Weave vs Braintrust (2026): Which LLM Eval Platform to Pick

If you are choosing an LLM eval platform in 2026 and the shortlist is Weave vs Braintrust, the short answer is this: pick W&B Weave if your team already runs Weights & Biases and you want LLM evaluation without adding a vendor, and pick Braintrust if evaluation is the centre of your development loop and you want a platform built for nothing else. This post compares them on evaluation workflow, tracing, scorers, guardrails, pricing, and how well each supports eval driven development. For the adjacent decision, see our Braintrust vs LangSmith comparison.

The short answer

• W&B Weave - the consolidation pick. LLM tracing, a structured Evaluation primitive, code and LLM-judge scorers, guardrails, a playground, and online evaluation, all inside the Weights & Biases platform. Best when your ML team is already there and you want one account, one permissions model, one bill.
• Braintrust - the specialist pick. Eval-first workflow with dataset curation, side-by-side experiment comparison, human review, and CI release gating, on infrastructure built for agent-scale trace volumes. Best when shipping quality is an engineering discipline rather than an occasional check.
• Both - occasionally seen in large organisations where a platform team standardises on W&B while a product team runs Braintrust, but the overlap makes it a poor default.

Deciding factor to pick

Match your priority to the recommendation. This is the Weave vs Braintrust decision in one table:

Rule of thumb: Weave wins when evaluation is one job inside a bigger ML platform, Braintrust wins when evaluation is the job.

What each tool is

• W&B Weave is the LLM layer of the Weights & Biases platform. You add roughly one line of instrumentation and every model call is captured as a searchable, versioned, shareable trace. On top of that sit a weave.Evaluation primitive that runs a function across a dataset and aggregates scorer results into a comparable report, built-in scorers covering exact match, regex, model-graded, and embedding similarity, LLM-judge and code-based custom scorers, a playground for testing prompts and models against production traces, online evaluation that scores live traffic, and guardrails with prebuilt scorers for toxicity, bias, PII, hallucination, coherence, fluency, and context relevance. Weights & Biases was acquired by CoreWeave in a deal announced in March 2025 and completed on 5 May 2025, reported at around $1.7 billion, which put the platform inside a GPU cloud rather than an independent tooling vendor.
• Braintrust is a purpose-built evaluation and observability platform for AI products. Its centre of gravity is the eval loop: curate a dataset, define scoring functions in code, run experiments, compare them side by side, route ambiguous cases to human review, and gate the release on the result. It is framework-agnostic, offers a playground and an AI proxy, and runs traces on a custom-built datastore designed for the volume and shape of multi-step agent traces. Braintrust announced an $80 million Series B in February 2026 led by ICONIQ with participation from Andreessen Horowitz, Greylock, Basecase, and Elad Gil, reported at an $800 million valuation, with publicly named customers including Notion, Replit, Cloudflare, Ramp, and Stripe.

Weave vs Braintrust: head-to-head

Published pricing and feature tiers on both platforms move; treat the figures above as a snapshot taken in September 2026 and confirm on the vendors’ own pages before you commit budget.

Verdict by use case

Your ML team already runs W&B. Weave. The integration argument is real and it is not just about the invoice: shared projects, shared access control, and a single place where a fine-tuned model’s training run and the LLM application traces that use it sit side by side. Adding a second vendor to get a marginally better eval UI is a hard sell in that situation.

You are shipping an LLM product and quality is the constraint. Braintrust. The workflow is the product. Experiment comparison that a product manager can read, review queues for the outputs a scorer cannot settle, and a CI gate that fails the build on a regression are exactly the mechanics of eval driven development, and they are noticeably more polished here than in a general ML platform.

You need safety scoring out of the box. Weave. Its guardrails ship prebuilt scorers for toxicity, bias, PII, and hallucination, plus quality scorers for coherence, fluency, and context relevance. You can build all of these in Braintrust, but you are writing them.

You are cost-sensitive at low volume. Weave, or an open-source platform. The gap between a roughly $60 per month entry point and $249 per month matters at seed stage. If neither fits, the open-source field is genuinely competitive now - see Opik vs Langfuse vs Phoenix.

You have strict data-residency requirements. Braintrust for its on-prem and hybrid enterprise deployment, or self-hosted open source. Weave self-hosting is an enterprise conversation.

You are running agents with long multi-step traces. Braintrust. Its trace infrastructure was rebuilt specifically for that volume and shape, and agent traces break naive trace stores faster than anything else in this space.

Can you use them together?

Technically yes, practically rarely. The overlap covers tracing, datasets, scorers, and experiment comparison, so running both means paying twice for the same job and reconciling two dashboards when they disagree. The combination that does occur in the wild is organisational rather than technical: an ML platform team standardises on Weights & Biases for training and model management while a product engineering team adopts Braintrust for the application eval loop.

If you land there, pick one system of record for LLM traces and let the other be authoritative for its own domain. Two sources of truth for the same trace is how teams end up debugging their observability instead of their product.

Cost comparison

Both platforms meter primarily on data volume, which means your instrumentation and sampling choices drive the bill more than the plan you pick.

• W&B includes Weave tracing, evaluations, and scorers on its free tier with 1 GB per month of Weave data ingestion. Pro is listed at around $60 per month with 1.5 GB of Weave ingestion included and overage metered at $0.10 per MB, plus separate storage pricing. Sources differ on whether the headline Pro price is per seat or a base plan with seats included, so verify before you model the cost.
• Braintrust offers a free Starter tier with $10 in monthly model credits, 1 GB of processed data, 10,000 scores, and 14-day retention, and Pro at $249 per month with $100 in credits, 5 GB, 50,000 scores, 30-day retention, RBAC, and its Loop agent for eval iteration. Enterprise is custom-priced with on-prem or hosted deployment. Qualifying startups can get several months of Pro free.

The practical advice is the same for both: instrument everything in development, sample in production, and keep full-fidelity traces only for the paths you actually debug. Teams that trace 100% of production traffic at agent scale are the ones who get surprised by a bill.

Common pitfalls

• Buying a platform instead of building evals. Neither tool writes your dataset or defines what “good” means for your product. The platform is the easy 20% of the work.
• LLM-judging everything. Model-graded scorers are seductive and expensive. Use deterministic code scorers wherever the answer is checkable, and reserve judges for the genuinely subjective dimensions.
• Tracing 100% of production forever. Both platforms bill on ingested volume. Full-fidelity tracing is a development-time luxury, not a production default.
• Never re-baselining your scorers. A judge prompt calibrated on last quarter’s data drifts as your traffic changes. Re-validate judge agreement against human labels periodically.
• Confusing observability with evaluation. Traces tell you what happened; evals tell you whether it was good. If your dashboard is full of traces and empty of scored experiments, you have monitoring, not quality control. Our LangSmith vs Langfuse comparison covers the observability side of that split.

Related reading

• Braintrust vs LangSmith - the other half of the commercial eval platform decision
• Opik vs Langfuse vs Phoenix - the open-source alternatives if neither budget nor data residency works
• LangSmith vs Langfuse - observability-first platforms and where they overlap with evals

Getting help

We help Series A-C AI companies turn an eval platform subscription into an actual quality gate. A genai.qa Application QA Sprint builds the datasets, scorers, and CI wiring on Weave or Braintrust in five days, and a QA Program Design engagement makes sure the practice survives after we leave.

Book a free scope call.