AI Agent Evals 2026: Braintrust $249 vs LangSmith Cost

Quick Answer: For AI agent evals in 2026, Braintrust Pro costs $249 per month with unlimited users, while LangSmith Plus costs $39 per seat per month plus trace overage. Braintrust wins above seven seats or when you meter by processed data. LangSmith wins for small LangGraph teams that want native tracing and managed deployment in one place.

Author: Deepak Bagada — founder of SaaS Next in Junagadh, Gujarat. I run eval gates for agent builds across five providers, and I have paid both bills. The comparison below uses the published October 2026 pricing and the trace counts from my own production agents.

Key Takeaways

• The billing units are different. LangSmith charges per seat plus per trace. Braintrust charges a platform fee plus processed gigabytes and scores. A price per trace cannot be compared directly with a price per GB.
• Seven seats is the break-even point. At seven LangSmith seats, the $273 seat bill alone passes Braintrust Pro at $249, before any trace overage.
• Trace multiplication is the hidden cost. One user question can produce seven traces in an agent with planning, retrieval, and tool calls. Budget for traces per task, not per conversation.
• The eval gate matters more than the dashboard. Choose the platform that can fail a pull request. A pretty trace viewer without a CI threshold is an expensive log viewer.

The Published Pricing, Side by Side

Both vendors changed their packaging during 2026, so old blog posts are unreliable. These are the terms I verified in October 2026.

The unlimited-user model is Braintrust's main commercial advantage. A five-person team pays $249, not $195, so LangSmith is cheaper at five seats. At eight seats, LangSmith Plus is $312 before usage, and Braintrust is still $249 plus data.

What One Agent Query Actually Costs

Trace counting is where budgets break. A single support question in my stack produces multiple spans: a router call, a retrieval query, a reranker call, a tool call, a final generation, and sometimes a retry. LangSmith bills base traces, and the vendor's own documentation warns that an agent query can produce several billable traces.

At 100,000 base traces, the LangSmith overage is 90,000 traces beyond a single seat's 10,000 allowance. At the published $5 per 1,000 base traces, that is $450 before the seat fee. The same workload on Braintrust is metered by processed data, which depends on payload size, not span count. A 100,000-trace month at 1.5KB per trace is about 150MB, well inside the 5GB Pro allowance.

That is the core trade. LangSmith's unit punishes verbose agents. Braintrust's unit punishes large payloads: long prompts, big retrieved chunks, and image inputs. If your agent sends 40KB of context per call, Braintrust's $3/GB overage becomes the number to watch.

The Break-Even Math for an Indian Team

I run the comparison in rupees because the vendor bills in dollars and the team budget is in INR. The table uses ₹88 per dollar and ignores GST, which applies to both.

At 100,000 traces, LangSmith is more expensive at every seat count in this table, because the trace overage dominates. At 10,000 traces per month, LangSmith at three seats is $117 versus $249, and the answer flips.

The break-even depends on two variables, not one. Seat count sets the floor; trace volume sets the slope. Plot your own numbers before choosing, and include the traces your staging environment generates. I have seen a team budget for production and forget that CI runs add 18% to the monthly trace count.

Feature Differences That Change the Decision

Price is only half the comparison. The platforms have different centres of gravity: LangSmith is built around traces and the LangGraph lifecycle, Braintrust is built around experiments and datasets.

If your agent is LangGraph, LangSmith's zero-config tracing is hard to beat. If your agent is Pydantic AI, CrewAI, or a custom loop, Braintrust's integration list is broader, and its CLI fits a GitHub Actions gate without much glue.

My OpenTelemetry tracing setup is the escape hatch for both: emit OTel spans, keep the vendor replaceable, and use the platform for evals rather than as the only source of truth.

Production War Story: The Trace Bill That Doubled

In April 2026 a client's support agent went from 14,000 to 31,000 monthly conversations after a WhatsApp campaign. The LangSmith bill rose from $186 to $612 in one cycle. Nobody had changed the agent, but the campaign added a retry path for undelivered messages, and each retry created two extra traces.

The team had budgeted per conversation, not per trace. I added a trace budget to the agent's instrumentation: a counter per conversation, an alert at 12 spans, and a hard stop at 20. The hard stop returns a graceful fallback instead of continuing the loop. Trace volume fell 22% without a visible quality change, and the bill stabilised at $410.

The fix was not a vendor switch. It was measuring the right unit. A team that does not know its traces per conversation will be surprised by either vendor's bill.

Production War Story: The Eval Gate That Caught a 19% Regression

In June 2026 I upgraded a client's extraction model to a cheaper tier. The demo looked identical, and the latency improved by 340ms. The eval gate told a different story: tool-call accuracy on the 140-case gold set fell from 94.3% to 75.1%, a 19.2-point drop.

The failure was concentrated in invoices with handwritten totals. The cheaper model read the printed total and ignored the handwritten correction. Without a gate, that regression would have shipped and produced wrong ledger entries for a week before a client noticed.

I kept the cheaper model for printed invoices and routed handwritten ones to the previous model. Cost fell 31%, accuracy returned to 93.8%, and the routing rule was documented in the runbook. The Pydantic eval harness pattern is the structure behind the gate.

A CI Gate You Can Copy

The platform is not the gate. The gate is a threshold in CI that fails the build; the autonomous QA swarm covers the broader bug-catching loop around it. This is a pytest example that works with either vendor's SDK; swap the scorer import for Braintrust's if that is your platform.

# tests/test_agent_quality.py
import json
import os

import pytest
from langsmith import Client, evaluate

DATASET = os.environ["EVAL_DATASET"]
THRESHOLD = 0.90


def exact_tool_match(run, example) -> dict:
    expected = example.outputs["tool"]
    actual = (run.outputs or {}).get("tool")
    return {"key": "tool_match", "score": float(expected == actual)}


@pytest.mark.skipif(os.environ.get("RUN_EVALS") != "1", reason="eval job only")
def test_agent_tool_accuracy():
    client = Client()
    results = evaluate(
        lambda inputs: {"tool": my_agent(inputs["question"])["tool"]},
        data=DATASET,
        evaluators=[exact_tool_match],
        experiment_prefix="ci-gate",
    )

    scores = [r["tool_match"] for r in results if r.get("tool_match") is not None]
    accuracy = sum(scores) / len(scores)
    print(json.dumps({"accuracy": accuracy, "cases": len(scores)}))

    assert accuracy >= THRESHOLD, f"tool accuracy {accuracy:.3f} below {THRESHOLD}"

# .github/workflows/agent-evals.yml
name: agent-evals
on:
  pull_request:
    paths:
      - "app/agents/**"
      - "prompts/**"
jobs:
  evals:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: shivammathur/setup-php@v2
        with:
          php-version: "8.4"
      - run: composer install --no-interaction --prefer-dist
      - run: pip install langsmith pytest
      - run: RUN_EVALS=1 pytest tests/test_agent_quality.py
        env:
          LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
          EVAL_DATASET: support-agent-gold-140

The workflow runs only when agent files or prompts change. That keeps the eval cost bounded and makes the gate fast enough that engineers do not bypass it.

When NOT to Use Either Platform

Do not pay for either platform if you have no gold set. An eval dashboard without a labelled dataset is a trace viewer with a monthly fee. Build 80 to 150 cases first, even in a spreadsheet, then choose the tool that can run them.

Do not choose Braintrust only because the sticker price looks lower. At three seats and low trace volume, LangSmith is cheaper. At high payload sizes, Braintrust's GB overage can pass LangSmith's trace bill. Run your own numbers with your own token and trace counts.

Do not use a managed platform if your client requires data residency inside India and the vendor cannot meet it. Both offer enterprise self-hosting, but that changes the price and the operational burden. Check the contract before the architecture.

Do not use either as your only observability layer. Keep OTel spans in your own store so a vendor outage does not blind you. The platform is for evaluation and collaboration; the raw telemetry belongs to you.

Bottom Line

Braintrust Pro at $249 per month is the better commercial fit for teams above seven seats or for agents with high trace volume and small payloads. LangSmith Plus at $39 per seat is the better fit for small LangGraph teams that want native tracing and managed deployment. The real decision is not the dashboard; it is whether you can fail a pull request on a quality regression. Pick the platform that makes that gate easy, then negotiate the bill.

Frequently Asked Questions

Is LangSmith or Braintrust cheaper in 2026?

It depends on seats and volume. LangSmith Plus costs $39 per seat with 10,000 base traces included; Braintrust Pro costs $249 per month with unlimited users and 5GB of processed data. At seven seats, LangSmith's seat bill alone passes Braintrust Pro before trace overage. At three seats and low volume, LangSmith is cheaper.

How does LangSmith bill traces?

LangSmith bills base traces, and an agent query can produce several traces because each planning, retrieval, and tool step is instrumented. The published overage is $5 per 1,000 base traces, with extended retention costing more. A 100,000-trace month can exceed the seat fee by several times.

How does Braintrust bill usage?

Braintrust charges a platform fee plus processed data and scores. Starter includes 1GB and 10,000 scores, with overage at $4/GB and $2.50 per 1,000 scores. Pro includes 5GB and 50,000 scores, with overage at $3/GB and $1.50 per 1,000 scores. Users are unlimited on both plans.

Which platform is better for LangGraph agents?

LangSmith is the lower-friction choice for LangGraph because tracing works with one environment variable and the platform includes managed LangGraph deployment. Braintrust is framework-agnostic and a better fit if your stack includes Pydantic AI, CrewAI, or several orchestration frameworks. Deepak Bagada uses an OTel layer to keep both replaceable.