Langfuse vs LangSmith: 2026 comparison

Langfuse and LangSmith in brief

Langfuse and LangSmith help you understand applications and agents built around language models. Both describe ways to inspect execution steps, compare results, and monitor costs. Their clearest initial difference is the access model. Langfuse has open code and a free self-hosting option, while LangSmith is a closed-code freemium product. Both offer a free tier, web access, and an API. Your decision will depend on how you want to deploy the service, where observations need to go, and how spending scales with your usage.

Langfuse : Langfuse tracks agent calls and steps, measures model costs and usage, and evaluates responses. It serves people who want to manage instruction versions and compare trials on datasets.

LangSmith : LangSmith examines agent steps and compares results across versions. It serves people who want to create test datasets and monitor the costs and response times of their agents.

Both meet the same need, which makes the comparison useful. We compare them on 10 verifiable criteria, using the same data as their profiles, collected from official sites.

Comparison table

Criterion by criterion

For each criterion: why it matters, each one's value and who has the edge. A criterion without data on both sides is not decided.

Free tier

A free tier lets you test the tool on your own examples before paying anything. Langfuse : yes. LangSmith : yes. A tie on this point.

Entry price

The first paid plan, per month, gives the minimum cost of regular use; higher plans cost more. Langfuse : $29 per month. LangSmith : $39 per month. Edge: Langfuse.

Public API

An API lets you connect the tool to your software, an automation tool or an AI agent. Langfuse : yes. LangSmith : yes. A tie on this point.

Data export

Being able to get your content back avoids lock-in the day you want to switch tools. Langfuse : yes. LangSmith : yes. A tie on this point.

Data hosting

Where data is stored matters for GDPR and client data: the EU or local processing make compliance simpler. Langfuse : several regions. LangSmith : several regions. A tie on this point.

Interface languages

More interface languages make the tool easier to roll out across an international team. Langfuse : not published. LangSmith : not published. Cannot be decided: data missing on one side.

Platforms

The more platforms (web, desktop, mobile, extension), the better the tool fits your daily routine. Langfuse : web browser and API. LangSmith : web browser and API. A tie on this point.

Open source

Public code lets you audit the tool, self-host it and keep using it if the publisher changes course. Langfuse : yes. LangSmith : no. Edge: Langfuse.

Track record

A tool launched several years ago has a longer history: less risk of sudden shutdown, more user feedback. Langfuse : launched in 2023. LangSmith : launched in 2023. A tie on this point.

Recognition

The number of reference community lists citing the tool shows how well known it is among practitioners. Langfuse : cited in 3 lists. LangSmith : cited in 3 lists. A tie on this point.

In charts

Our analysis

Both products sit in LLMOps and observability and were launched in 2023. Langfuse describes tracking calls and agent steps, measuring costs and model usage, evaluating answers, managing instruction versions, and comparing experiments on datasets. LangSmith emphasizes inspecting agent steps, comparing versions, creating test datasets, and monitoring costs and response times. These descriptions point to overlapping purposes, with different details made explicit in each record. They are useful starting points for matching the products to your work. However, the omission of a feature from one supplied list does not establish that the product lacks it.

Langfuse offers a free tier and lists Core on Langfuse Cloud at USD 29 per month. Free self-hosting is also specified. LangSmith has a free tier and lists Plus at USD 39 per month per user, excluding additional consumption. The two entry prices therefore use different billing bases. Without user counts, usage volumes, or free-tier limits, these figures cannot establish the total cost of an equivalent deployment. Langfuse scores 95 out of 100 for access and transparency, compared with 85 for LangSmith. Those scores describe access and disclosure criteria, rather than observability quality or the accuracy of evaluations.

Langfuse's code is open, whereas LangSmith's is closed in the supplied records. Both list mixed data hosting, but neither record provides regional details, retention periods, or deployment-specific conditions. Langfuse's free self-hosting option gives you another way to choose your environment, without establishing every privacy guarantee. Export formats provide a more concrete distinction. Langfuse lists CSV and JSON, programmatic export through its API, and export to object storage. LangSmith lists CSV, JSONL, and an OpenAI fine-tuning preparation format. The destination and intended reuse of your observations can therefore help guide the choice before you compare interface preferences.

Both services provide web and API access, giving you common starting points for inspecting observations and planning integrations. Langfuse explicitly mentions instruction version management and model usage measurement. LangSmith explicitly includes response-time monitoring and test dataset creation. Which description is more relevant depends on the work you need to perform regularly. Neither product publishes interface language information in these records. Quotas, specific integrations, and onboarding requirements are also absent. As a result, the supplied information supports a comparison of stated workflows and access conditions, but cannot establish how much implementation effort either product would require in your environment.

Depending on your situation

The right choice depends on use. For a few common situations, here are the criteria that matter and who wins (Langfuse value / LangSmith value).

Personal use on a tight budget

Here, Langfuse has the edge: it wins 1 of the 3 criteria that matter in this situation, namely free tier (yes / yes), entry price ($29 per month / $39 per month), and interface languages (not published / not published).

Business use with client data

Neither stands out on the criteria for this situation (data hosting (several regions / several regions), data export (yes / yes), and track record (launched in 2023 / launched in 2023)): decide with a trial.

Integration with your software

Here, Langfuse has the edge: it wins 1 of the 3 criteria that matter in this situation, namely public api (yes / yes), open source (yes / no), and platforms (web browser and API / web browser and API).

Which one to choose?

Choose Langfuse if…

• you value entry price ($29 per month)
• you value open source (yes)

Langfuse is a logical choice when inspecting the code, using free self-hosting, or exporting to object storage matters to you. Its stated features also match instruction versioning and model usage tracking. The Cloud price provides an initial reference point, but an informed budget still requires the volumes and conditions relevant to your deployment and the way your team will operate it.

Choose LangSmith if…

LangSmith does not lead on any measurable criterion; it is still relevant if you already use it or prefer it after a trial.

LangSmith fits priorities such as agent step inspection, test datasets, version comparisons, and response-time monitoring. Its listed OpenAI fine-tuning preparation export may also match your intended data workflow. When considering Plus, account for the per-user billing basis and additional consumption. The supplied feature list gives clear evaluation targets, while leaving quotas and specific integration requirements to be clarified.

Strengths and limits of each

Langfuse

• + Free tier to try it
• + Cheaper than its category median
• + Public API

LangSmith

• + Free tier to try it
• + Cheaper than its category median
• + Public API

The best test is still yours: take three real tasks from your day and give them to both for a week.

What about the others?

If neither suits you, look at Opik, Arize Phoenix, Overmind, MLflow, Respan, and Prisma AIRS AI Gateway — Portkey.

Related comparisons:

• Langfuse vs Arize Phoenix
• LangSmith vs Arize Phoenix

Conclusion

The useful distinction is operational fit. Langfuse provides open code, free self-hosting, and exports to object storage. LangSmith explicitly describes test datasets, response-time monitoring, and a fine-tuning preparation export. Match those capabilities to your workflow, then compare costs using your team size and usage. Neither the entry prices nor the access and transparency scores establish a universal winner or demonstrate which product will produce better evaluations.