Lamini vs Langfuse
A detailed comparison to help you choose between Lamini and Langfuse.
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Verdict: Langfuse wins
Langfuse scores 4.3/5, edging out Lamini at 3.5/5. Engineering teams running LLM apps or agents in production who want framework-agnostic tracing and evaluation with either a cheap managed cloud or a free self-hosted stack.
Lamini Fine-tune LLMs on your data, guaranteed accurate | Langfuse Open-source LLM observability and evaluation | |
|---|---|---|
| Rating | 3.5 (1 review) | 4.3 (1 review) |
| Pricing Model | freemium | freemium |
| Starting Price | Free tier available | Free tier available |
| Best For | Enterprise teams whose agents hallucinate about a closed set of company facts — schemas, catalogs, taxonomies — and who have hit an accuracy ceiling with RAG alone. | Engineering teams running LLM apps or agents in production who want framework-agnostic tracing and evaluation with either a cheap managed cloud or a free self-hosted stack. |
| Free Tier | ||
| API Access | ||
| Team Features | ||
| Open Source | ||
| Tags | free tierapi access | free tieropen sourceapi access |
| Visit Lamini → | Visit Langfuse → |
Lamini
Pros
- + Memory Tuning targets factual accuracy directly: vendor-reported 94.7% text-to-SQL accuracy where the same team's RAG pipeline stalled at 50% (lamini.ai case study)
- + Flat inference pricing at $0.50 per million tokens for input, output and JSON output alike (lamini.ai/pricing, at time of writing)
- + Deterministic JSON structured output removes format failures from agent tool-call parsing (docs.lamini.ai)
- + Tuning is fast enough to iterate in a working session — roughly 30 minutes per pass for 1,000 samples on a 7B model in the published walkthrough
- + Deployment flexibility covers regulated buyers: On-Demand, Reserved GPUs, or Self-Managed in a VPC, on-premises or air-gapped
Cons
- - Continued tuning on an already-tuned model is unsupported (documented known issue), so a changing corpus means re-tuning from base each time
- - Training data must follow exact input/output dict formatting or the job can complete without any factual improvement
- - No native LangChain or LlamaIndex integrations — wrappers are your own work
- - Memory Tuning results live in a proprietary pipeline; migrating platforms means retraining and losing the accuracy gains
- - Published tuning rates conflict ($0.50/step on the pricing page versus $1/step in the On-Demand launch blog), and the 95%+ accuracy claims are all vendor-reported
Langfuse
Pros
- + MIT-licensed core with free, unrestricted self-hosting — only named enterprise features like audit logs and SCIM need a licence key
- + A 5-seat team at 100k traces runs roughly $69/month versus about $420/month for the main closed alternative (Panoply's independent estimate, consistent with my bills)
- + OpenTelemetry-native tracing across OpenAI SDK, LangChain, LlamaIndex, Pydantic AI, Vercel AI SDK, CrewAI and raw OTLP
- + Graduated usage pricing is transparent and cheap: worked examples on the pricing page match my own invoices (200k units on Core = $37/month)
- + Dataset-backed evaluation with the langfuse/experiment-action GitHub step let me gate prompt changes in CI on a real test set
Cons
- - v3 self-hosting needs Postgres, ClickHouse, Redis and S3 where v2 ran on one container — a four-service system to operate
- - Heavy agent traces render slowly in the UI (issue threads #10391 and #9933 match the multi-second loads I measured)
- - Online evaluations score a sample of traffic rather than everything, so rare failure modes can go unscored
- - UI-configured evaluators cannot be triggered programmatically for scheduled pipelines in my setup
- - Owned by ClickHouse, Inc. since January 2026 — licence unchanged so far, but the vendor-independence profile shifted
Frequently Asked Questions
Which is better, Lamini or Langfuse?▼
Langfuse scores higher at 4.3/5 vs 3.5/5 for Lamini, making it the better-rated option in our comparison.
What is the difference between Lamini and Langfuse?▼
Lamini: Lamini is an enterprise platform for tuning and running open large language models on a company's own data, inside its own cloud or data centre. Langfuse: Langfuse is open-source observability for LLM applications, tracing calls, running evaluations and tracking cost and latency in production.
Is Lamini or Langfuse more affordable?▼
Lamini starts at €0/mo, Langfuse starts at €0/mo. Check the comparison table above for current pricing.
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