Confident AI is built on DeepEval, an open-source evaluation framework, and focuses on pre-built metrics, multi-turn simulation, and red teaming. Braintrust integrates production tracing, evaluation, CI/CD quality gates, human review, and AI-assisted optimization into a single platform. This article compares Confident AI and Braintrust across evaluation depth, production workflows, pricing, and team fit so buyers can assess which platform fits how they build and ship AI products.
Confident AI is the commercial platform built on top of DeepEval, the open-source LLM evaluation framework. DeepEval handles metrics and testing logic locally, while Confident AI adds collaboration, dataset management, production tracing, and no-code workflows. Confident AI stands out for its broad pre-built metric coverage and end-to-end application testing, which help teams get started quickly but offer less flexibility as evaluation requirements become more domain-specific.
Braintrust is an AI evaluation and observability platform that connects development and production through a shared quality workflow. Tracing, evaluation, release checks, and prompt testing run in the same system, helping teams apply the same quality standards both before deployment and on live traffic. Braintrust also gives teams full control over scoring logic, with scorers that can be inspected, versioned, and improved alongside application code.
Both platforms cover LLM evaluation, but they differ in how evaluation connects to production workflows, release enforcement, and long-term quality control.
| Dimension | Braintrust | Confident AI |
|---|---|---|
| Best for | Eval-to-production workflows, CI/CD enforcement, and cross-functional quality review | Pre-built metric coverage, multi-turn simulation, and safety testing |
| Built-in metrics | 25+ built-in scorers in autoevals, plus custom code-based scorers, LLM-as-a-judge, and human review workflows | 50+ research-backed metrics through DeepEval |
| Trace-level scoring | Scores full execution traces across tool calls, retrieval, and multi-step workflows | Output-level scoring |
| AI assistant | Loop generates scorers, datasets, and prompt improvements from natural language | Not available |
| CI/CD quality gates | Native GitHub Action blocks merges on quality thresholds | CI/CD supported through DeepEval and pytest workflows, but no native PR-level merge blocking |
| Production-to-eval workflow | Production traces converted into reusable eval cases | Auto-curates datasets from traces, but the workflow is less direct than Braintrust's one-click conversion |
| Multi-turn simulation | Not built-in | Dynamic multi-turn conversation simulation |
| Red teaming and safety testing | Not built-in | Built-in red teaming and safety testing aligned to OWASP and NIST-oriented use cases |
| Drift detection | Online scoring and alerting on quality drops | Per-prompt and per-use-case drift tracking |
| Prompt management | Playground for side-by-side testing against production traces, with prompts and models in one place | Git-based branching and approval workflows |
| Query performance | Brainstore for fast querying on AI trace workloads | Standard infrastructure |
| Framework integrations | Native SDK integrations and OpenTelemetry support across major AI agent and testing frameworks | Integrations through DeepEval and OpenTelemetry |
| Free tier | 1M trace spans, 10K scores, 1 GB processed data, 14-day retention, unlimited users, projects, datasets, playgrounds, and experiments | 2 seats, 1 project, 5 test runs/week, 1 GB, 1-week retention |
| Paid pricing | $249/mo, 5 GB processed data, 50K scores, unlimited trace spans, custom charts, environments, and priority support | From $19.99/seat/month, 1 GB processed data, 5K eval metric runs |
| Enterprise | Self-hosting, hybrid deployment, SSO/SAML | Enterprise controls vary by plan; custom enterprise options available |
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Multi-turn simulation and red teaming: Confident AI generates and evaluates dynamic multi-turn conversations, with built-in red teaming aligned with the OWASP Top 10 and NIST AI RMF, which is useful for teams that need chatbot and safety testing in the same workflow, especially when security testing is required from the start.
Per-seat pricing at a small scale: Confident AI's entry pricing is lower for a solo developer or a very small team. Lower tracing costs can also make Confident AI easier to justify for teams with tight budgets and limited collaboration needs.
Braintrust is the stronger choice when the buying decision extends beyond evaluation methods into release control, production debugging, and continuous quality improvement.
Braintrust lets teams write, inspect, and version scoring logic through code-based scorers, LLM-as-a-judge setups, and custom eval functions stored alongside application code. Braintrust also scores full traces of tool calls, retrieval steps, and multi-step agent workflows, giving teams direct access to the logic behind each result. As evaluation requirements become more domain-specific, inspectable, and versioned scoring logic is easier to adapt than a workflow centered on pre-built metrics.
Braintrust's native GitHub Action runs evaluations on every pull request, posts a detailed score summary as a PR comment, and blocks the merge when scores drop below defined thresholds. Evaluation runs automatically on every PR, so a regression can't ship because someone forgot to run the eval suite. Confident AI supports CI/CD through DeepEval's pytest integration, which works well for running evals in a pipeline. Braintrust's GitHub Action automatically enforces quality at the PR level, while DeepEval's pytest approach requires teams to build their own enforcement logic to block merges.
Braintrust converts production traces into evaluation dataset entries with a single click. When a user reports a bad response or a quality alert fires, the team turns that failure into a regression test that runs on every future deployment. The evaluation dataset grows from real production failures over time, and each resolved incident strengthens the test coverage for the next release. Confident AI also connects production traces to evaluation datasets through auto-curation, but the process is slower than Braintrust's one-click workflow. Braintrust's shared data layer between tracing and evaluation removes the manual steps that slow down the process of turning a production failure into a regression test.
Loop, Braintrust's AI assistant, generates scorers from natural-language descriptions, creates evaluation datasets from production logs, identifies failure patterns across traces, and suggests prompt improvements based on the results. Custom metric creation that would normally take hours of implementation work takes minutes with Loop. Confident AI does not offer an equivalent AI assistant, so teams that build custom evaluation criteria must implement the logic manually or rely on DeepEval's existing metric library.
Braintrust keeps production traces, eval datasets, scorers, and release thresholds connected within a single system. The same scorers that evaluate a prompt change in the Playground also run in CI/CD and score live production traffic. Because the same scorers run everywhere, regressions surface before deployment, and production monitoring uses the same quality definitions that development testing relies on. Running separate tools for development evals and production monitoring often leads to distinct quality definitions, making regressions harder to catch and explain.
Braintrust supports a wide set of native integrations across AI frameworks and SDKs, along with OpenTelemetry support and Braintrust Gateway for unified model access, caching, and tracing. Brainstore also enables teams to query large AI trace workloads faster, which is helpful as production traffic grows and debugging depends on quick access to traces, scores, and recurring failure patterns. Topics, an ML-powered clustering feature, automatically surfaces user intents, sentiment shifts, and recurring issues across production traffic without manual log review.
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Confident AI starts at $19.99 per seat per month on the Starter plan, with tracing priced at $1 per GB-month. The free tier includes 2 seats, 1 project, 5 test runs per week, and 1 week of retention. Because pricing scales with seat count, total cost rises as more collaborators join the platform.
Braintrust Pro starts at $249 per month and includes unlimited users and 50K evaluation scores. Braintrust's free tier includes 1M trace spans, 10K scores, unlimited users, and 14-day retention. The flat-rate model means the platform fee stays the same as the team grows, making Braintrust easier to scale across engineering, product, and domain expertise.
Confident AI is cheaper for an individual user or a very small team. Braintrust becomes more cost-effective as collaboration expands because it does not add per-seat charges. Braintrust's free tier gives teams enough capacity to run meaningful evaluation workflows on production traffic, while Confident AI's free tier is better suited to limited testing and early exploration.
Choose Confident AI when the main requirement is broad pre-built metric coverage, multi-turn simulation, and red teaming, especially when per-seat pricing works for a small team. Confident AI is most relevant for teams that want to run evaluations against a large library of research-backed metrics without tying evaluation closely to release control.
Choose Braintrust when evaluation needs to connect directly to production improvement as prompts, models, and application behavior change. Braintrust is better suited to teams that want inspectable and versioned scoring logic, quality enforcement in release workflows through native CI/CD gates, and a direct path from production failures to regression tests that improve the system over time.
Airtable, Vercel, Stripe, Zapier, Instacart, and other top AI teams use Braintrust in production. Notion's AI team went from triaging 3 issues per day to 30 after adopting Braintrust's eval workflows. Braintrust's free tier gives teams enough room to start building evaluation coverage on real production traffic before committing to a paid plan. Start free with Braintrust
Braintrust is the stronger choice for most teams as it provides full control over scoring logic and connects evaluation directly to CI/CD enforcement, production tracing, and regression prevention. Confident AI can be a faster starting point when a team's requirements align closely with its pre-built metric library, but Braintrust becomes the stronger choice when evaluation needs to reflect domain-specific quality standards and influence what reaches production.
DeepEval is the open-source LLM evaluation framework that handles metrics and testing logic. Confident AI is the commercial platform built on top of DeepEval, adding collaboration, dataset management, production tracing, and no-code workflows. Teams already using DeepEval locally can also integrate with Braintrust through OpenTelemetry and SDK integrations without switching evaluation frameworks.
Braintrust is the strongest option for production AI applications because it supports the complete evaluation workflow from development through release and ongoing production monitoring. Production traces feed into eval datasets, CI/CD gates block regressions before deployment, and the same scorers can be used consistently across testing and production monitoring. Teams at Notion, Stripe, Vercel, and Zapier use Braintrust to ship and improve AI products in production.
Confident AI starts lower at $19.99 per seat per month, which can work well for an individual user or a very small team. Braintrust starts at $249 per month and includes unlimited users, which makes the pricing model easier to scale across larger teams. Braintrust's free tier also includes 1M trace spans, 10K scores, unlimited users, and 14-day retention, giving teams more room to evaluate real production traffic before upgrading.
Braintrust covers everything Confident AI offers for evaluation, and adds observability, CI/CD gating, human review, and production monitoring on a single platform. For teams using Confident AI mainly for evaluation, Braintrust provides strong support for built-in scorers, LLM-as-a-judge, and custom scoring logic, while also adding the production workflows that make evaluation enforceable over time. Braintrust becomes the stronger long-term choice when the goal extends beyond standalone testing into continuous quality improvement.