What Is Braintrust? Is It the Best for AI Observability?

Your AI agent shipped last week. It passed every test. And now a customer just got a hallucinated answer that makes zero sense.

You check the logs. You see the request went through. But you can't tell why the agent picked the wrong tool, retrieved stale context, or decided to improvise. That's the gap AI observability is supposed to close. And Braintrust is one of the hottest names trying to close it. But is it the right pick for your team? Let's find out.

What Is Braintrust?

Braintrust (braintrust.dev — not the freelancing marketplace) is an AI observability and evaluation platform. It helps teams monitor LLM applications and AI agents in production, evaluate output quality, and iterate on prompts and models.

The company was founded by Ankur Goyal. In February 2026, Braintrust raised an $80 million Series B led by ICONIQ, with Andreessen Horowitz, Greylock, and Elad Gil participating. That round valued the company at $800 million — a strong signal that investors see AI observability as critical infrastructure.

Braintrust's customer list is impressive. Notion, Stripe, Vercel, Airtable, Instacart, Zapier, Ramp, Dropbox, Cloudflare, and BILL all use the platform. Notion publicly credited Braintrust with helping them go from fixing 3 issues per day to 30.

What Does Braintrust Do Well?

• Tracing. Every step of your AI agent's reasoning gets captured — prompts, tool calls, retrieved context, latency, cost. Traces are stored in Brainstore, a purpose-built database designed for AI workloads. You can query millions of traces quickly.
• Evaluation. This is Braintrust's signature. You can score AI outputs using built-in scorers, LLM-as-a-judge, custom code, or human review. Evaluations run in CI/CD pipelines, so regressions get caught before they ship. One click turns a production trace into a test case.
• Loop. An AI assistant that analyzes your traces and suggests better prompts, scorers, and datasets. Describe what you want to optimize in natural language, and Loop generates it. It's like having a co-pilot for your eval process.
• Annotation and review. Custom annotation interfaces let non-engineers — product managers, QA teams, domain experts — review and score AI outputs without touching code.

Where Braintrust Falls Short

No tool is perfect. Here's what to watch out for.

Braintrust doesn't build agents. It monitors them. Braintrust is an observability and evaluation layer. It doesn't design conversation flows, create knowledge bases, or deploy agents. You build your AI somewhere else, then send traces to Braintrust to evaluate what happened. That means your building tool and your observability tool are two different systems. When something breaks, you spot it in Braintrust but fix it somewhere else.

The Pro tier is a jump. Free is generous. But the next step is $249 per month. For bootstrapped startups or small teams, that's a meaningful leap — especially if you're comparing against open-source options like Langfuse or Phoenix that offer more for less at that stage.

No runtime guardrails. Braintrust evaluates outputs after they happen. It doesn't block or intercept bad responses in real time. If you need guardrails that prevent unsafe outputs from reaching users, you'll need to add that separately. Competitors like Galileo offer this natively.

It's still engineering-centric. The annotation and review features help bring non-engineers into the loop. But the core platform — tracing, evals, datasets, experiments — assumes someone comfortable with SDKs, CI/CD pipelines, and prompt engineering. Product managers can participate, but engineers still drive.

No pre-production simulation. Braintrust shines once your agent is live and generating traces. But if you want to simulate thousands of conversations before launch, test across personas, or validate edge cases in staging — you'll need to supplement with other tools.

The community is still growing. Braintrust isn't open-source. The community around it, while enthusiastic, is smaller than what you'd find with Phoenix, Langfuse, or LangSmith. That means fewer third-party tutorials, fewer community integrations, and a smaller pool of shared knowledge.

Who Shouldn't Use Braintrust?

Braintrust isn't built for everyone. Specifically:

• If you're building AI agents and need a platform that handles design, deployment, and observability together, Braintrust only covers the last part.
• If your team is non-technical — mostly product managers, CX leaders, or conversation designers — you'll need engineering support to get value from Braintrust.
• If you need real-time guardrails to block bad outputs before they reach users, Braintrust evaluates after the fact, not during.
• If you're building AI agents for customer support or lead gen, Braintrust gives you trace data, but it doesn't show you the conversation in the context of the workflow that produced it.

That last point matters more than it sounds. Let me explain.

How Voiceflow Approaches Observability Differently

Braintrust answers: "Was this AI output good?"

Voiceflow answers that too — but also: "Why did the agent say that? Where in the conversation flow did it happen? And how do I fix it right now?"

Voiceflow is an AI agent platform. You design, build, deploy, and observe agents in the same place. Observability isn't a separate tool you pipe data into. It's how you iterate on your agent every day.

Transcripts show every conversation your agent has with real users — turn by turn. Replay them. See exactly what the customer experienced. Inspect tool calls and LLM responses in context. When a conversation goes wrong, you're looking at the actual interaction, not a trace tree.

Evaluations score those transcripts automatically against criteria you define. Resolution rate. Customer satisfaction. Compliance. Custom metrics. They run on every new conversation and can be applied retroactively to historical data. No manual review required.

Analytics give product leads and execs a dashboard that answers "how are our agents performing?" without filing an engineering ticket. Usage patterns, evaluation trends, credit consumption — all in one place.

Agent logs provide the technical depth engineers need. Tool calls, model decisions, data retrieval — the span-level detail you'd get from Braintrust, but embedded in the platform where the agent lives.

So — Is Braintrust the Best for AI Observability?

If "best" means "best at evaluating LLM output quality," Braintrust has a genuine claim. The eval-first approach is smart. The production-to-test-case workflow is fast. The customer list speaks for itself.

But evaluation is only half the story.

The other half is what happens after you find the problem. And that's where the "separate tool" model hits a wall. You spot a regression in Braintrust. You switch to your agent framework. You find the relevant code. You make the change. You redeploy. You wait for new traces. Then you check whether it worked.

In Voiceflow, you see the problem in a transcript. You click through to the visual flow that produced it. You adjust the logic. You test it in staging. You promote to production. You watch evaluations improve. Same session. Same tool.

The fastest path to better AI isn't more dashboards. It's a shorter distance between insight and action.

Braintrust shortens the eval loop. Voiceflow shortens the entire loop — from observation to improvement to ship.

See what that looks like in practice. Start building for free on Voiceflow or watch a demo to see how transcripts, evaluations, and the visual builder work together.