Sold by: Braintrust
Deployed on AWS
Braintrust is the AI observability platform. By connecting evals and observability in one workflow, Braintrust gives builders the visibility to understand how AI behaves in production and the tools to improve it.
Teams at Lovable, Notion, Stripe, Zapier, Vercel, and Ramp use Braintrust to compare models, test prompts, and catch regressions-turning production data into better AI with every release.
4.4
Overview
Braintrust is the agent observability platform. By actively applying intelligence to agent traces and automatically surfacing the most critical patterns, Braintrust gives teams the visibility to understand how agents behave in production and the tools to improve them.
Teams at Notion, Stripe, Box, OpenAI, and Cloudflare use Braintrust to trace their agents, find the issues in their observability data, and run evals that tell them how to improve.
Braintrust provides:- Data + Instrumentation: turning traces and outputs into structured eval datasets.
- Active observability: autonomous and continuous intelligence on top of your observability data, surfacing the patterns that matter.
- Evals: defining what "good" means, measure against it, and determine how to improve your agents.
- Iteration: comparing prompts, models, and versions to improve quality, cost-effectively.
- Quality gates: automated checks that prevent regressions from reaching production
- Improvement loop: AI-powered tools that speed up the entire development cycle.
Highlights
- Loop: A specialized agent for querying observability and trace data. Ask follow-up questions in plain language and get answers backed by your production data.
- Brainstore: Braintrust's database for agent observability. Search and filter millions of traces in under a second, including full-text search across prompts and error messages.
- Scalable architecture with enterprise-grade security: Braintrust is RBAC, SSO, SAML, HIPAA and SOC II compliant and offers a hybrid deployment model for customers with certain data requirements.
Details
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Deployed on AWS
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Pricing
Pricing is based on the duration and terms of your contract with the vendor, and additional usage. You pay upfront or in installments according to your contract terms with the vendor. This entitles you to a specified quantity of use for the contract duration. Usage-based pricing is in effect for overages or additional usage not covered in the contract. These charges are applied on top of the contract price. If you choose not to renew or replace your contract before the contract end date, access to your entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/12 months |
|---|---|---|
Enterprise SaaS License | Contact Us For Private Offer Pricing at info@braintrust.dev. | $250,000.00 |
Dimension | Description | Cost/unit |
|---|---|---|
Usage | Usage-based credits for data volume, scores, and data retention | $0.01 |
Dimensions summary
Braintrust offers two ways to buy. The Usage dimension bills you with credits that scale by consumption. You pay based on data volume processed, the number of scores run against your outputs, and how long data is retained. This lets your cost move up or down with actual activity. The Enterprise SaaS License is a fixed-term contract set through a private offer. You arrange this pricing directly with the vendor at info@braintrust.dev, which suits high-volume or privacy-sensitive deployments. Together, these dimensions let you choose between pay-as-you-go usage credits and a negotiated committed license.
Top-of-mind questions for buyers
Usage credits cover three metered activities. Processed data is measured in gigabytes per month. Scores count each output graded by an LLM-as-judge, automated check, or custom scorer, billed per 1,000 scores. Data retention is measured per gigabyte per month beyond the included period. Model credits also apply, then token rates.
Three metrics combine on one invoice: processed data, scores run against outputs, and data retention. Data volume tends to dominate for high-traffic applications with heavy tracing. Scores drive cost when you grade a large share of outputs. Retention grows for teams keeping data long past the included period.
The Usage dimension meters actual consumption. Charges rise and fall with data processed, scores run, and retention used, so cost tracks activity. The Enterprise SaaS License is a fixed-term contract priced through a private offer. You arrange terms directly with the vendor, which suits high-volume or privacy-sensitive deployments.
www.braintrust.dev
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Delivery details
Software as a Service (SaaS)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
Resources
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Support
Vendor support
Email us at info@braintrust.dev .
Join us on Discord!
https://www.braintrust.dev/contact
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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Customer reviews
Roopam s.
Braintrust Makes AI Output Evaluation Fast and Easy
Reviewed on Aug 10, 2026
Review provided by G2
What do you like best about the product?
The best thing about Braintrust is how easy it makes it to evaluate and improve AI outputs at one place. It saves lots of time when testing different prompts and models and helps the team make better decisions
What do you dislike about the product?
The learning curve can be a little steep in the beginning. Some features take time to understand and setup properly
What problems is the product solving and how is that benefiting you?
It helps us keep AI testing and evaluation more organized. We can quaickly compare outputs and identify what need improvements
LOKESH G.
Powerful LLM Evaluation & Monitoring for Real-World AI Apps
Reviewed on Aug 08, 2026
Review provided by G2
What do you like best about the product?
It has a strong focus on evaluating and improving AI applications for real-world use cases. The tools for tracing, testing, evaluation, and monitoring LLM applications make it easier to spot quality issues, compare model performance, and continuously refine AI systems both before and after production deployment.
What do you dislike about the product?
The main thing I dislike about Braintrust is that setting up advanced evaluations and tracing can take a while, especially when you’re working on more complex AI applications. I’d also appreciate more customization options, along with clearer pricing as usage grows and the number of evaluations increases.
What problems is the product solving and how is that benefiting you?
Braintrust helps address the challenge of **testing, evaluating, debugging, and monitoring AI applications as they move into production**. It gives me clearer visibility into model performance and makes it easier to spot problems like poor responses, regressions, and inconsistent behavior. For me, that means **less manual testing, higher AI quality, faster debugging, and more confidence when deploying to production**.
Lakshmidas P.
Braintrust Makes It Easy to Compare AI Responses and Pick the Best
Reviewed on Aug 07, 2026
Review provided by G2
What do you like best about the product?
I like Braintrust’s ability to provide different AI responses in one place, so I can compare them and choose the best result based on my requirements.
What do you dislike about the product?
Sometimes it takes a bit longer for them to understand my requirements.
What problems is the product solving and how is that benefiting you?
Braintrust makes it easier to find the best prompts in one place. It help me to test and improve AI quality.
Subhashree S.
Streamlined AI Model Evaluation with Great Experiment Tracking
Reviewed on Aug 07, 2026
Review provided by G2
What do you like best about the product?
What I like best about Braintrust is its structured workflow for evaluating AI models and prompts. It makes it easy to create datasets, run automated evaluations, compare model outputs, and track performance over time. The platform integrates well into existing AI development workflows, helping teams identify regressions early and improve model quality before deployment. I also appreciate its developer-friendly SDKs, experiment tracking, and clear visualization of evaluation results, which make iterative AI development much more efficient.
What do you dislike about the product?
Braintrust has a bit of a learning curve, especially when setting up evaluation pipelines and understanding the best practices for organizing datasets and experiments. Some advanced features require additional configuration, and first-time users may need more onboarding resources. Larger evaluation runs can also take time depending on dataset size and model complexity, but these are relatively minor compared to the overall value the platform provides.
What problems is the product solving and how is that benefiting you?
Braintrust helps solve the challenge of evaluating and improving AI applications in a consistent, measurable way. Instead of relying on manual testing, it automates model evaluations, tracks performance across prompt and model changes, and quickly identifies regressions. This has improved the reliability of AI-powered features, reduced debugging time, accelerated experimentation, and increased confidence when deploying updates to production.
Muhammad O.
Easy-to-Use AI Observability Platform for Faster Debugging
Reviewed on Aug 07, 2026
Review provided by G2
What do you like best about the product?
What I like most about Braintrust is how easy it is to monitor and evaluate AI applications from a single dashboard. The interface is clean, and the tracing and logging features are well organized, which makes it straightforward to understand application behavior and spot issues during development. Overall, it feels lightweight while still offering the tools I need for observability and debugging.
What do you dislike about the product?
What I dislike about Braintrust is that some of the more advanced features can take a bit of time to understand, especially if you’re new to AI observability. I think the onboarding could benefit from a few more guided examples to help you get oriented faster. That said, once you’re familiar with the workflow, the overall experience feels smooth, efficient, and productive.
What problems is the product solving and how is that benefiting you?
Braintrust helps me identify issues in AI applications by bringing clear tracing, logging, and evaluation tools together in one place. It makes debugging faster and gives better visibility into how the application behaves. As a result, I spend less time investigating errors, which helps speed up development and testing.