How is Confident AI different from DeepEval?

DeepEval is our open-source evaluation framework for running LLM tests locally or in CI. Confident AI is the cloud platform that layers on top — adding collaboration, dataset management, tracing, real-time monitoring, and dashboards so the whole team can work together.

Can I self-host Confident AI?

Yes. Confident AI offers a fully self-hosted deployment option alongside the managed cloud. You can run the entire platform in your own VPC or on-prem infrastructure, keeping all data within your network. Self-hosting is available on our Enterprise plan.

How long does it take to get started?

Most teams are up and running in under 15 minutes. Install the SDK, add a few lines of code to log traces or run evals, and results show up in the platform immediately.

Does Confident AI offer LLM observability?

Yes. Every LLM call is captured as a trace with full context — inputs, outputs, tool calls, latency, token cost, and metadata. You can drill into any production request, set up alerts on quality degradation, and monitor trends over time without building custom logging.

Can I use Confident AI in CI/CD pipelines?

Yes. DeepEval integrates directly into your CI pipeline so you can run regression tests on every pull request. If quality drops below thresholds you define, the build fails — no bad prompts make it to production.

What LLM use cases are supported?

All types of LLM use cases are supported, including summarization, Text-SQL, customer support chatbots, internal RAG QAs, conversational agents, and more.

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Confident AI

Confident AI is a comprehensive platform for engineering, QA, and product teams to evaluate, observe, and improve LLM applications throughout their lifecycle. It provides research-backed evaluation metrics, real-time production tracing, automated dataset curation, and collaborative workflows to ensure AI quality from prototype to scale. The platform integrates with popular frameworks like OpenAI, LangChain, and OpenTelemetry, offering no-code evals, chat simulations, and Git-based prompt versioning. It's built for regulated industries with SOC 2, HIPAA, and GDPR compliance, supporting both cloud and self-hosted deployments.

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Sep 22, 2026

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LLM EvaluationLLM ObservabilityAI Red TeamingDataset ManagementPrompt VersioningCustom MetricsSelf-hosted OptionSOC 2 Compliant

Product information

Everything worth knowing about Confident AI

The complete picture — from everyday features to the technical detail builders and IT folks dig for.

Core features

What it actually does

LLM Evaluation Suite

Full unit and regression testing suite for LLM systems, including AI arena for comparing prompts and apps.

LLM Observability

Trace every LLM call in production, monitor latency, token cost, and set real-time quality alerts.

No-Code Eval Workflows

Evaluate live AI apps via APIs without code, with pre-evaluation data transformers for API responses.

Dataset Management

Create, edit, auto-curate from traces, and run scheduled evals against datasets on the cloud.

Technical capabilities

Custom Metrics

Define evaluation criteria in natural language (G-Eval) or with code-based metrics for custom logic.

Git-based Prompt Workflows

Version prompts and use branching/merging like code, with approval workflows and eval gates.

OTEL & API Integrations

Ingest traces via OpenTelemetry or the platform API; integrates with 20+ LLM gateways and frameworks.

Must watch videos

RAG Evaluation Using DeepEval & Confident AI — Full Tutorial

28:57

Tutorial28:57

RAG Evaluation Using DeepEval & Confident AI — Full Tutorial

In this video - I show you how to perform RAG evaluation using DeepEval, the open-source LLM evaluation framework, together with the Confident AI platform. We cover how to integrate DeepEval into a retrieval-augmented generation (RAG) app, set up test cases, pick the right metrics (answer relevancy, faithfulness, contextual precision/recall), run evaluations, and interpret the results. What you’ll learn: 1. Understanding RAG evaluation and why it matters for RAG pipelines 2. Installing and setting up DeepEval in your project 3. Choosing appropriate RAG metrics and thresholds using DeepEval 4. Confident AI 5. Using Confident AI to manage evaluation data, run experiments, track metrics and dashboards 6. Integrating evaluation into your development/CI workflow for RAG apps Resources & links: • Project Git : https://github.com/yashprogrammer/DeepEval.git • RAG Project used : https://github.com/yashprogrammer/document_portal.git • DeepEval GitHub: https://github.com/confident-ai/deepeval.git • Confident AI Evaluation Platform: https://www.confident-ai.com/ Drop a 👍 if you found this useful, and subscribe for more tutorials on AI engineering, prompt-engineering, RAG pipelines and evaluation best-practices. Leave your questions/comments below! #RAGEvaluation #DeepEval #ConfidentAI #LLMEvaluation #RetrievalAugmentedGeneration #AIEngineering

The Art of Being Wrong: How to Calibrate Confident AI Models That Lie

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Tutorial42:33

The Art of Being Wrong: How to Calibrate Confident AI Models That Lie

- Most product teams spend a whole quarter trying to fix a model that will never be perfect. Pranav Pathak spent his early career doing exactly that, until he realized the model wasn't the problem. In this session, Nicole Gottselig sits down with Pranav Pathak, Product Director for AI & ML at Booking.com, for a live breakdown of why confident AI fails in the real world and what product teams can actually do about it. Pranav walks through real lessons from building AI at scale, including a story from nine years ago where he couldn't get a spam model above 90% accuracy and had to stop trying to fix the ML and start fixing the UX instead: - Why "Zombie Projects" happen when teams keep throwing data and algorithms at a model instead of designing around its limits - The 99/80/40 Rule in practice: why a 99% accurate cancellation model should never cancel an order, and what to do at every confidence level - How 75-80% of Netflix's views come from recommendations, and why the cost of being wrong is near zero - What Air Canada's chatbot lawsuit teaches us about the one thing you can never let AI make up - How Booking.com lowered precision thresholds on a customer service routing model to cover more use cases and actually improved the product - Using a judge LLM to catch 90-95% of inaccuracies before they reach a user - Training a model to say "I don't know" and why that counts as the correct answer - Why Pranav tracks AI capabilities (summarization, intent detection, generation) instead of tracking models, and how that keeps his team calm Whether you're a product manager trying to ship AI that survives month six, or someone trying to figure out why your demo felt so promising and your roadmap feels stuck, this conversation will reframe how you think about AI accuracy entirely. Timestamps 00:00 Introduction and Welcome 01:17 Why Building with AI Is a Soft Skill, Not a Science 02:30 Understanding Model Accuracy: The 99%, 80%, and 40% Rule 08:27 Practical Applications of AI in Business 14:25 Building AI with Fail-Safes and Human Oversight 20:31 The Future of Jobs in the Age of AI Connect with Pranav on LinkedIn: https://www.linkedin.com/in/pranavmpathak/

[AI COVER] CONFIDENT - LOUIS & RYUL (루이스) (류) LNGSHOT (롱샷)

4:09

Tutorial4:09

[AI COVER] CONFIDENT - LOUIS & RYUL (루이스) (류) LNGSHOT (롱샷)

Original song: https://youtu.be/47YClVMlthI?si=9g6qk4wfzZPWSW0t models by: @ynkslj #aicover #aivoice #straykids #cortis #kpop #kpopidol #linedistribution #line_distribution #aivideo #bts #blackpink #jaypark #lngshot #lngshot4sho #ohyul #ryul #woojin #louis #viral #kpopviral #viralvideo #like #korea #entertainment #explore #explorepage #jungkook #bangchan #felix #hyunjin #rm #suga #jimin #aespa #trending #trendingvideo #music #lesserafim #leeknow #cortis #btsarmy #btsv #lngshot4sho

Who uses it

Real use cases, no hype

LLM Evaluation

Benchmark LLM systems with research-backed metrics (relevancy, faithfulness, bias).

Production Observability

Trace, monitor, and alert on production LLM systems for latency, cost, and quality.

AI Red Teaming

Stress-test LLM apps against adversarial attacks like prompt injection and PII leakage.

AI Governance

Enforce AI standards, controls, and compliance (SOC2, HIPAA) across teams.

Prompt Management

Version prompts with git-like branching, approval workflows, and eval gates.

Dataset Management

Curate, version, and generate synthetic test data from production traces.

Multi-Turn Simulation

Simulate thousands of realistic chatbot conversations to test behavior at scale.

What's great

Unified platform for eval, tracing, and red teaming
Seamless integration with DeepEval open-source framework
Comprehensive metrics library (50+ single/multi-turn)
Git-based prompt versioning with eval gates
SOC2, HIPAA, GDPR compliance for regulated industries

Technical strengths

Full pipeline exposed as API for custom automation
Dataset auto-curation from production traces

Where it falls short

Free tier severely limited (2 seats, 1 project, 5 runs/week)
Can be expensive for high-volume trace ingestion
Most advanced features locked to Team/Enterprise tiers
Steep learning curve for full platform utilization

Technical limitations

Token-based pricing can fluctuate with model choice
Self-hosting only available on Enterprise plan
No offline mode for air-gapped environments

For technical folks

The deep-dive specs

FAQ

Includes technical Q&A

Confident AI is the AI quality platform built by the creators of DeepEval. It gives engineering, QA, and product teams a single place to evaluate, observe, and improve LLM applications — from prototyping through production.