TL;DR — Confident AI vs Braintrust in 2026
Confident AI is the best alternative to Braintrust in 2026 because while both platforms offer strong observability and evaluation foundations — tracing, alerting, scoring, annotation — Confident AI goes deeper on evaluation. It tests your actual AI application end-to-end via HTTP, ships 50+ built-in metrics, offers multi-turn simulation, drift detection, and red teaming. Braintrust's playground is polished but limited to prompt-level testing with custom scorers only.
Both platforms do observability well. The difference is evaluation depth — end-to-end application testing, 50+ built-in metrics, multi-turn simulation, drift detection, and red teaming. Choose Braintrust if prompt-level testing is all you need. Choose Confident AI if you need to test your actual AI application.
Confident AI and Braintrust are both LLM evaluation and observability platforms. Both offer production tracing, alerting, scoring, annotation, dashboards, and a playground for experimentation. On the observability side, the two platforms are genuinely comparable — span-level tracing, cost and latency tracking, quality-aware alerting, and conversation grouping all work well on both.
The difference is on the evaluation side — specifically, what you can evaluate and how deep that evaluation goes.
Braintrust offers a clean evaluation playground for comparing prompt and model combinations, CI/CD gates for catching regressions, and custom scorer workflows. For teams focused on prompt optimization, it covers that well.
Confident AI goes further. It evaluates your actual AI application end-to-end via HTTP (not just prompts in isolation), simulates multi-turn conversations, tracks quality drift per prompt and use case, and includes red teaming for safety testing — with 50+ built-in metrics instead of requiring custom scorer implementation for every use case. On the observability side, Confident AI adds drift detection on top of the shared foundation — tracking how specific prompts and use cases perform over time, not just aggregate quality. The initial setup takes longer, but once an engineer configures the HTTP connection, the entire team — PMs, QA, domain experts — runs evaluation cycles independently.
In this guide, we'll break down the differences across features, pricing, and use cases to help you decide.
How is Confident AI Different?
1. End-to-end application testing, not just prompt scoring
This is the fundamental difference. Braintrust evaluates prompts in its playground — you input a prompt, select a model, and score the output. Confident AI evaluates your actual AI application via HTTP, the same way a user would interact with it in production.
Why this matters: your AI application isn't just a prompt. It's a pipeline — retrieval, routing, tool selection, generation, post-processing. Evaluating the prompt in isolation misses every failure mode that happens before or after the LLM call. Confident AI's AI connections send real HTTP requests to your application, testing the entire pipeline end-to-end.
The trade-off is setup time. Braintrust's playground works out of the box with zero configuration. Confident AI requires an engineer to configure the initial HTTP connection. But once that's done, non-technical teams run full evaluation cycles against your actual application independently — no recreating your agent on the platform just for testing. It's a longer initial setup for significantly better long-term iteration.
2. 50+ built-in metrics vs custom scorers only
Braintrust requires you to implement custom scorers for every evaluation metric — faithfulness, hallucination detection, relevance, bias, toxicity, tool selection accuracy, planning quality. Each one is a function you write and maintain.
Confident AI ships 50+ research-backed metrics out of the box through DeepEval, covering agents, chatbots, RAG, single-turn, multi-turn, and safety. Metrics are validated against human judgment and used by teams at OpenAI, Google, and Microsoft. You can still add custom metrics, but you don't have to build the foundation from scratch.
3. Multi-turn simulation
Braintrust has no multi-turn simulation. Testing a chatbot or conversational agent means manually prompting through conversations or replaying historical logs.
Confident AI generates realistic multi-turn conversations with tool use and branching paths — simulating the dynamic user interactions your AI handles in production. What takes 2-3 hours of manual prompting takes minutes. This is critical for agents, chatbots, and any multi-turn use case where failures emerge across turns, not within a single response.
4. Drift detection on top of shared observability
Both platforms do observability well — tracing, alerting, dashboards, cost tracking. Where Confident AI adds a layer is drift detection: tracking quality changes per prompt and per use case over time.
When a model update degrades your "refund request" workflow without affecting "order status" queries, drift detection pinpoints the issue. Instead of investigating aggregate score drops across your entire application, you see exactly which use case degraded, when it started, and what changed. Braintrust's alerting catches score drops at the aggregate level, but can't isolate which prompt or use case caused them.
5. Cross-functional teams own quality
Both platforms have accessible playground UIs. The difference is what non-technical teams can do beyond the playground.
On Braintrust, PMs can test prompts in the playground and review datasets. On Confident AI, PMs upload datasets, trigger full end-to-end evaluations against your production AI application, review results with 50+ metrics, and annotate outputs — all without engineering involvement after initial setup. QA teams own regression testing. Domain experts annotate production traces. Engineering sets up the connection, then steps back.
Features and Functionalities
Confident AI and Braintrust share a strong observability and evaluation foundation — tracing, alerting, scoring, annotation, dashboards, and a playground all work on both platforms. Where they diverge is evaluation depth: end-to-end testing, built-in metrics, multi-turn simulation, drift detection, and red teaming.
Confident AI | Braintrust | |
|---|---|---|
LLM Observability Trace AI agents, track latency and cost, and more | ||
Quality-aware alerting Alerts on eval score drops, not just latency | ||
End-to-end app testing Evaluate your actual AI application via HTTP | ||
Drift detection Track quality changes across prompts and use cases | ||
Multi-turn simulation Generate and evaluate dynamic multi-turn conversations | ||
Built-in LLM metrics Research-backed metrics available out of the box | 50+ metrics | Custom scorers only |
Red teaming Safety and security testing | ||
Single-turn evals Supports evaluation workflows for prompt-response pairs | ||
Regression testing Side-by-side performance comparison across versions | ||
AI playground No-code workflows to run evaluations | ||
Online evals Run evaluations as traces are logged | ||
Human annotation Annotate traces and align with evaluation metrics | ||
Dataset management Supports datasets for both single and multi-turn use cases | ||
Prompt versioning Manage single-text and message-prompts | ||
Custom dashboards Build quality KPI dashboards |
LLM Observability
Both platforms offer strong production observability — span-level tracing, alerting, cost/latency tracking, conversation grouping, and dashboards. The observability table below is mostly green on both sides. Confident AI's addition is drift detection: tracking quality per prompt and use case over time.
Confident AI | Braintrust | |
|---|---|---|
Free tier Based on monthly usage | Unlimited seats, 10k traces, 1 month data retention | Unlimited seats, 1k traces/month, basic features |
Core Features | ||
Integrations One-line code integration | ||
OTEL Instrumentation OTEL integration and context propagation for distributed tracing | ||
Graph Visualization A tree view of AI agent execution for debugging | ||
Metadata logging Log any custom metadata per trace | ||
Trace sampling Sample the proportion of traces logged | ||
Online evals Run live evals on incoming traces, spans, and threads | ||
Custom span types Customize span classification for better analysis on the UI | ||
Dashboarding View trace-related data in graphs and charts | ||
Conversation tracing Group traces in the same session as a thread | ||
User feedback Allow users to leave feedback via APIs or on the platform | ||
Drift detection Track quality changes per prompt and use case over time | ||
Quality-aware alerting Alerts on eval score drops |
LLM Evals
Both platforms support evaluation workflows. Confident AI's key advantages are end-to-end application testing, built-in metrics, and multi-turn simulation.
Confident AI | Braintrust | |
|---|---|---|
Free tier Based on monthly usage | Unlimited offline evals, online evals free for first 14-days | Free tier includes playground and basic scoring |
Core Features | ||
Experimentation on multi-prompt AI apps 100% no-code eval workflows on multiple versions of your AI app | ||
Eval on AI connections Reach any AI app through HTTP requests for experimentation | ||
Online and offline evals Run metrics on both production and development traces | ||
Multi-turn simulations Simulate user conversations with AI conversational agents | ||
Multi-turn dataset format Scenario-based datasets instead of input-output pairs | ||
Testing reports & regression testing Allow regression testing and stakeholder sharable testing reports | ||
LLM Metrics Supports LLM-as-a-judge metrics for AI agents, RAG, multi-turn, and custom ones | 50+ metrics for all use cases, research-backed, powered by DeepEval | Custom scorers only — every metric requires manual implementation |
Non-technical friendly test case format Upload CSVs as datasets that do not assume technical knowledge | ||
AI app & Prompt Arena Compare different versions of prompts or AI apps side-by-side | Only for single prompts | |
CI/CD evaluation gates Catch regressions before deployment |
Human Annotations
Both platforms support annotations with scoring and queues.
Confident AI | Braintrust | |
|---|---|---|
Free tier Based on monthly usage | Unlimited annotations and annotation queues, forever data retention | Annotations included in free tier |
Core Features | ||
Reviewer annotations Annotate on the platform | ||
Annotations via API Allow end users to send annotations | ||
Custom annotation criteria Allow annotations to be of any criteria | ||
Annotation on all data types Annotations on traces, spans, and threads | ||
Custom scoring system Allow users to define how annotations are scored | Yes, either thumbs up/down or 5 star rating system | Yes, numerical scoring |
Curate dataset from annotations Use annotations to create new rows in datasets | ||
Export annotations Export via CSV or APIs | ||
Eval alignment Statistics for how well LLM metrics align with human annotation |
AI Red Teaming
Confident AI offers red teaming for AI applications — Braintrust does not. Red teaming automatically scans for security and safety vulnerabilities in your AI system in under 10 minutes.
Confident AI | Braintrust | |
|---|---|---|
Free tier Based on monthly usage | Red teaming on enterprise-only | Not supported |
Core Features | ||
LLM Vulnerabilities Library of prebuilt vulnerabilities such as bias, PII leakage, etc. | ||
Adversarial Attack Simulations Simulate single and multi-turn attacks to expose vulnerabilities | ||
Industry frameworks and guidelines OWASP Top 10, NIST AI, etc. | ||
Customizations Custom vulnerabilities, frameworks, and attacks | ||
Red team any AI app Reach AI apps through the internet to red team | ||
Purpose-specific red teaming Get use case tailored attacks based on AI purpose | ||
Risk assessments Generate risk assessments that contain things like CVSS scores |
Pricing
Both platforms offer free tiers, but pricing diverges significantly as teams scale.
Confident AI
Confident AI charges based on usage and user seats. Usage is measured in GB-months at $1 per unit — representing either one GB of data ingested or one GB of data retained for one month, with flexible allocation between the two.
- Free: Unlimited seats, 10k traces, 1 GB-month, 1-week data retention
- Starter: $19.99/seat/month, 1 GB-month included, unlimited data retention
- Premium: $49.99/seat/month, 15 GB-months included, unlimited data retention
- Team/Enterprise: Custom pricing with volume discounts on tracing
Braintrust
Braintrust offers a free tier, then jumps to $249/month for its Growth plan — no mid-tier option for growing teams.
- Free: Unlimited seats, limited features
- Growth: $249/month
- Enterprise: Custom pricing
The key pricing differences:
- No mid-tier on Braintrust. The jump from $0 to $249/month creates friction for teams that have outgrown the free tier but don't need enterprise features. Confident AI's Starter plan at $19.99/seat/month fills this gap.
- Tracing costs. Braintrust charges $3/GB for ingestion and retention. Confident AI charges $1/GB-month — 3x cheaper at the same volume.
- What you get for the price. Confident AI's paid plans include end-to-end testing, 50+ metrics, multi-turn simulation, drift detection, and red teaming. Braintrust's paid plans expand the same prompt-level evaluation capabilities.
Security and Compliance
Both platforms are enterprise-ready with standard security certifications.
Confident AI | Braintrust | |
|---|---|---|
Data residency Regional deployment options | US, EU, and Australia | US and EU |
SOC II Audit-ready compliance | ||
HIPAA Healthcare compliance | ||
GDPR EU data protection | ||
2FA Two-factor authentication | ||
Social Auth (e.g. Google) Simplified authentication | ||
Custom RBAC Fine-grained data access control | Team plan or above | Enterprise only |
SSO Standardized authentication | Team plan or above | Enterprise only |
On-Prem Deployment Self-hosted for strict data requirements | Enterprise only | Enterprise only |
Confident AI offers RBAC and SSO on its Team plan — Braintrust gates these behind enterprise pricing.
Why Confident AI is the best Braintrust Alternative
Both platforms are strong on observability — tracing, alerting, dashboards, and cost tracking work well on both. The comparison comes down to evaluation depth and what happens beyond prompt-level testing.
Confident AI delivers more ROI by:
- Testing your actual AI application, not just prompts. End-to-end evaluation via HTTP means you catch failures in retrieval, routing, tool selection, and post-processing — not just generation. Braintrust evaluates prompts in isolation and can't test the full pipeline.
- Shipping with 50+ metrics out of the box. Research-backed metrics for agents, chatbots, RAG, and safety — no custom scorer implementation required. Teams evaluate on day one instead of spending weeks building a metric library.
- Compressing multi-turn testing from hours to minutes. Multi-turn simulation generates realistic conversations with tool use and branching paths automatically. Braintrust has no multi-turn simulation.
- Tracking quality drift at the use case level. Drift detection tells you when your "refund request" workflow degraded after a model update, even if your aggregate scores look fine. Braintrust's alerting catches score drops but can't isolate which use case caused them.
- Enabling the whole team to own quality. After a one-time engineering setup, PMs, QA, and domain experts run full evaluation cycles independently. The longer initial setup pays for itself through faster long-term iteration.
Finom, a European fintech platform serving 125,000+ SMBs, cut agent improvement cycles from 10 days to 3 hours after adopting Confident AI. Before, every prompt change and dataset update required filing an engineering ticket — product managers with deep knowledge of customer intent were locked out of the evaluation loop entirely. With Confident AI's end-to-end testing, their product team now evaluates the full agentic system — tools, sub-agents, MCP servers, and all — without recreating it on the platform. No engineering bottleneck, no isolated prompt testing. Finom estimates this eliminated roughly €500K in annual engineering costs that would have been spent on dedicated evaluation engineers.
When Braintrust Might Be a Better Fit
Braintrust excels in specific scenarios:
- If prompt optimization is your primary use case: Braintrust's playground is clean, fast, and immediately accessible for comparing prompt and model combinations. If your evaluation needs don't extend beyond prompt scoring with CI/CD gates, it covers that well.
- If you need a non-technical playground with zero setup: Braintrust's dataset editor and playground work out of the box without engineering configuration. Confident AI requires an initial HTTP connection setup by engineering — but once that's done, non-technical users can run full end-to-end evaluations against your actual AI application independently, indefinitely. It's a longer initial setup for significantly better long-term iteration.
Frequently Asked Questions
Is Confident AI better than Braintrust?
Confident AI is better than Braintrust for teams that need more than prompt-level evaluation. It tests your actual AI application end-to-end via HTTP, ships 50+ built-in metrics, offers multi-turn simulation, drift detection, and red teaming — none of which Braintrust provides. Braintrust is a reasonable choice for teams whose evaluation needs are limited to prompt scoring in a playground with CI/CD gates.
Is Confident AI cheaper than Braintrust?
Yes. Confident AI's Starter plan is $19.99/seat/month — Braintrust jumps from free to $249/month with no mid-tier option. Tracing costs $1/GB-month on Confident AI vs $3/GB on Braintrust. At the same usage volume, Confident AI is significantly cheaper while including more evaluation capabilities.
Can non-technical teams use Braintrust?
Braintrust's playground is accessible to non-technical users for prompt testing and dataset review. The limitation is scope — non-technical users can't trigger evaluations against your actual AI application the way they can on Confident AI. Confident AI's end-to-end testing via AI connections lets PMs, QA, and domain experts run full evaluation cycles, manage datasets, and annotate production traces independently after initial engineering setup.
Does Braintrust support multi-turn evaluation?
Braintrust has no multi-turn simulation. Testing conversational AI on Braintrust means manually prompting through conversations or replaying historical logs. Confident AI generates realistic multi-turn conversations with tool use and branching paths automatically, compressing hours of manual testing into minutes.
Which is better for evaluating AI agents — Confident AI or Braintrust?
Confident AI is better for AI agent evaluation. It evaluates individual tool calls, reasoning steps, and retrieval within a single agent trace — scoring each decision point independently. Multi-turn simulation automates agent conversation testing. Braintrust has no agent-specific evaluation capabilities and can't test multi-step agent workflows end-to-end.
Which is better for evaluating RAG applications — Confident AI or Braintrust?
Confident AI is stronger for RAG evaluation. It offers dedicated retrieval and generation metrics — faithfulness, hallucination detection, context relevancy, retrieval precision — out of the box. Evaluations can target individual retrieval or generation spans within traces, isolating whether failures stem from retrieval quality or generation logic. Braintrust requires building custom scorers for each RAG metric and can't evaluate your actual RAG pipeline end-to-end.
Does Confident AI support drift detection?
Yes. Confident AI tracks quality changes per prompt and per use case over time. When a model update or prompt change degrades one workflow without affecting others, drift detection pinpoints the issue — turning aggregate score drops into actionable, use-case-specific insights. Braintrust does not offer drift detection.
Which is better for enterprise — Confident AI or Braintrust?
Confident AI offers RBAC and SSO on its Team plan — Braintrust gates these behind enterprise pricing. Confident AI supports regional deployments across the US, EU, and Australia, with on-premises deployment for strict data requirements. Enterprise customers include Panasonic, Amazon, BCG, and CircleCI.