Braintrust starts at $249/month for its paid tier, a flat platform fee that immediately filters out hobbyists. While competitors like LangSmith or Langfuse charge by the seat or usage event, Braintrust charges for the infrastructure of evaluation, betting that serious teams will pay a premium for unlimited seats and a rigorous, data-centric workflow.
For that price, you aren't just buying a trace logger; you're buying an "Excel for LLMs" that forces you to think in datasets, not just singular prompt runs. The platform shines in its evaluation framework. Instead of vaguely eyeing a trace to see if it looks right, Braintrust pushes you to define unit tests (scorers) for your prompts. You run an experiment, it diffs the results against a baseline, and you get a clear percentage improvement or regression.
Technically, the standout feature is the architecture. For enterprise tiers, Braintrust uses a "hybrid" model where the data plane (logs, traces) lives in your own AWS/GCP account, while the control plane (UI) is hosted by them. Your browser queries your own database directly via CORS. This is a massive win for compliance-heavy industries (fintech, healthcare) that need SOC2/HIPAA compliance but don't want to build their own internal tools. The data never hits Braintrust's servers.
However, the pricing model creates a distinct awkward zone. The Free tier is generous (1M traces), but the jump to $249/month for Pro is steep if you’re a solopreneur or a 2-person shop. In contrast, LangSmith’s $39/seat model scales more gently. Additionally, while Braintrust handles LangChain traces, it doesn’t feel as native to the LangChain internals as LangSmith does. Deep debugging of complex agent loops feels slightly more friction-heavy here.
Ultimately, Braintrust is the "adult" choice. If you are a team of 10+ including Product Managers who need to review prompts without touching code, the flat fee allows you to invite the whole company without worrying about seat costs. If you are a solo dev hacking on a side project, stick to Langfuse or the free tier of LangSmith.
Pricing
The free tier is surprisingly usable for pre-production, offering 1M trace spans and 1GB of data retention (14 days). This is enough for a small team to build and test an MVP.
The cliff is vertical: upgrading to Pro costs a flat $249/month. This covers unlimited users and unlimited trace spans, but you still pay overages for processed data ($3/GB > 5GB) and evaluation scores ($1.50/1k > 50k).
Real Workload Math: For a team of 10 engineers generating 5M traces/month:
- LangSmith: ~$390 (seats) + usage fees.
- Braintrust: $249 (flat) + data overages. Braintrust becomes cheaper as your headcount grows, whereas competitors become cheaper if your team is small but traffic is high.
Technical Verdict
Solid, low-latency SDKs for Python and TypeScript that stay out of your way. The library creates a local buffer and sends logs asynchronously, so your app performance isn't impacted. Documentation is clean but assumes you understand the concepts of "experiments" vs "logging." The hybrid architecture (Enterprise only) is technically impressive, utilizing a custom Rust-based engine ("Brainstore") in your VPC for query performance that rivals SaaS solutions.
Quick Start
# pip install braintrust
from braintrust import init_logger, log
logger = init_logger(project="MyProject")
with logger.start_span(name="llm_call") as span:
# Your LLM logic here
output = "Hello world"
span.log(input="Hi", output=output)
print(f"Logged to: {logger.project.url}")Watch Out
- The $249/mo Pro plan does NOT include the hybrid (self-hosted data plane) feature; that is Enterprise only.
- The UI is heavy on "Experiments" and "Datasets"; if you just want a simple scrolling log of production traces, it feels like overkill.
- Data retention on the Free tier is only 14 days, which is short for debugging historical regressions.
- Scoring functions (evals) run in your environment, so you must manage the compute/latency for running them.