The honest version: if you're running LangChain or LangGraph, start with LangSmith. Zero-configuration tracing, immediate production visibility, built by the same team. If you're not on LangChain, or if systematic evaluation matters more than fast tracing, use Braintrust. The marketing pages for both tools look nearly identical. The actual product priorities are not.
I've seen teams spend weeks on the wrong tool because they evaluated both at demo quality instead of at the friction points that appear in production: the eval workflow that requires more boilerplate than expected, the trace storage bill that compounds faster than the pricing page suggested, the self-hosting requirement that neither tool makes easy. This post covers those friction points directly.
Is Braintrust or LangSmith better for LLM evaluation?
Braintrust is better for evaluation. Its entire architecture is built around experiments: versioned datasets, per-sample scoring, CI integration, and diff views against prior runs. LangSmith added evaluation features on top of a tracing product, and the seams show — the eval workflow requires more manual setup to reach the same systematic coverage that Braintrust provides out of the box.
Braintrust's published results include Notion (10x productivity improvement), Zapier (sub-50% to 90%+ accuracy in 2–3 months using Braintrust evals), and Coursera (90% learner satisfaction at 45x interaction scale). The consistent pattern across these cases is the same: teams caught regressions before release instead of discovering them through user complaints.
If I'm building a product where prompt quality directly affects user outcomes — an AI writing assistant, a code generation tool, a customer-facing chatbot — I want Braintrust in CI running evals against every prompt change before it ships. LangSmith can do this too, but I'd be writing more configuration to get there. With Braintrust's GitHub Action, a basic regression suite is running in under two hours.
Pricing (braintrust.dev/pricing): Starts with a free tier, paid plans scale by logged rows. Self-hosting requires an enterprise contract — there's no self-managed open-source tier.
When Braintrust isn't the right choice: If your team's primary workflow is production incident investigation ("something broke at 2pm, show me what happened"), Braintrust's trace UI is functional but not optimized for that. It's built around evaluation cycles, not forensic debugging.
Is LangSmith worth using if you're not on LangChain?
Probably not. LangSmith's main advantage is zero-configuration auto-instrumentation for LangChain and LangGraph apps. If you're calling the OpenAI or Anthropic API directly, that advantage disappears. You're writing explicit logging calls via the LangSmith Python client, which isn't much different from integrating any SDK-based observability tool.
For teams not on LangChain, LangSmith becomes a competent but unremarkable tracing tool at $39/seat/month — the same price as tools with better agent tracing depth for non-LangChain stacks.
For teams on LangChain, LangSmith is genuinely the fastest path to production visibility. The run tree visualization — where every chain step, tool call, and LLM invocation is nested in a tree you can walk — is particularly good for debugging complex agent workflows. When a multi-step chain produces a bad output, LangSmith's trace UI shows exactly which step went wrong and what the intermediate states were. This is where the LangChain-native integration earns its keep.
Pricing (langchain.com/langsmith): Free tier with limited monthly traces. Plus is $39/seat/month with more traces and longer retention. Self-hosting is available but requires running their Docker stack yourself — it's a real operational commitment, not a one-command deploy.
The eval limitation I'd push back on: LangSmith's evaluation workflow, as documented here, requires more steps to configure a clean regression suite than Braintrust does. This isn't a fatal flaw — it works — but it does mean spending more engineering time on evaluation infrastructure and less on improving your prompts. At scale, that difference compounds.
Braintrust vs LangSmith: direct comparison
| Dimension | Braintrust | LangSmith |
|---|---|---|
| Built around | Experiments and evals | Tracing and monitoring |
| LangChain dependency | None | Zero-config for LangChain; more work otherwise |
| Eval datasets | First-class, versioned | Available, more manual setup |
| CI integration | Built-in GitHub Action | Available, requires more config |
| Production debugging | Functional | Strong (especially on LangChain) |
| Self-hosting | Enterprise contract only | Docker stack, self-managed |
| Pricing model | Per logged row | Per trace |
| Free tier | Yes | Yes |
| Budget enforcement | None | None |
| Ownership | Independent | LangChain Inc. (VC-backed) |
The pricing model difference matters at agent scale. LangSmith charges per trace — one user action that triggers 20 LLM calls in a chain is one trace. Braintrust charges per logged row — if you're logging each of those 20 calls, that's 20 rows. At low volume, this is irrelevant. At 10M+ calls per month, the billing model you're on becomes a real line item.
The self-hosting difference matters for compliance. Neither tool makes self-hosting simple: LangSmith's Docker stack requires operational maintenance, and Braintrust requires an enterprise contract before you can run it yourself. If your requirements include data residency or no external data transmission, verify both tools' options before committing.
What neither tool does: prevent the spend before it happens
Braintrust and LangSmith both operate after the LLM call returns. They receive the response, log the token counts and cost, and make it queryable. Neither one can intercept the call before it fires.
For most teams this is fine. You're using these tools for quality assurance — catching eval regressions, debugging production incidents, understanding cost trends. Spending happens, you see it, you optimize. The feedback loop works.
The feedback loop breaks for agent workloads. An autonomous agent that enters a retry loop at 3am, or processes a 500k-token document because the user uploaded the wrong file, or spawns parallel subagents — that agent can exhaust a month's budget in twenty minutes. Braintrust and LangSmith will log every call clearly and attribute every token correctly. The money is still gone.
Pre-flight enforcement is a different mechanism: it estimates the cost of each call before sending it, checks the remaining budget, and blocks the call if the budget is exhausted. This is upstream of observability — it prevents the spend instead of recording it. For teams with per-customer budgets or autonomous agents, it's not optional, and neither Braintrust nor LangSmith provides it at any tier. For how pre-flight enforcement actually works, see How to Set a Hard Budget Cap on LLM API Calls.
Frequently asked questions
Should I use Braintrust or LangSmith for a new AI product in 2026?
If you're using LangChain or LangGraph: start with LangSmith. The zero-configuration tracing is a genuine advantage at that stage — you'll have production visibility immediately without additional setup. If you're calling the OpenAI or Anthropic API directly, or using another framework: start with Braintrust. The eval infrastructure is more mature, and the absence of a framework dependency means it integrates cleanly into any stack. Either way, add systematic evals before your first real deployment — finding regressions through user complaints is substantially more expensive than catching them in CI.
Can I run LangSmith without LangChain?
Yes, via the LangSmith Python client or REST API. You write explicit logging calls instead of relying on auto-instrumentation. The tracing functionality works, but the zero-configuration advantage that makes LangSmith compelling for LangChain teams no longer applies. At that point I'd compare LangSmith against Langfuse and Arize Phoenix, which offer similar SDK-based tracing with different self-hosting economics.
How does Braintrust's per-row pricing compare to LangSmith's per-trace pricing?
For simple, low-call-count workflows, Braintrust's per-row pricing is typically cheaper than LangSmith's per-trace pricing. For agent workflows where a single user action triggers 20+ LLM calls in a chain, LangSmith's per-trace model becomes significantly cheaper — you pay for one trace regardless of how many individual calls are in it. Model your actual call patterns before committing to either pricing model at scale.
Does either tool enforce per-user budget limits?
No. Both tools track and attribute spend after calls complete. Neither one enforces a hard per-user cap that blocks API calls before they fire. For per-user enforcement, that requires a separate pre-flight enforcement layer — see noburn.dev for a self-serve option that handles this without enterprise pricing.
Is LangSmith free to self-host?
LangSmith offers a self-hosted option, but it requires running their Docker stack yourself — it's not a simple one-command deploy. Braintrust's self-hosting requires an enterprise contract. If self-hosting is a hard requirement, Langfuse is open-source under MIT and self-hostable without a commercial agreement or Docker complexity.