Basalt VS Langfuse
Langfuse vs Basalt: which platform should your AI team choose in 2025?
TL;DR
Langfuse is a good pick for early-stage teams or startups focused on monitoring. But if you need advanced evaluations, workflows, or a tool that lets both tech and non-tech teams move fast, Basalt clearly takes the lead.
At a Glance
Langfuse:
Great at monitoring and basic observability. Fine for small teams building simple AI features. But it struggles with advanced use cases like running complex evals, working with logic in prompts, or handling datasets flexibly. If you need to iterate fast or compare results easily, you’ll hit a wall.
Basalt:
All-in-one platform that combines evaluations, monitoring, and collaboration in a user-friendly way. You can iterate on prompts, run tests, compare models, and get feedback instantly. Built for teams of all sizes—including product managers and data folks—not just engineers.
Evaluations from the UI
This is where Basalt stands out.
With Basalt, you can tweak a prompt and instantly re-run evals in the same place. No need to jump between tabs or update registries. You can compare more than ten prompt versions in under a minute, and you get all the major API keys preloaded.
Basalt lets you:
- Edit prompts and rerun tests instantly from the same tab
- Compare LLMs side by side in an Arena view
- Get immediate feedback on every change you make
Langfuse limitations:
- No workbench to quickly re-run after changes
- No side-by-side model comparison
- No clear feedback on prompt quality
- Importing datasets is clunky; no variable mapping
Evaluations in Code (SDK)
Both platforms let you run evals from code, but Basalt makes it easy to AB test your agent and see exactly which version performs best. You get a clear, visual comparison of traces, and you can even run evaluators on different steps inside your workflow.
Basalt strengths:
- AB test and compare traces visually
- See at a glance which agent version wins
- Evaluators can run at trace level or inside steps
Langfuse falls short:
- No side-by-side trace comparison
- No evaluators inside trace steps
- Hard to know which run worked best without digging through all the outputs
Playground
Langfuse offers a basic playground—good for quick tries, but not for complex scenarios. There’s no templating or logic, so forget about if/else or loops in prompts.
Basalt playground:
- Add logic with Jinja templating
- Move text blocks around in a Notion-like interface
- Choose different block types easily
Datasets and Evaluators
Both platforms handle datasets, but Basalt offers more flexibility. Langfuse lacks mapping when importing datasets into prompts, which becomes a hassle as things scale. Basalt supports custom Python evaluators and makes reusing datasets easy.
Monitoring
Langfuse is originally a monitoring tool, and it’s strong on analytics. You get custom dashboards and columns for tracking. But the UX is developer-focused and not easy for product managers or non-tech folks.
Basalt monitoring:
- Simpler interface for non-technical users
- Slightly fewer options, but easier for teams to replay and analyze runs
- Perfect for teams that need everyone (not just devs) to collaborate
Final Verdict
Langfuse is great if you only need monitoring and have a technical team. But for teams that want to move fast, run advanced evaluations, compare results instantly, and let product managers or data analysts join in, Basalt is the better choice. It’s built for modern, collaborative AI teams that need more than just logs, they need answers and insight.
In short:
Go with Basalt if your team cares about fast iteration, in-depth evaluation, and easy collaboration.
