7 best AI observability platforms for LLMs in 2025

Quick comparison of the best AI observability platforms for LLMs:

• Best overall (improvement loop): Braintrust
• Best open source: Langfuse
• Best for runtime guardrails: Galileo AI
• Best for ML + compliance: Fiddler

The question has changed. A year ago, teams building with LLMs asked "Is my AI working?" Now they're asking "Is my AI working well?"

When you're running a chatbot that handles 50,000 conversations a day, "it returned a response" isn't good enough. You need to know which responses helped users, which ones hallucinated, and whether that prompt change you shipped on Tuesday made things better or worse. Traditional monitoring tools track metrics like uptime and latency, but they don't review and score live answers from AI agents.

This is where AI observability comes in. The teams winning aren't just shipping AI features; they're building feedback loops that make those features better every week. The right AI observability platform is the difference between flying blind and having a system that improves itself.

What is AI observability?

AI observability monitors the traces and logs of your AI systems to tell you how they are behaving in production. Contrary to traditional software observability, AI observability goes beyond uptime monitoring to answer harder questions: Was this output good? Why did it fail? How do I prevent it from failing again?

The line between "logging tool" and "observability platform" comes down to what happens after you capture data. Basic logging stores your prompts and responses. Maybe you get a dashboard showing request volume and error rates. That's useful for the first week, but it stops being useful when you have 100,000 logs and no way to know how your AI systems are performing.

A modern AI observability platform goes beyond passive monitoring by tightly integrating debugging, evaluation, and remediation into the development lifecycle. Production logs are correlated with traces and model inputs, which feed directly into automated evaluations running in CI/CD. When regressions or failures are detected, those cases are automatically captured as reusable test datasets, turning real-world incidents into guardrails for future releases. Rather than simply explaining what happened, the platform closes the loop by helping teams fix issues and continuously verify that the fix holds in production.

The 7 best AI observability platforms in 2025

1. Braintrust

Braintrust is an end-to-end platform that connects observability directly to systematic improvement. Production traces become eval cases with one click, eval results show up on every pull request, and PMs and engineers work in the same interface without handoffs.

Braintrust is opinionated about workflows in a way that saves time. Get instant AI observability by sending logs to Braintrust. Key metrics are automatically tracked on each log with the ability to configure custom metrics and scorers as well.

Companies like Notion, Zapier, Stripe, and Vercel use Braintrust in production. Notion reported going from fixing 3 issues per day to 30 after adopting the platform.

Best for: Teams shipping AI products to real users who need to catch regressions before they hit production, not just monitor what already happened.

Pros:

• Exhaustive trace logging out of the box
• Fast load speeds and low latency
• Online and offline scorers
• Active observability with Topics
• Simple data model
• Great UX for devs and product teams
• Hosted SaaS
• AI gateway
• Generous free tier

Cons:

• Self-hosting requires an enterprise plan
• Pro tier ($249/month) may be steep for solo developers or very early-stage teams

Pricing: Free (1 GB processed data, 10k scores, 14-day retention), Pro $249/month (5 GB data, 50k scores, 30-day retention), Enterprise custom.

2. Langfuse

Langfuse is the open-source option in LLM observability. The platform covers tracing, prompt management, and evaluations with multi-turn conversation support.

Best for: Teams who want open-source flexibility, especially those comfortable self-hosting.

Pros:

• Fully open-source under MIT license
• OpenTelemetry support
• Active community and frequent releases
• Cost tracking with automatic token counting

Cons:

• UI is functional but less polished than commercial alternatives
• CI/CD integration requires custom work
• Self-hosting needs DevOps knowledge to set up properly

Pricing: Free cloud tier (50k units/month), paid plans from $29/month (Core) up to $199/month (Pro), self-hosted is free.

3. Galileo AI

Galileo is a managed, proprietary evaluation and observability platform that ships 20+ vendor-maintained metrics, including hallucination detection and Context Adherence.

Best for: Teams who want prebuilt, vendor-managed evaluators out of the box and runtime guardrails on agent outputs.

Pros:

• 20+ built-in metrics that work out of the box
• Low-latency inline scoring
• Automatic failure-mode detection
• Runtime guardrails that intercept issues before users see them
• Integrations for various platforms

Cons:

• Closed and proprietary
• Runtime guardrails are Enterprise-only
• Free tier is limited

Pricing: Free (5,000 traces/month), Pro ~$100/month (50,000 traces), Enterprise custom.

4. Helicone

Helicone is an AI Gateway with routing, failovers, rate limiting, and caching across 100+ models in addition to an evals platform.

Best for: Teams who want gateway features in addition to evals.

Pros:

• Built-in caching reduces LLM costs
• AI Gateway routes to 100+ models
• Session tracing for multi-step workflows

Cons:

• Less depth on evaluation features
• More focused on operational metrics

Pricing: Free (10k requests/month), Pro at $79/month flat with usage-based pricing.

5. Maxim AI

Maxim AI combines simulation, evaluation, and observability in their platform.

Best for: Teams who want to come up with AI-generated test cases.

Pros:

• Agent simulation engine tests workflows across varied scenarios
• Pre-built evaluator library for quality checks
• SOC 2 Type 2 compliant

Cons:

• Newer platform with a smaller community
• Some features are still in alpha

Pricing: Free tier available, Pro $29/seat/month, Business $49/seat/month, Enterprise custom.

6. Fiddler AI

Fiddler AI tracks both traditional ML and LLMs in one place.

Best for: Enterprises running both ML and LLM workloads who need explainability and regulatory compliance.

Pros:

• Unified observability for predictive ML and generative AI
• Explainable AI features
• Root cause analysis tools

Cons:

• Enterprise pricing
• Steeper learning curve
• More suited for organizations with dedicated ML teams

Pricing: Free and usage-based Developer tiers, with Enterprise custom pricing.

7. Evidently AI

Evidently AI is an open-source library with over 40 million downloads and 100+ built-in metrics.

Best for: Teams running both traditional ML and LLM workloads who want unified monitoring.

Pros:

• Over 100 pre-built metrics for data quality
• Open-source with permissive license
• Strong data drift detection

Cons:

• Less emphasis on production-to-improvement loops
• Best features in the cloud version

Pricing: Free tier available, with paid Cloud and Enterprise plans.

Comparison table

FAQs

What is AI observability?

AI observability is the practice of monitoring, tracing, and analyzing AI systems to understand behavior, detect issues, and improve quality over time. It goes beyond traditional monitoring by evaluating output quality, not just system health.

How do I choose the right AI observability tool?

Start with your stack and team. Consider tracing depth, evaluation features, and whether non-engineers need access.

What's the difference between AI observability and traditional APM?

Traditional APM tracks system metrics: latency, error rates, uptime. AI observability adds quality evaluation and ensures the responses are not just system healthy but also useful.

If I need a gateway, should I use Braintrust?

Braintrust includes an AI gateway, but if routing and model switching is your only need, a dedicated gateway may be a simpler solution.