evaluations before and after releases, and consider multi‑provider setups to benchmark cost and quality. If you need SSO/RBAC and enterprise guarantees, plan for the paid tier; otherwise, the self‑hosted core is a solid entry point.