The 4 best LLM monitoring tools to understand how your AI agents are performing in 2026
LLM applications fail differently than traditional software. A prompt change might work fine on your test cases but break on edge cases in production. Token costs can spike overnight. Quality can degrade gradually without throwing errors.
The best engineering teams ship AI features confidently because they've built observability into their workflow. They catch issues before deployment, understand where their money goes, and detect quality problems before users complain.
What is LLM monitoring?
LLM monitoring tracks how your AI applications perform in production. It goes beyond checking if your API is responding. It measures whether your responses are accurate, relevant, and safe.
Traditional monitoring tells you if requests succeed. LLM monitoring tells you if they're actually good. It captures prompts, responses, token usage, latency, and costs, then helps you understand patterns across thousands of requests.
When you need more than basic logging: If you're running prompts in production and spot-checking responses manually, basic logging is enough. When you're shipping prompt changes weekly, serving thousands of users, or managing multiple AI features, you need systematic monitoring. The line is simple: if you can't manually review every response, you need automation.
The 4 best LLM monitoring tools in 2026
1. Braintrust
Best for: Teams who need comprehensive evaluation and production monitoring in one platform
Braintrust monitors LLM quality, cost, and performance from development through production. Track token usage per user and feature to understand where money goes. Online scoring continuously evaluates production traffic using the same quality metrics you test with before deployment. When responses degrade, alerts fire before users complain. Every production trace can become a test case, creating a feedback loop where monitoring data directly improves your evaluation coverage.
When something fails in production, load that trace into the Playground, test your fix against real examples, and redeploy. Loop analyzes production logs to identify failure patterns, generates test datasets from real user interactions, and builds scorers that catch your specific quality issues.
Pros:
- Loop automatically generates evaluation datasets from production logs and creates custom scorers
- One-click conversion of production failures into test cases
- Online scoring continuously evaluates live production traffic with configurable sample rates
- GitHub Action runs evals on every PR and posts detailed results as comments
- Real-time cost tracking per user and per feature
- 25+ built-in scorers (factuality, relevance, safety) plus custom scorer support
- Playground enables rapid testing and debugging without code
- Native integrations with LangChain, LlamaIndex, Vercel AI SDK, OpenTelemetry, CrewAI
- Self-hosting options for complete data control
Cons:
- Enterprise features like self-hosting require paid plans
Pricing: Free tier available (unlimited users, 1M spans, 10K scores); paid plans scale with usage
2. Vellum
Best for: Teams wanting visual workflow builders with end-to-end platform features
Vellum gives you visual tools to build, test, and deploy LLM applications. Non-technical team members can experiment with prompts and models through the UI. The workflow builder lets you chain prompts, API calls, and business logic without code.
Visual development: Build complex workflows in the browser. Chain together LLM calls, database queries, API requests, and conditional logic. Test each step before deploying. The visual interface makes AI development accessible to product managers.
Evaluation and testing: Run evaluations against test datasets. Compare different prompt versions and models side-by-side. A/B test in production with traffic splitting. Version control means you can roll back instantly if changes cause problems.
Production monitoring: Dashboards show cost, latency, and quality trends. Request-level tracing helps debug failures. Configure alerts for anomalies. Human-in-the-loop workflows let subject matter experts review outputs.
Pros:
- Strong visual workflow builder for non-technical users
- End-to-end platform from prototyping to production
- Good prompt versioning and A/B testing
- Instant deployment with rollback capability
- Collaborative features for cross-functional teams
Cons:
- Less comprehensive evaluation automation than code-first platforms
- Enterprise pricing can scale quickly
- Better suited for workflow orchestration than deep monitoring
Pricing: Pro plans start around $500/month; Enterprise custom pricing
3. Fiddler
Best for: Teams building multi-agent systems
Fiddler monitors AI workflows with visibility across agents, traces, and spans. Root cause analysis helps identify where multi-step workflows break. Real-time guardrails catch harmful outputs before they reach users.
The platform includes bias detection and audit trails. Supports deployment on AWS, Azure, or GCP with self-hosting options.
Pros:
- Multi-agent observability with hierarchical tracing
- Real-time guardrails for content safety
- Self-hosting and multi-cloud deployment
- Bias detection and audit capabilities
Cons:
- Less focused on rapid iteration
- Enterprise pricing model
- Heavier setup than developer-first tools
Pricing: Enterprise custom pricing
4. LangSmith
Best for: Teams deeply integrated with LangChain
LangSmith is built specifically for LangChain applications. If you're already using LangChain, the integration is seamless. Add a few lines of code and all your chains are automatically traced.
LangChain integration: Automatic tracing for chains and agents. See every step in your workflow: retrieval, LLM calls, tool usage. Debug complex chains by following execution paths. The integration captures everything without manual instrumentation.
Evaluation framework: Run evaluations with datasets and LLM-as-judge scorers. Test prompts in the Playground. Compare different approaches side-by-side. Track experiments over time.
Production monitoring: Track costs and latency per request. User feedback collection ties directly to traces. Performance dashboards show trends. Basic alerting on errors and latency spikes.
Pros:
- Seamless LangChain integration with minimal code
- Mature tracing for complex chains and agents
- Large community and extensive documentation
- Good for teams already building with LangChain
Cons:
- Per-trace pricing becomes expensive at high volumes
- Limited self-hosting (Enterprise only)
- Less framework-agnostic than competitors
- Quality scoring less comprehensive than evaluation-focused platforms
Pricing: Free tier limited; paid plans based on trace volume; costs increase significantly at scale
Comparison table
| Tool | Starting Price | Best For | Core Strength |
|---|---|---|---|
| Braintrust | Free tier | Teams shipping frequent prompt changes | Unified eval-to-production workflow with automated dataset generation |
| Vellum | ~$500/month | Visual workflow teams | No-code UI and orchestration |
| Fiddler | Enterprise pricing | Multi-agent systems | Hierarchical observability and guardrails |
| LangSmith | Free tier | LangChain users | Deep LangChain integration and tracing |
→ Start monitoring with Braintrust Free tier available
Why teams choose Braintrust
Most LLM monitoring tools separate evaluation from production monitoring. You test in one place, monitor in another, and manually connect the insights. Braintrust unifies both.
The continuous feedback loop: Production traces become test cases with one click. Evaluations run automatically on every code change through GitHub Actions. The same quality scorers that test prompts in CI/CD monitor production traffic. When quality drops, you immediately know why and can add failing cases to your eval suite.
Automated dataset generation solves the hard problem: Loop analyzes production logs to identify failure patterns and generates evaluation datasets from real user interactions. You don't spend weeks building test suites manually. The system learns from production and creates tests automatically.
Cost visibility other platforms miss: Track token usage per user, per feature, per conversation. Identify expensive patterns before bills spike. Most platforms only show aggregate spending - Braintrust shows you exactly where the money goes.
The monitoring data directly improves evaluation coverage. Production failures become tests within minutes, creating a system that gets smarter with every deployment.
FAQs
What is LLM monitoring?
LLM monitoring tracks AI application performance in production by measuring response quality, cost, and behavior patterns. It captures prompts, model parameters, responses, token usage, and latency, then applies automated scoring to evaluate quality at scale. This lets teams detect issues like hallucinations, cost spikes, and quality degradation before users complain.
How do I monitor LLM costs and prevent surprise bills?
LLM monitoring tools track token usage at granular levels: per user, per feature, per conversation. Braintrust's cost monitoring shows exactly where your spending goes in real-time dashboards and identifies expensive workflows. You can group costs by any metadata field to understand which parts of your application consume the most tokens and optimize accordingly.
How do LLM monitoring tools help me detect quality drift?
Quality drift happens gradually without throwing errors. LLM monitoring platforms like Braintrust use online scoring to continuously evaluate production traffic with the same quality metrics you test with in development. When scores drop below thresholds, alerts fire immediately. Historical trend analysis shows exactly when quality shifted and correlates degradation with specific deployments or changes, catching issues before users complain.
How do I know if a prompt change will break my application?
Run automated evaluations before deployment. Braintrust's GitHub Action runs evals on every pull request, testing your changes against dozens or hundreds of real examples. Results post directly to the PR showing which test cases improved, which regressed, and by how much. Set quality gates that block merges if scores drop. This catches regressions before they reach production.
How quickly can I see results from LLM monitoring?
Most platforms show immediate visibility after instrumentation. Braintrust typically takes under an hour to integrate. You'll see request traces, costs, and latency instantly. Meaningful quality insights require building evaluation datasets, starting with 5-10 test cases. Teams usually see value in the first week: catching a regression before deployment or identifying expensive workflows. Long-term value comes from the feedback loop where production traces improve your eval datasets over time.
What's the difference between LLM monitoring and observability?
Monitoring tracks predefined metrics like latency and token counts. Observability provides tools to investigate arbitrary questions about system behavior. Monitoring asks "Is latency acceptable?" while observability asks "Why did this specific request fail?" The best platforms combine both: continuous monitoring of key metrics plus deep trace capture for debugging unexpected issues.
How do I build evaluation datasets without spending weeks on it?
The hardest part of LLM monitoring is creating good test data. Braintrust solves this by converting production traces into test cases with one click. Spotted a failure? Add it to your dataset immediately. Loop can also generate datasets automatically from your production logs, creating realistic test cases that match actual user patterns. Start with 5-10 examples and grow organically from real usage.
Is Braintrust better than LangSmith?
Braintrust is better for comprehensive evaluation automation, multi-framework support, and production monitoring. The GitHub Action integration, one-click dataset creation, and fast query performance make it ideal for teams shipping frequently across different frameworks. LangSmith is better if you're building exclusively with LangChain and need deep chain tracing. However, LangSmith's per-trace pricing becomes expensive at scale. Choose based on your framework commitment and evaluation needs.