Helicone vs LangSmith
Two sides of the LLM observability & evals decision: proxy logging and hosted eval 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 LangSmith if- Your app is built on LangChain or LangGraph
Use it whenYou already use the LangChain stack.
Trade-offPaid per seat beyond the free tier; self-hosting is enterprise-only.
At a glance
| Helicone | LangSmith | |
|---|---|---|
| Used by | 13 makers' products | 11 makers' products · 49 open-source projects |
| Cost at default usagetraces 100k traces | $116/mo Pro | $475/mo Developer |
| Downloads | 3.8k/wk | 6.1M/wk−4% 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 with a monthly trace limit; paid per-seat plan with usage overage; custom Enterprise, including self-hosted. · paid from $39/seat/mo |
| Free tier | Yes | Yes |
| Open source | Yes · self-hostable | No |
| Incidents, 90 daysfrom its status page | no public status feed | 0 |
Cost as you grow
Both cost $0 up to 1k traces; from 10k traces Helicone costs less ($0 vs $25); and still does at 10M traces ($415 vs $49,975). They're different kinds of tool — proxy logging and hosted eval platform — so the prices don't buy the same thing.
The numbers, plan by plan
| Traces per month | Helicone | LangSmith |
|---|---|---|
| 1,000 | $0 Hobby | $0 Developer |
| 10,000 | $0 Hobby | $25 Developer |
| 50,000 | $100 Pro | $225 Developer |
| 100,000 | $116 Pro | $475 Developer |
| 500,000 | $164 Pro | $2,475 Developer |
| 1,000,000 | $199 Pro | $4,975 Developer |
| 5,000,000 | $315 Pro | $24,975 Developer |
| 10,000,000 | $415 Pro | $49,975 Developer |
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 LangSmithLangSmith’s real-time analytics and versioning keep our AI agents rock-solid -- so everything just works better.
You can't build AI agents without monitoring. Metadata filtering is strong.
I deployed the manage Pig agent on LangGraph and it's been smooth sailing!
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
LangSmith- It keeps pulling users toward the LangChain platform, and LangChain docs push LangSmith, which feels like lock-in. HNHN 2HN 3HN 4
- Traces show which agent failed but not why, so root-cause analysis stays manual. HNHN 2HN 3HN 4
- Viewing your own traces requires a cloud account, with no local-first option. HNHN 2
Who uses each
Helicone13 makers' products
Alai
Codebuff
Fume
Image Ally
Persana
Tusk
WordwareLangSmith11 makers' products
lmChatGPTtfy
Watchman AI
Meet-TingWhat makers pair each with
With Helicone
Pretty Prompt
Spiral
Brand.devBrand.devWith LangSmith
Astrid
Pig
Traycer AIAstrid
NeuralAgentTraycer AINeuralAgentTraycer AI
GitLawTraycer AIGitLaw
IntrycTraycer AINeither quite right?
See the whole LLM observability & evals decision →
LangfuseOpen-source platformTracing, prompt management and evals in one tool you can run on your own server for free.vs Helicone →vs LangSmith →
Arize PhoenixOpen-source platformOpenTelemetry-based tracing and evals you can start locally in a notebook, then self-host or move to its cloud.vs LangSmith →
BraintrustHosted eval platformEval-driven work — scoring outputs and comparing prompts and models side by side in experiments.vs Helicone →vs LangSmith →