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Yes. Langfuse exposes its observability APIs through two free paths: the hosted Cloud Hobby tier, documented at 50,000 units per month for two users (Langfuse clarification, September 4, 2026), and the MIT-licensed self-hosted edition, which includes the core platform and APIs with unlimited usage. Langfuse can capture prompts, input and output usage, generation and embedding costs, traces, evaluations, and experiments. The important qualification is that cost accuracy depends on usage data and model pricing definitions. If a provider does not send hidden reasoning-token counts, Langfuse cannot reconstruct the true bill from the visible prompt and answer alone.
Does Langfuse have a free API?
Yes. You can use Langfuse’s public APIs on the free Cloud Hobby plan or run the open-source edition on your own infrastructure without a software license fee. Those choices are materially different: Cloud Hobby has a documented monthly allowance, while self-hosting is described as unlimited but shifts deployment, operations, and infrastructure responsibility to you.
| Option | Price and allowance | What is included | Deployment |
|---|---|---|---|
| Cloud Hobby | Free; 50,000 units per month for 2 users, according to the September 4, 2026 clarification | Langfuse cloud access and its observability APIs | Use the hosted Langfuse service |
| Open Source self-hosted | Free under the MIT license; unlimited units and usage are listed for the open-source edition | All core platform features and APIs | Run locally with Docker Compose or deploy with Kubernetes templates |
| Self-Hosted Enterprise | Paid; billing can be handled through AWS Marketplace or invoice | Management APIs, project-level RBAC, retention policies, audit logs, and dedicated support | Operate on your infrastructure with the enterprise package |
The 50,000-unit figure is a dated packaging detail, not a permanent promise. Check the current Cloud plan before designing a production workload around it. “Free” for self-hosting means no license charge for the open-source edition; compute, storage, backups, networking, and operational labor remain your responsibility.
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Langfuse is an open-source AI engineering platform that combines tracing, prompt management, evaluation, and experiments. It sits around an application’s model calls so a team can inspect what happened for each request, compare behavior across models or use cases, and monitor usage and spend over time.
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A trace can include the prompt sent to a model, the generated response, input and output usage, metadata such as a user or application tag, and the resulting cost when that information is available. The same platform also covers embedding observations, so it is useful for retrieval pipelines as well as chat or completion workflows.
How does Langfuse track tokens, prompts, and cost?
Langfuse records usage and cost on LLM generation and embedding observations. It can receive those values directly from a provider response, an SDK, or an integration; when they are missing, it can infer them by matching the generation’s model name to a model definition.
Prompt and response data
Tracing captures the inputs and outputs associated with a model call, allowing engineers to inspect the exact prompt, variables, response, and surrounding spans in an application workflow. This makes it possible to connect a quality issue or latency spike to a particular prompt version or model invocation instead of looking only at aggregate logs.
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Usage fields
Generation and embedding observations can carry input and output units. Depending on the provider, “units” may represent tokens or another usage measure exposed by that API. Langfuse stores the values it receives rather than assuming every provider reports usage in exactly the same way.
Cost fields
Cost details are represented in U.S. dollars for generation and embedding observations. A provider-supplied cost or usage value is used when available. Otherwise, Langfuse applies the prices and usage types in the model definition that matches the recorded model name.
Automatic ingestion through integrations
Integrations and wrappers for OpenAI, LangChain, LlamaIndex, and LiteLLM can provide usage automatically. Automatic capture reduces manual instrumentation, but you should still verify that the integration passes every usage field your billing model requires.
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How does Langfuse calculate LLM cost?
Cost calculation combines observed usage with a model’s configured per-usage-type prices. For a straightforward model, that usually means multiplying input units by the input rate and output units by the output rate, then storing the resulting dollar amount on the observation. Embedding models use their own usage and pricing definitions.
When a provider supplies a cost directly, Langfuse can ingest that value. When it supplies usage but not cost, the matching model definition provides the rates. If the model name is not matched or its prices are outdated, the calculated total can be incomplete or wrong even though tracing itself works.
Where to review aggregate spend
Langfuse provides dashboards and threshold alerts for monitoring spend. Its Metrics API returns aggregated usage and cost and can filter results by application type, user, or tags. Those dimensions let a team separate, for example, a retrieval workload from a customer-facing assistant or identify which user group is consuming the most model budget.
How to define custom prices
You can create or adjust model definitions in the Langfuse interface or through the Models API. Definitions support custom prices, tokenizers, and pricing tiers, so an internal deployment or a provider with nonstandard billing can be represented explicitly instead of being forced into a generic model entry.
Why can reasoning-model costs be inaccurate?
Reasoning models can consume hidden reasoning tokens that are not visible in the ordinary prompt and answer fields. Langfuse’s documentation specifically warns that models such as OpenAI o1 require token usage to be ingested; without those hidden-token counts, Langfuse cannot infer the correct cost.
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This is a data-availability problem, not a tracing failure. A trace may still show the visible input, output, and metadata while the cost remains understated because the provider did not expose the reasoning usage. Before trusting totals for a reasoning model, confirm that the SDK or integration passes the provider’s complete usage object, including any reasoning-token field.
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Which Langfuse APIs are available?
The Metrics API is designed for reporting. It retrieves aggregated usage and cost and supports filters such as application type, user, and tags, which makes it suitable for budget dashboards, periodic reports, and threshold monitoring.
The Models API manages the definitions used for inference. The documented public routes are:
GET /api/public/modelsto list model definitions.POST /api/public/modelsto create a definition.GET /api/public/models/{id}to retrieve one definition.DELETE /api/public/models/{id}to remove a definition.
Model definitions can contain prices for different usage types, tokenizers, and pricing tiers. That is useful when a provider changes rates, when an organization has negotiated pricing, or when a self-hosted model has no public price list. Keep the model name emitted by your application aligned with the definition name; otherwise the intended rates may not be selected.
Should you use Langfuse Cloud Hobby or self-host Langfuse?
Choose Cloud Hobby when you want the shortest path to tracing and your workload fits the documented 50,000-unit monthly allowance and two-user limit. Choose self-hosting when unlimited usage, infrastructure control, or data residency is more important than avoiding operational work.
Cloud Hobby is the simpler starting point
The hosted option removes the need to deploy Langfuse yourself. You can begin instrumenting an application and use the APIs without first operating a Docker or Kubernetes environment. The trade-off is the published allowance and the fact that the plan’s packaging can change; the September 4, 2026 clarification is the source for the 50,000-unit, two-user figure.
Self-hosting provides control and unlimited usage
The open-source edition is MIT-licensed, includes the core platform features and APIs, and is listed as unlimited for units and usage. Langfuse says you can run it locally with Docker Compose or deploy production instances using Kubernetes templates. Data remains in your own infrastructure, which gives you control over data residency and the surrounding network and storage policies.
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That control comes with responsibility for running the service, keeping its dependencies available, sizing storage, applying updates, and monitoring reliability. The free license does not make those infrastructure costs disappear.
Enterprise features are a separate tier
Self-Hosted Enterprise adds management APIs, project-level role-based access control, data-retention policies, audit logs, and dedicated support. Those features are not part of the free open-source package described on the self-hosted pricing page. Enterprise procurement can use AWS Marketplace or an invoice, which may fit organizations that require a formal purchasing route.
How do you plan a self-hosted Langfuse deployment?
You can make the deployment decision without committing to a particular infrastructure vendor by following this sequence:
- Estimate observability volume. Count generation and embedding observations and compare the expected volume with Cloud Hobby’s 50,000-unit monthly allowance. If the allowance is likely to be exceeded or is difficult to predict, evaluate the unlimited self-hosted edition.
- Choose the operating mode. Use Docker Compose for a local or small evaluation environment. Use the documented Kubernetes templates when you need a production-oriented deployment pattern.
- Decide where data should reside. Self-hosting keeps Langfuse data in your infrastructure. Record the storage location, access boundaries, and retention requirements that apply to your prompts and outputs.
- Set the model catalog before measuring spend. Review the model names your application emits and create or update definitions with the correct tokenizers, prices, and pricing tiers through the UI or Models API.
- Validate ingestion with a known call. Send a test generation and an embedding request, then verify that the trace contains input and output usage and a dollar cost. For reasoning models, confirm that hidden reasoning-token usage is present before accepting the total.
- Choose governance features deliberately. If you need project-level RBAC, retention policies, audit logs, management APIs, or dedicated support, budget for Self-Hosted Enterprise rather than assuming the MIT-licensed package includes them.
What are the most common tracking mistakes?
- Confusing units with dollars. The Cloud allowance is expressed in units, while cost observations are stored in U.S. dollars. Track both so a high-volume, low-cost workload is not confused with a low-volume, expensive model.
- Leaving model names unmatched. Inference depends on matching the recorded model name to a model definition. A spelling or version mismatch can produce missing or incorrect cost values.
- Using stale prices. Provider rates and tiers can change. Review custom definitions whenever your provider changes pricing or your contract uses negotiated rates.
- Ignoring embeddings. Retrieval systems generate embedding observations as well as chat generations. Excluding embedding usage can make an application’s total spend look lower than the provider invoice.
- Assuming visible tokens are complete. Reasoning models may hide billable tokens. Require complete provider usage ingestion before using those totals for chargeback or budget decisions.
- Expecting the free license to provide enterprise controls. The open-source edition includes core features and APIs, but project-level RBAC, retention policies, audit logs, management APIs, and dedicated support belong to the enterprise offering.
- Designing around an undated quota. The 50,000-unit, two-user Cloud Hobby limit is documented with a September 4, 2026 date. Recheck the current plan terms before treating it as a long-term capacity guarantee.
What does “free” mean for an AI application team?
For Cloud Hobby, free means access to a hosted tier with the documented allowance and user limit. For self-hosting, free refers to the MIT-licensed software and unlimited listed usage, not to the servers, storage, backups, network transfer, maintenance, or staff time needed to operate it.
The practical break-even point depends on your priorities. A small prototype may favor Cloud Hobby because setup is minimal. A high-volume service, a team with strict residency requirements, or an organization that already operates Kubernetes may prefer self-hosting even after accounting for infrastructure work.
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How should you verify a Langfuse cost report?
Start with one known request and compare the provider’s usage response with the corresponding Langfuse observation. Check input units, output units, embedding usage where applicable, the model name, and the selected price definition. Then use the Metrics API to aggregate by user, application type, or tag and compare the resulting period total with the provider invoice.
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If the totals diverge, investigate missing usage fields before changing prices. For reasoning models, first confirm that hidden-token usage was ingested; for ordinary models, inspect model-name matching and pricing tiers. This sequence separates instrumentation errors from a genuinely different billing rule.
Frequently Asked Questions
Can Langfuse track embeddings as well as text generations?
Yes. Embedding observations can carry usage and U.S.-dollar cost just like generation observations. You need an embedding model definition with the appropriate usage types and prices when the provider does not supply a cost directly.
Which integrations can populate usage automatically?
Langfuse documents wrappers and integrations for OpenAI, LangChain, LlamaIndex, and LiteLLM that can pass usage into observations. Their coverage still depends on what the underlying provider returns, so reasoning-token fields must be checked separately.
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Yes. Model definitions can be created or edited in the Langfuse interface or managed through the Models API. Updating the definition lets later cost calculations use new rates, tiers, or tokenization rules while your application continues emitting the same model name.
Does the free self-hosted edition include audit logs and project-level roles?
No. The free MIT-licensed package includes the core platform and APIs. Audit logs, project-level RBAC, retention policies, management APIs, and dedicated support are listed as Self-Hosted Enterprise additions.
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