Helicone vs Langfuse
Two sides of the LLM observability & evals decision: proxy logging and open-source platform. When each fits, what it costs, who moves from one to the other, and what makers who chose it say.
HeliconeProxy loggingllms.txtIn maintenance: Acquired by Mintlify in March 2026; Helicone says the service stays live in maintenance mode — security fixes, new model support and bug fixes, but no new product direction. source
Which fits you
Choose Helicone if- You want request logs and costs today by changing one base URL
Use it whenYou want visibility today and your app makes direct model calls.
Trade-offProxy logging sees individual requests well but agent steps and eval workflows less deeply; acquired by Mintlify in March 2026, so check its roadmap before building on it.
Choose Langfuse if- You want tracing, prompts and evals in one tool you can self-host for free
Use it whenYou want to own your trace data or keep costs flat as volume grows.
Trade-offSelf-hosting means running Postgres, ClickHouse, Redis and object storage alongside it; otherwise it's a usage-based cloud plan.
At a glance
| Helicone | Langfuse | |
|---|---|---|
| Used by | 13 makers' products | 33 makers' products · 43 open-source projects |
| Cost at default usagetraces 100k traces | $116/mo Pro | $29/mo Core |
| Downloads | 3.8k/wk | 1.8M/wk+81% vs npm |
| Pricing | Free tier with a monthly request limit; paid tiers with usage overage; custom Enterprise. Self-hosting is free (Apache 2.0). · paid from $79/mo | Free tier (50k units/mo); usage-based paid plans; fully free to self-host under MIT license. · paid from $29/mo |
| Free tier | Yes | Yes |
| Open source | Yes · self-hostable | Yes · self-hostable |
| Incidents, 90 daysfrom its status page | no public status feed | 15 (1 major) |
Cost as you grow
Both cost $0 up to 10k traces; from 50k traces Langfuse costs less ($0 vs $100); from about 5M traces Helicone does ($315 vs $421). They're different kinds of tool — proxy logging and open-source platform — so the prices don't buy the same thing.
What makers say
Makers on using it for LLM observability & evals, from Product Hunt and Starter Story interviews, each linked to the source. Products with a page of their own and fuller notes first.
On HeliconeHelicone AI offers open-source observability tools tailored for developers working with LLMs. It simplifies debugging and optimization, providing valuable insights into AI model performance.
I found Helicone in the middle of development, and it has been awesome. It gives me extremely useful and detailed insights on usage, costs, and response times, with just a couple of lines of code.
Helps us debug AI, and have clarity on AI analytics, costs, and latency. We've also been able to save quite a lot of credits because of its caching functionality!
On LangfuseMarc gave me an in-person onboarding in SF - I found an issue in our LLM provider config just 30 minutes after the onboarding thanks to Langfuse. 10/10 recommendation
Langfuse powers our LLM observability. Without Langfuse, our AI agent would not be best-in-class. We have been using Langfuse since nearly the beginning: 2+ years!
We use Langfuse to keep track of our LLM prompts while building MCP-Builder.ai. It’s a great tool that makes it easy to monitor and analyze prompt performance, helping us improve quickly and efficiently.
Loved and watch-outs
Themes that recur in makers' words and Hacker News comments, each linked to what it summarises.
Helicone- Setup takes a couple of lines of code and works as a proxy, so it suits stacks outside Python too. PHHN
- Custom properties attribute LLM cost, latency and usage to individual customers or features. helicone.aiPH
- Request logs and session views make it practical to debug issues coming from real users. PHhelicone.aiHN
- Acquired by Mintlify in March 2026; some developers report the product has since moved to maintenance mode. helicone.aiHN
- Per-run cost breakdowns need manual labelling and custom SQL, since views are retrospective. HNHN 2
Langfuse- Detailed traces show each agent call and the context it pulled, which makes debugging agent behaviour much easier. PHPH 2HN
- Token usage and cost are tracked across providers alongside quality, in one place. PHPH 2HN
- Open source and self-hostable, a common default for teams wanting tracing on their own infrastructure. PHHNHN 2HN 3
- Prompt management and experiments feel basic next to the tracing core. HNHN 2HN 3
- Views are retrospective dashboards, so explaining one run's cost or failure still means reading trace trees by hand. HNHN 2HN 3HN 4
- The ClickHouse acquisition raised GDPR and data-residency concerns for EU users of the cloud version. HNHN 2
Who uses each
Alai
Codebuff
Fume
Image Ally
Persana
Tusk
Wordware
Agentplace.io
Conduit
Finyuus
giselle
Imagine
Magic Patterns
Manufact (mcp-use)What makers pair each with
With Helicone
Pretty Prompt
Spiral
Brand.devBrand.devWith Langfuse
OpenFunnel(YC F24)
stagewise
Outlit AI
Productlane
Relay.app
THEO Growth
Pebbles Ai
GoMarble
Flowstep
Lamatic.ai
Lingo.dev
MCP-Builder.ai
Minicule
Symvol Video AI for Education
Yapify
Sendbird
cognee
Draft Alpha
Orango
TruvaNeither quite right?
See the whole LLM observability & evals decision →
Arize PhoenixOpen-source platformOpenTelemetry-based tracing and evals you can start locally in a notebook, then self-host or move to its cloud.vs Langfuse →
LangSmithHosted eval platformApps built on LangChain or LangGraph, where tracing works with almost no setup.vs Helicone →vs Langfuse →
BraintrustHosted eval platformEval-driven work — scoring outputs and comparing prompts and models side by side in experiments.vs Helicone →vs Langfuse →