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Yes—Langfuse offers a free API-accessible observability stack in two ways. Its Cloud Hobby tier is documented as free for 50,000 units per month and two users (a limit recorded on September 4, 2026). Its self-hosted edition is MIT-licensed, includes the core platform and APIs, and is described as supporting unlimited usage.
Langfuse records prompts, model responses, usage, and USD cost for generation and embedding calls. It can ingest usage from a provider response, SDK, or integration, or calculate it from a matching model definition. The important limitation is that reasoning-model costs are only accurate when hidden reasoning-token usage is supplied.
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What is the Langfuse free API?
Langfuse is an open-source AI engineering platform that combines tracing, prompt management, evaluation, and experiments. The API is the interface your application or an integration uses to send observations and retrieve analytics; it is not a separate, token-only product. Traces can connect a user request to the prompts, generations, embeddings, latency, usage, and cost produced while handling it.
There are two distinct free routes:
- Cloud Hobby: a hosted account with a documented allowance of 50,000 units per month for two users. The clarification carrying that figure is dated September 4, 2026, so treat it as a time-specific limit rather than a permanent promise.
- Open Source (self-hosted): the MIT-licensed edition includes all core platform features and APIs and is described by Langfuse as offering unlimited units and usage. You operate it on infrastructure you control.
“Free API” therefore means free software or a free hosted allowance, not necessarily zero total operating cost. Cloud usage is constrained by the Hobby allowance; self-hosting removes the Langfuse usage charge but leaves infrastructure, operations, and security work with you.
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What exactly does Langfuse record?
For generation and embedding observations, Langfuse stores usage details such as input and output units and cost details in US dollars. In a complete trace, those observations can be examined by model, application, user, tags, and time period rather than as an isolated request log.
Prompts and responses
Tracing captures the prompt and the resulting model observation so an engineer can inspect what the application actually sent and received. This makes prompt changes, regressions, and unexpected model behavior easier to connect to a specific application flow.
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Usage may arrive directly in an LLM response, through an SDK, or through an integration. When a provider does not return usable usage information, Langfuse can infer cost from a model definition matched to the generation’s model name. The resulting values are intended for monitoring spend across models, use cases, and time.
Embeddings as well as chat generations
Cost tracking is not limited to chat completions. Embedding observations can also carry usage and USD cost, allowing retrieval pipelines and generation pipelines to appear in the same operational picture.
How does Langfuse calculate LLM cost?
Langfuse uses supplied usage whenever possible and falls back to configured model pricing when it must infer a value. That distinction matters: a trace can exist even when an exact billable total cannot be reconstructed.
Provider- or integration-supplied values
If the provider response, SDK, or wrapper includes input and output usage, Langfuse ingests those values. Integrations and wrappers for OpenAI, LangChain, LlamaIndex, and LiteLLM can provide usage automatically, reducing the amount of instrumentation you have to write yourself.
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Model-definition inference
A model definition associates a model name with prices for the relevant usage types. Langfuse matches that definition to the generation and calculates an estimated USD amount from the recorded units. You can create or change definitions in the UI or through the Models API, including custom prices, tokenizers, and pricing tiers.
Why reasoning models need special handling
Reasoning models can consume hidden reasoning tokens that are not visible in the ordinary prompt and output fields. Langfuse’s documentation specifically warns that it cannot infer the correct cost for models such as OpenAI o1 when those reasoning-token counts are absent. For those calls, configure your integration or wrapper to ingest the provider’s complete usage data; otherwise, treat the calculated amount as incomplete rather than authoritative.
Which APIs are available for metrics and model pricing?
The public API exposes both analytics and configuration surfaces. The exact endpoints documented for model management are:
GET /api/public/modelsto retrieve 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.
The Metrics API returns aggregated usage and cost. Filters include application type, user, and tags, so a team can ask questions such as which product flow generated the most spend or how one user segment’s usage changed over time. Langfuse also documents dashboards and threshold alerts for ongoing monitoring; the API is useful for exporting or embedding the same aggregate view in internal reporting.
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Is Langfuse Cloud Hobby really free?
Yes, the Langfuse clarification page dated September 4, 2026 records Cloud Hobby as free at 50,000 units per month for two users. “Units” is the plan’s allowance; it should not be casually translated into a fixed number of prompts or tokens because different observations can consume different amounts of the allowance.
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The hosted option is attractive when you want to begin tracing without maintaining databases, upgrades, networking, or backups. Monitor the allowance as traffic grows and decide whether to reduce retention, instrument fewer workloads, move to a paid hosted tier if one is available, or deploy the open-source edition yourself. Because plan packaging can change, verify the current Cloud limits in Langfuse’s own pricing and clarification material before committing a production budget.
What does free self-hosted Langfuse include?
The self-hosted Open Source edition is MIT-licensed and includes the core platform features and APIs with unlimited usage as described on Langfuse’s self-hosted pricing page. It can be run locally with Docker Compose or deployed for production using Kubernetes templates.
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What you control
Self-hosting keeps Langfuse data in infrastructure you operate. That gives your team control over data residency, network boundaries, backups, and access to the underlying deployment. It is the stronger fit when prompts or traces must remain in a particular environment.
What you must operate
Unlimited software usage does not remove the engineering work of running a service. You are responsible for choosing infrastructure, applying updates, securing credentials, sizing storage, monitoring availability, and planning backups and recovery. The available data does not state a minimum hardware size, so capacity should be determined from your trace volume, retention policy, and team’s reliability requirements rather than from a universal number.
Local versus production deployment
- For evaluation, run the open-source stack locally with the documented Docker Compose option.
- Connect a development SDK or supported integration and send a small representative workload.
- Check that prompts, generations, input and output usage, and inferred or supplied USD cost appear in the trace.
- For production, use the documented Kubernetes templates and establish storage, secrets, backups, and monitoring before routing real traffic.
- Validate model definitions and reasoning-token ingestion with a known request before relying on cost reports.
Should you choose Cloud Hobby or self-hosting?
Cloud is the faster starting point; self-hosting provides more control and removes the hosted usage allowance. The practical choice depends on the trade-off between operational effort and infrastructure control.
| Decision factor | Cloud Hobby | Open Source self-hosted |
|---|---|---|
| Price documented for the free option | Free; 50,000 units per month for two users, recorded September 4, 2026 | MIT-licensed software; unlimited units and usage as documented |
| Setup | Hosted account and instrumentation; no Langfuse service maintenance | Docker Compose locally or Kubernetes templates for production; your team maintains the deployment |
| Data location | Hosted by Langfuse according to the selected cloud service | Data remains in infrastructure you control |
| Scaling constraint | The Hobby allowance is a monthly 50,000-unit limit | No Langfuse unit limit is stated for the open-source edition; infrastructure capacity remains the practical constraint |
| Core tracing, prompts, evaluation, experiments, and APIs | Available through the hosted service | Included in the open-source edition |
| Operations | Langfuse operates the hosted service | You handle updates, security, storage, backups, and uptime |
| Enterprise controls | Not established by the cited Hobby clarification | Self-Hosted Enterprise adds management APIs, project-level RBAC, retention policies, audit logs, and dedicated support |
| Enterprise procurement | Direct cloud use | Self-Hosted Enterprise can be billed through AWS Marketplace or invoice |
The table separates the free open-source edition from Self-Hosted Enterprise. Enterprise features are not part of the claim that the MIT edition includes unlimited usage.
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How do you instrument an application without losing cost accuracy?
Start with one representative request and verify the entire data path before adding every application. The objective is not merely to create traces; it is to ensure that usage and model identity arrive with each generation.
- Select an ingestion path. Use a Langfuse SDK, or a documented wrapper/integration for OpenAI, LangChain, LlamaIndex, or LiteLLM when it matches your stack.
- Create a trace around a user operation. Give the trace enough context to distinguish an application flow, user, or experiment. Add tags that you will later use as Metrics API filters.
- Record each generation and embedding call. A trace should contain the prompt or input, model name, response observation, and any provider-returned usage.
- Confirm input and output units. Compare what Langfuse received with the provider’s response for the same request. Investigate missing fields before trusting aggregate spend.
- Configure the model definition. If usage is present but price is not, select or create the matching definition and set the applicable usage-type prices, tokenizer, or tiers.
- Test a reasoning model separately. Verify that hidden reasoning-token usage is included. Do not accept an apparently precise total when the provider did not expose those tokens.
- Query aggregates. Use the Metrics API with application, user, or tag filters and compare the result with a known small workload before expanding instrumentation.
What can go wrong with a free Langfuse API setup?
Most misleading reports come from allowance confusion, missing usage, or a model-definition mismatch rather than from the trace itself.
- The Hobby allowance is exceeded: the documented free plan has a 50,000-unit monthly limit for two users. Treat that as a capacity boundary and review which workloads are being instrumented.
- Cost is blank or zero: check whether the provider or integration supplied usage and whether the generation’s model name matches a priced model definition.
- Totals look too low for a reasoning model: hidden reasoning tokens may not have been ingested. Obtain complete provider usage through the integration before using the total for billing analysis.
- Embedding spend is missing: ensure embedding observations are being sent, not only chat generations.
- Metrics do not match a dashboard: verify the same time range, application type, user, and tags. Aggregates are filter-sensitive.
- Self-hosted data is unavailable: investigate the deployment’s storage, credentials, backups, and service health; unlimited usage does not guarantee unlimited infrastructure capacity.
When does Self-Hosted Enterprise make sense?
Choose the enterprise self-hosted offering when the open-source core is not enough for organizational governance. The documented additions are management APIs, project-level role-based access control, data-retention policies, audit logs, and dedicated support.
Enterprise can be billed through AWS Marketplace or by invoice. That procurement route is relevant for organizations that already manage software purchasing through a cloud marketplace, but it does not change the MIT license or the feature set of the open-source edition.
How should teams use Langfuse cost data?
Use traces for diagnosis and aggregates for decisions. A single trace can explain why one request was expensive; Metrics API queries can reveal whether the pattern is isolated or systematic.
- Tag calls by product surface, workflow, or experiment so spend can be compared without parsing prompt text.
- Separate generation and embedding observations when reviewing retrieval-heavy applications.
- Set threshold alerts around the spend levels that matter to your team, then investigate the traces behind an alert.
- Keep model definitions current when a provider changes prices or when your application adds a model with different input, output, or tiered rates.
- Report reasoning-model totals with a qualification whenever hidden-token usage was not supplied.
These practices turn the free API from a request log into an operational feedback loop: prompt changes can be evaluated, model choices can be compared, and unexpected usage can be found before it becomes an invoice surprise.
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Bottom line: is Langfuse’s free API worth using?
For a prototype or small team, Cloud Hobby is the quickest way to begin, with the documented 50,000-unit monthly allowance for two users. For teams that need data-residency control or expect usage beyond that allowance, the MIT-licensed self-hosted edition supplies the core platform and APIs without a Langfuse unit charge, provided the team is prepared to run the infrastructure.
Whichever deployment you choose, validate usage ingestion and model pricing early. Langfuse can track prompts, tokens or other provider-reported units, embeddings, and cost, but reasoning-model totals are only trustworthy when hidden reasoning-token usage is actually supplied.
Frequently Asked Questions
Does self-hosted Langfuse require a Langfuse subscription?
The Open Source edition is MIT-licensed and described as free with unlimited usage, so the software itself does not require a hosted subscription. You still need to provide and operate the infrastructure on which it runs.
Are Langfuse units the same as tokens?
Not necessarily. The Cloud Hobby allowance is expressed in Langfuse units, while traces can contain provider-specific input and output usage; do not convert the monthly allowance into a fixed prompt or token count.
Can Langfuse show an exact bill for a reasoning model?
Only when the trace includes the model’s complete usage, including hidden reasoning-token counts where applicable. If those values are absent, Langfuse warns that it cannot infer the correct cost.
What happens if my provider’s model is not in Langfuse’s pricing definitions?
Create a custom model definition through the UI or Models API and provide the prices, tokenizer, and tiers that apply to your account. Cost inference depends on the generation’s model name matching that definition.
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Can I use Langfuse only for embeddings?
Yes. Langfuse’s usage and cost model covers embedding observations as well as generation observations, so an embedding pipeline can be monitored independently or alongside chat calls.
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