Snapshot
Continue to About ↓- What it does
- Langfuse is an open-source AI engineering platform for tracing, evaluating, and monitoring LLM applications and AI agents.
More detail
- It records every model call, tool invocation, and retrieval step so engineering teams can debug behavior, track token cost and latency, version prompts, and run evaluations against datasets.
- It runs as a managed cloud service in the EU, US, or Japan, or self-hosted including air-gapped deployments.
- Best for
- Engineering teams building LLM applications and agents in production that need tracing, evaluation, prompt versioning, and cost visibility in one platform.
More detail
- Also a good fit for regulated or data-sensitive organizations that need self-hosted, VPC, or air-gapped deployment under an MIT license.
- Pricing
- Langfuse Cloud is billed as a monthly subscription with graduated usage pricing.
More detail
- Hobby is free with 50k units/month, 30 days data access, and 2 users
- Core is $29/month with 100k units included and unlimited users
- Pro is $199/month with the same 100k units plus 3 years data access and higher rate l
- Main limitation
- Teams looking for general application performance monitoring rather than LLM and agent-specific traces.
More detail
- Also a poor fit for teams without the appetite to operate self-hosted infrastructure (Docker Compose, Kubernetes/Helm, Terraform) if they require data control beyond the managed cloud.
- Record
- Living and monitored
- Reviewed
- Evidence
- Public web research
- Machine access
- Structured profile ↗
About
- Langfuse is an open-source AI engineering platform for tracing, evaluating, and monitoring LLM applications and AI agents.
- It records every model call, tool invocation, and retrieval step so engineering teams can debug behavior, track token cost and latency, version prompts, and run evaluations against datasets.
- It runs as a managed cloud service in the EU, US, or Japan, or self-hosted including air-gapped deployments.
What it helps with
- Non-deterministic AI and agent behavior is hard to debug without a structured record of every prompt, response, tool cal
- Output quality drifts are hard to catch before and after release without datasets, experiments, automated evaluators, an
- Prompts scattered across code and teams are hard to version, deploy, and roll back safely.
- Token spend and latency are opaque per user, session, model, or feature without centralized tracking.
- Regulated teams need retained execution history, audit logs, access controls, and PII redaction to support supervisory a
- Centralizing model provider credentials and per-user spend across many AI applications is difficult without a single gat
Capabilities
A practical view of what is available and where limitations apply.
| Offering | Available | Detail |
|---|---|---|
| AI observability and tracing | Yes | Hierarchical traces of LLM calls, tool invocations, retrieval and embedding steps, with sessions, users, agent graphs, and a timeline view, built on OpenTelemetry. |
| Prompt and skill management | Yes | Version control, staging and production labels, and one-click deploy and rollback of prompts separate from application code. |
| Evaluation | Yes | LLM-as-a-judge, code and heuristic evaluators, user feedback, and human annotation on production data or during experiments. |
| Datasets and experiments | Yes | Test sets, side-by-side experiment comparison, and baseline regression gates for release decisions. |
| Cost and latency dashboards with alerts | Yes | Dashboards for cost, latency, and quality, with alerts to Slack, GitHub Actions, or webhooks when thresholds are crossed. |
| AI Gateway | Limited | Listed as beta. Provides one endpoint for OpenAI and Anthropic calls with centralized credentials, per-user gateway keys, and automatic tracing; any gateway markup is undisclosed. |
| Self-hosting and air-gapped deployment | Yes | Docker Compose, Kubernetes (Helm), and Terraform for AWS, GCP, and Azure, plus fully air-gapped deployment; customer bears infrastructure cost. |
| Compliance certifications | Yes | SOC 2 Type II and ISO 27001 listed, HIPAA-ready region, GDPR, and FIPS 140-3 and Iron Bank-hardened images for government. |
| General application performance monitoring | No | Purpose-built for LLM and agent traces rather than traditional APM; it observes model and tool calls and does not sit in the inference path unless the optional AI Gateway is used. |
| Independent third-party review coverage | Unknown | No independent reviews were captured in the evidence pack; independent limitation themes should be treated as unknown pending further research. |
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Evidence
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- Verification
- Scribble researched these facts from the linked public sources.
- The owner has not manually confirmed every field.
- Last reviewed
- Primary source
- langfuse.com
Technical profile details
Record ID
fd9d3016-1172-42ae-9e8c-14abc6a7ec0dCanonical URLhttps://profiles.scribble.network/listing/langfuse-fd9d3016