Just In: New Slack Community! Connect with AI engineers building with Confident AI, TL;DR — Confident AI vs Braintrust in 2026
Confident AI is the best alternative to Braintrust in 2026 because it evaluates your actual AI application end-to-end via HTTP — not just prompts in isolation. It ships 50+ built-in metrics, multi-turn simulation, git-based prompt management with branching and approval workflows, quality-aware alerting with drift detection, and red teaming. Braintrust's playground is polished but limited to prompt-level testing with custom scorers only, and its pricing jumps from $0 to $249/month with tracing at 3x the cost.
Other alternatives include:
- LangSmith — Native LangChain tracing with annotation workflows, but evaluation depth drops outside the LangChain ecosystem and there are no cross-functional workflows.
- Langfuse — Open-source and self-hostable tracing, but no built-in evaluation metrics, no multi-turn support, and no non-technical workflows.
Braintrust evaluates prompts in isolation — it can't test your application as-is via HTTP for end-to-end evaluation. No multi-turn simulation, no git-based prompt management, and a steep pricing jump from $0 to $249/month. Confident AI tests your actual AI application, ships 50+ metrics out of the box, and closes the production-to-development loop with drift detection and auto-curated datasets. Pick Confident AI if you need end-to-end application testing, evaluation depth beyond prompt scoring, and production quality monitoring in one platform.
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), ships 50+ research-backed metrics out of the box, simulates multi-turn conversations, tracks quality drift per prompt and use case, manages prompts with git-style branching and approval workflows, and includes red teaming for safety testing. The initial setup takes longer, but once an engineer configures the HTTP connection, the entire team — PMs, QA, domain experts — runs evaluation cycles independently.
How is Confident AI Different?
1. Drift detection, production-to-eval pipeline, and safety monitoring
Both platforms offer tracing and alerting. Confident AI adds drift detection on top — 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, you see exactly which use case degraded, when it started, and what changed.
- Automatic dataset curation turns production traces into evaluation datasets. When quality degrades, the responses that caused it feed directly into the next test cycle — so your test coverage evolves alongside real usage instead of relying on static datasets that go stale.
- Safety monitoring detects toxicity, bias, and PII leakage on production traffic continuously. Braintrust does not offer safety monitoring at the time of writing.
The result is a closed loop: production traces → evaluations → alerts → auto-curated datasets → next test cycle. Both platforms trace. Confident AI turns traces into quality improvements.
2. End-to-end evaluation depth with cross-functional workflows
This is the fundamental evaluation difference. Braintrust evaluates prompts in its playground — you input a prompt, select a model, and score the output. At the time of writing, Braintrust cannot test your actual AI application as-is via HTTP. Confident AI can.
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.
Braintrust requires custom scorer implementation for every evaluation metric. Confident AI ships 50+ research-backed metrics out of the box, open-source through DeepEval, covering agents, chatbots, RAG, single-turn, multi-turn, and safety. Teams evaluate on day one instead of spending weeks building a metric library.
Multi-turn simulation generates realistic conversations with tool use and branching paths — simulating the dynamic user interactions your AI handles in production. At the time of writing, Braintrust does not offer multi-turn simulation. What takes 2-3 hours of manual prompting takes minutes. Red teaming covers PII leakage, prompt injection, bias, and jailbreaks based on OWASP Top 10 for LLM Applications and NIST AI RMF — no separate vendor needed.
The trade-off is setup time. Braintrust's playground works out of the box. Confident AI requires an engineer to configure the initial HTTP connection. But once that's done, 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. QA teams own regression testing. Domain experts annotate production traces. Engineering sets up the connection, then steps back.
Finom, a European fintech platform serving 125,000+ SMBs, cut agent improvement cycles from 10 days to 3 hours after adopting Confident AI. Their product team now evaluates the full agentic system — tools, sub-agents, MCP servers, and all — without recreating it on the platform. Finom estimates this eliminated roughly €500K in annual engineering costs.
3. Git-based prompt management with automated evaluation
Braintrust offers prompt versioning and a playground for comparing prompt variations. Confident AI adds the workflow layer that turns prompt editing into a managed development process:
- Branching — multiple engineers experiment on the same prompt in parallel branches without overwriting each other. Braintrust uses linear versioning.
- Pull requests and approval workflows — reviewers see diffs and evaluation results before approving changes. Full audit trail.
- Eval actions — automated evaluation suites trigger on every commit, merge, or promotion. A prompt change that degrades faithfulness gets flagged before it ships.
- Production prompt monitoring — 50+ metrics tracked per prompt version over time, with drift detection and alerting when a version starts degrading.
Features and Functionalities
Confident AI | Braintrust | |
|---|---|---|
LLM Observability Trace AI agents, track latency, cost, and quality | ||
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 eval metrics Research-backed metrics available out of the box | 50+ metrics | Custom scorers only |
Git-based prompt management Branching, PRs, approval workflows, eval actions | ||
Cross-functional workflows PMs and QA run evals without engineering | Limited | |
Production-to-eval pipeline Traces auto-curate into evaluation datasets | ||
Red teaming Safety and security testing | ||
Safety monitoring Toxicity, bias, PII detection on production traffic | ||
Regression testing CI/CD quality gates with regression tracking | ||
Single-turn evals Evaluation workflows for prompt-response pairs | ||
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 Datasets for both single and multi-turn use cases | ||
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. Confident AI adds drift detection and automatic dataset curation from production traces.
Confident AI | Braintrust | |
|---|---|---|
Free tier Based on monthly usage | 2 seats, 1 project, 1 GB-month, 1 week 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 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 analysis | ||
Custom dashboards Build dashboards around quality KPIs for your use cases | ||
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 | ||
Automatic dataset curation Production traces auto-curate into eval datasets | ||
Safety monitoring Toxicity, bias, PII detection on production traffic |
LLM Evaluation
Confident AI ships 50+ research-backed metrics out of the box and lets PMs, QA, and domain experts run full evaluation cycles independently — no engineer on the shoulder required. Teams test their actual AI application end-to-end via HTTP through AI connections, not a recreated subset of prompts in a playground. Braintrust evaluates prompts in isolation through its playground — teams have to recreate their application on Braintrust's platform rather than testing the actual app they ship.
Confident AI | Braintrust | |
|---|---|---|
Free tier Based on monthly usage | 5 test runs/week, unlimited online evals | Free tier includes playground and basic scoring |
Core Features | ||
LLM metrics Research-backed metrics for agents, RAG, multi-turn, and safety | 50+ metrics, open-source through DeepEval | Custom scorers only — every metric requires manual implementation |
Cross-functional eval workflows PMs and QA run evals via HTTP, no code | Limited | |
Eval on AI connections Test your actual AI application via HTTP | ||
Online and offline evals Run metrics on both production and development traces | ||
Multi-turn simulation Generate realistic conversations with tool use and branching paths | ||
Multi-turn dataset format Scenario-based datasets instead of input-output pairs | ||
Human metric alignment Statistically align automated scores with human judgment | ||
Production-to-eval pipeline Traces auto-curate into evaluation datasets | ||
Testing reports and regression testing CI/CD quality gates with regression tracking | ||
Error analysis to LLM judges Auto-categorize failures from annotations, create automated metrics | ||
Non-technical test case format Upload CSVs as datasets without technical knowledge | ||
AI app and 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 |
Prompt Management
Braintrust offers prompt versioning and a playground. Confident AI adds git-based management with branching, approval workflows, and eval actions that trigger evaluations on every prompt change.
Confident AI | Braintrust | |
|---|---|---|
Free tier Based on monthly usage | 1 prompt, unlimited versions | Prompts included in free tier |
Core Features | ||
Text and message prompt format Strings and list of messages in OpenAI format | ||
Custom prompt variables Variables interpolated at runtime | ||
Prompt branching Git-style branches for parallel experimentation | ||
Pull requests and approval workflows Review diffs and eval results before merging | ||
Eval actions Automated evaluation triggered on commit, merge, or promotion | ||
Full-surface prompt editor Model config, output format, tool definitions, 4 interpolation types | Limited | |
Prompt versioning and labeling Promote versions to environments | ||
Manage prompts in code Use, upload, and edit prompts via APIs | ||
Run prompts in playground Compare prompts side-by-side | ||
Link prompts to traces Find which prompt version was used in production | ||
Production prompt monitoring Quality metrics tracked per prompt version over time | ||
Prompt drift detection Alerting on quality degradation per prompt version |
Human Annotations
Both platforms support annotations with scoring and queues.
Confident AI | Braintrust | |
|---|---|---|
Free tier Based on monthly usage | Unlimited annotations and queues | 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 Annotations of any criteria | ||
Annotation on all data types Annotations on traces, spans, and threads | ||
Custom scoring system Define how annotations are scored | Thumbs up/down or 5-star rating | Numerical scoring |
Curate dataset from annotations Use annotations to create new dataset rows | ||
Export annotations Export via CSV or APIs | ||
Error analysis Auto-detect failure modes from annotations and recommend metrics | ||
Eval alignment Surface TP, FP, TN, FN to align automated metrics with human judgment | ||
Cross-functional annotation access PMs and domain experts annotate without engineering | Limited |
AI Red Teaming
Confident AI offers native red teaming for AI applications. At the time of writing, Braintrust does not offer red teaming capabilities.
Confident AI | Braintrust | |
|---|---|---|
Free tier Based on monthly usage | Enterprise only | Not supported |
Core Features | ||
LLM vulnerabilities Prebuilt vulnerability library — bias, PII leakage, jailbreaks, etc. | ||
Adversarial attack simulations Single and multi-turn attacks to expose vulnerabilities | ||
Industry frameworks OWASP Top 10, NIST AI RMF | ||
Customizations Custom vulnerabilities, frameworks, and attacks | ||
Red team any AI app Reach AI apps through HTTP to red team | ||
Purpose-specific red teaming Use-case-tailored attacks based on AI purpose | ||
Risk assessments Generate risk assessments with CVSS scores |
Pricing
Both platforms offer free tiers, but pricing diverges significantly as teams scale.
Plan | Confident AI | Braintrust |
|---|---|---|
Free | $0 — 2 seats, 1 project, 1 GB-month, 5 test runs/week | $0 — unlimited seats, limited features |
Starter / Growth | $19.99/seat/month — $1/GB-month, unlimited traces | $249/month |
Premium | $49.99/seat/month — 15 GB-months included, unlimited traces | N/A |
Team | Custom — 10 users, 75 GB-months, unlimited projects | N/A |
Enterprise | Custom — 400+ GB-months, unlimited everything | Custom |
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, git-based prompt management, 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 Multi-region deployment options | US, EU, AU | US, EU |
SOC II Security compliance certification | ||
HIPAA Healthcare data compliance | ||
GDPR EU data protection compliance | ||
2FA Two-factor authentication | ||
Social Auth Google and other social login providers | ||
Custom RBAC Fine-grained role-based access control | Team plan or above | Enterprise only |
SSO Single sign-on for enterprise 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.
- 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 at the time of writing.
- 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.
- Multi-turn simulation. Generate realistic conversations with tool use and branching paths in minutes. Braintrust does not offer multi-turn simulation.
- Git-based prompt management. Branching, pull requests, approval workflows, and eval actions that trigger evaluations on every prompt change. Braintrust offers linear versioning and a playground.
- Drift detection at the use case level. Know when your "refund request" workflow degraded after a model update, even if your aggregate scores look fine. Braintrust's alerting catches score drops but doesn't isolate which use case caused them.
- Production-to-eval pipeline. Production traces auto-curate into evaluation datasets — test coverage evolves alongside real usage.
- Cross-functional ownership. 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.
- 3x cheaper tracing. $1/GB-month vs Braintrust's $3/GB for ingestion and retention. Plus a $19.99/seat/month entry tier vs Braintrust's $249/month jump from free.
When Braintrust Might Be a Better Fit
- Prompt optimization as the primary use case: Braintrust's playground is clean, fast, and accessible for comparing prompt and model combinations with CI/CD gates. If your evaluation needs don't extend beyond prompt scoring, it covers that workflow.
- Zero-setup playground for non-technical users: Braintrust's dataset editor and playground work out of the box without engineering configuration. Confident AI requires an initial HTTP connection setup — but once configured, non-technical users run full end-to-end evaluations against your actual AI application independently.
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, git-based prompt management, drift detection, and red teaming — none of which Braintrust provides at the time of writing. 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 — 3x cheaper at the same volume. Confident AI also includes more evaluation capabilities at every price point.
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. After initial engineering setup, PMs, QA, and domain experts run full evaluation cycles independently on Confident AI.
Does Braintrust support multi-turn evaluation?
At the time of writing, Braintrust does not offer multi-turn simulation. Testing conversational AI requires manually prompting through conversations or replaying historical logs. Confident AI generates realistic multi-turn conversations with tool use and branching paths automatically.
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. End-to-end testing via HTTP means you test the full agentic pipeline, not just prompts in isolation. Braintrust does not offer agent-specific evaluation at this depth.
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. Braintrust requires building custom scorers for each RAG metric and can't evaluate your actual RAG pipeline end-to-end at the time of writing.
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. Production traces that trigger drift alerts are automatically curated into evaluation datasets for the next test cycle. Braintrust does not offer drift detection at the time of writing.
Does Confident AI offer prompt management?
Yes. Confident AI provides git-based prompt management with branching, commit history, pull requests, approval workflows, and eval actions that trigger automated evaluation on every prompt change. The prompt editor covers model configuration, output format, tool definitions, and four interpolation types — all accessible through the UI for cross-functional teams.
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 multi-region deployment across the US, EU, and Australia, with on-premises deployment for strict data requirements. Enterprise customers include Panasonic, Toshiba, Amdocs, BCG, and CircleCI.