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AI assistants & models
LANGFUSE, LANGCHAIN, HELICONE

Langfuse vs LangSmith vs Helicone: LLM observability pricing

Published Pricing verified

Once you ship an LLM feature you need to see what it's actually doing — traces, token costs, latency, eval scores. Three tools dominate, and they price on very different units.

Free tierPaid entryMeters onSelf-host
Langfuse50k units/moCore $29/mo (100k units, unlimited users)usage (units), seats freeyes — MIT, free
LangSmith10k traces/moPlus $39/seat/mo + $0.50 per 1k tracesseats + traceslimited (enterprise)
Helicone50k requests/moPro $20/morequestsyes — open source

The cost gap at volume is enormous. At roughly 1 million events a month, Langfuse Core works out to about $100 (the $29 base plus usage increments). The same volume on LangSmith Plus is around $2,500, because LangSmith charges per seat and per trace — every engineer you add raises the bill, and so does every trace.

Langfuse is the value pick and the one to self-host if you want to: MIT-licensed core, usage-based pricing that keeps team growth free, S3-compatible archival for old traces. It's become the default for teams not married to the LangChain framework.

LangSmith makes the most sense if you're already building on LangChain/LangGraph — tracing is automatic, the prompt playground and eval tooling are tightly integrated, and for a small team the seat cost is tolerable. It gets expensive fast as you scale.

Helicone is the lightweight option: it's a proxy, so integration is a one-line base-URL change, and its strength is cost analytics and caching (which can shave 20–40% off your model spend). Less depth on evals and complex agent traces.

Framework-agnostic and cost-sensitive: Langfuse. All-in on LangChain: LangSmith. Want a proxy that also cuts your bill: Helicone.

Sources
  1. Langfuse — pricing
  2. LangSmith — pricing
  3. Helicone — pricing