Best LLM Monitoring Tools for 2026

TL;DR: Best LLM monitoring tools for 2026

All-in-one solution: Braintrust — monitoring + evaluation + experimentation

Open-source: Langfuse — self-hosted LLM observability platform

Security and testing: Promptfoo — open-source red-teaming and eval CLI (now part of OpenAI)

Logging: Datadog — unified infrastructure and LLM monitoring

For production AI observability with built-in evaluations, token usage monitoring, and cost attribution for LLM apps, Braintrust delivers the most complete solution.

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Deploying a large language model to production is straightforward. Keeping it reliable, cost-effective, and high-quality over time is where teams struggle. Without LLM production monitoring, you have no idea how your AI is actually performing for customers. Latency spikes, quality regressions, and cost overruns happen quietly. By the time users complain, you've already burned through budget or damaged trust.

LLM monitoring tools track every request through your LLM pipeline. They capture inputs, outputs, tokens, latency, and costs. They let you evaluate quality, debug failures, and optimize performance with online evaluations before issues reach users.

At Braintrust, we built the platform to connect all of these capabilities in one loop. Monitoring, evaluation, and experimentation work together so your team catches problems early and ships improvements faster.

Why monitoring LLM applications matters

LLM monitoring platforms solve three problems that traditional application monitoring can't touch.

Cost control. LLM APIs charge per token. A single poorly optimized prompt can multiply costs by 10x. Token usage monitoring shows exactly where money goes and identifies expensive calls. Without visibility into token consumption, costs spiral with no warning.

Quality assurance. LLMs are non-deterministic. They hallucinate, miss context, and produce inconsistent outputs. A customer-facing assistant might work perfectly in testing but start generating incorrect product recommendations in production when users ask unexpected questions. LLM monitoring catches these issues through online automated scoring, flagging problems before users notice.

Performance debugging. Multi-step LLM workflows can fail at any point in the chain. A retrieval step might return irrelevant documents. A post-processing function might strip useful context. Real-time LLM observability pinpoints bottlenecks across the entire workflow, so you know exactly which step to fix.

With these three capabilities running continuously, your team shifts from reactive firefighting to proactive optimization.

4 best LLM monitoring tools (2026)

1. Braintrust

Braintrust is an end-to-end platform for monitoring, evaluating, and improving LLM applications in production. We combine LLM production monitoring, AI quality evaluation, and experimentation in a single integrated platform.

Braintrust captures full traces across multi-step LLM workflows, automatically logging inputs, outputs, metadata, and costs. Real-time LLM observability shows live request flows with drill-down into individual traces, surfacing your slowest calls, highest token consumption, and error patterns. Cost attribution for LLM apps breaks down spending by user, feature, or model so you see exactly where money goes.

What makes Braintrust the strongest choice for large language model monitoring is the depth across the entire LLM lifecycle. We capture detailed traces across multi-step workflows and run evaluations directly in your CI/CD pipeline. Engineers can see whether a pull request actually improves agent behavior before merging. Braintrust handles everything from initial development through production optimization.

Notion reported going from fixing 3 issues per day to 30 after adopting Braintrust. That 10x improvement in development velocity came from replacing manual testing with automated evaluation loops. Teams like Stripe, Vercel, Airtable, Instacart, and Zapier also run their production AI through our platform.

Pros

• Real-time LLM observability: Live dashboards show request flows with drill-down into individual traces, surfacing slowest calls, highest token consumption, and error patterns
• Token usage monitoring: Per-request cost breakdowns across all providers with aggregation by user, feature, or model to identify optimization opportunities
• Cost attribution for LLM apps: Tag-based spending breakdown by team, feature, or user with trend analysis and budget alerts
• AI quality evaluation: Custom scorers run continuously on production traffic, with threshold-based alerts that catch regressions before users report them
• Multi-step trace visualization: Full execution path tracking through chains and agent workflows, pinpointing exactly which step causes bottlenecks or failures
• Asynchronous logging: Non-blocking logs maintain application performance at high volume without adding latency to user requests
• Webhook alerts: Automated notifications for cost thresholds, quality drops, and performance issues integrate with Slack, PagerDuty, or custom systems
• Dataset versioning: Reproducible experiments with version-controlled test cases that expand as you discover edge cases
• CI/CD integration: Evaluations run on every code change, failing builds when quality scores drop below acceptable levels
• Prompt playground: Side-by-side comparison testing before deployment shows which prompts perform better on your actual data
• AI Proxy: Route LLM API calls through Braintrust to automatically capture logs, enable caching, and implement fallbacks across OpenAI, Anthropic, and other providers with a simple base URL change
• 9+ native framework integrations: OpenTelemetry, Vercel AI SDK, OpenAI Agents SDK, LangChain, LangGraph, Google ADK, Mastra, Pydantic AI, and more
• Loop AI assistant: Built-in AI that generates evaluation datasets, creates custom scorers, identifies failure patterns, and suggests prompt improvements

Cons

• Designed for LLM applications rather than general software monitoring
• Most valuable for teams running continuous evaluations

Best for

Teams building production LLM applications that need monitoring, evaluation, and experimentation in one platform.

Pricing

Free tier with 1M trace spans. Pro plan at $249/month with unlimited trace spans. Custom Enterprise plans. See pricing details →

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2. Langfuse

Langfuse is an open-source LLM observability platform built on OpenTelemetry. It logs traces and sessions, captures nested traces for chains and agents, groups interactions by session, and tracks prompt versions. With 23,000+ GitHub stars and adoption by organizations including Khan Academy, Twilio, and Merck, Langfuse has become the most widely used open-source option in the LLM observability space.

Langfuse covers four modules: observability (full tracing of LLM calls and agent workflows), prompt management (versioning, playground, experiments), evaluation (LLM-as-judge, human annotation, datasets), and metrics (costs, latency, user feedback). The platform supports Python, JavaScript, Java, and Go SDKs, and its v3 SDK is built natively on OpenTelemetry.

Pros

• Open-source (MIT license) with unrestricted self-hosting
• Session tracking connects related requests across conversations
• Production AI observability for complex chains and agent workflows
• Prompt versioning with trace linkage and A/B experiments
• OpenTelemetry-native, so traces from other OTEL-instrumented libraries work out of the box
• Unlimited users across all paid tiers

Cons

• Requires more manual instrumentation than proxy-based tools
• Evaluation features are less integrated than Braintrust's end-to-end loop
• Self-hosting requires PostgreSQL, ClickHouse, Redis, and S3-compatible storage, which means DevOps overhead
• UI can feel cluttered with large trace volumes

Best for

Teams who want full control over their data, prefer open-source tooling, and have the DevOps resources to self-host.

Pricing

Free tier with 50,000 units/month and 30-day retention. Core plan at $29/month with 100,000 units and 90-day retention. Pro plan at $199/month with 3-year retention and SOC 2/HIPAA compliance. Enterprise at $2,499/month with custom limits and dedicated support.

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3. Promptfoo

Promptfoo is an open-source CLI and library for evaluating and red-teaming LLM applications. In March 2026, OpenAI acquired Promptfoo, though the tool remains open source and MIT licensed. Before the acquisition, Promptfoo had grown to 350,000+ developers, 130,000 active monthly users, and adoption by over 25% of Fortune 500 companies.

Promptfoo's strength is in systematic testing and security scanning. Teams define test cases in YAML configuration files that live in version control. The CLI runs batch evaluations across different models and prompt variations, compares outputs side by side, and integrates into CI/CD pipelines. Promptfoo also includes built-in vulnerability scanning for prompt injection, PII exposure, jailbreak risks, and other security concerns that matter when deploying agents to production.

The key distinction: Promptfoo is a testing and evaluation tool, not a production monitoring platform. It does not provide real-time observability, live dashboards, or continuous monitoring of production traffic. If you need both pre-deployment testing and production monitoring, you'll need to pair Promptfoo with a monitoring tool like Braintrust or Langfuse.

Pros

• Fully open-source (MIT license) with local execution for data privacy
• Specialized red-teaming and vulnerability scanning for AI security
• YAML-based configuration keeps test cases in version control alongside application code
• CI/CD integration runs evaluations on every pull request
• Supports 90+ LLM providers including OpenAI, Anthropic, Google, and self-hosted models
• Now backed by OpenAI's resources while remaining open source

Cons

• No production monitoring or real-time observability of live traffic
• CLI-first workflow requires developer comfort with command-line tools
• No collaboration features for product managers or non-technical team members
• OpenAI acquisition introduces uncertainty about long-term provider neutrality
• Enterprise pricing is custom and may shift as integration into OpenAI's Frontier platform progresses

Best for

Developer teams focused on pre-deployment testing, red-teaming, and security scanning for LLM applications, especially those in regulated industries where vulnerability scanning is required.

Pricing

Free and unlimited for open-source use. Up to 10,000 red-team probes per month on the free tier. Enterprise pricing is custom based on team size and needs.

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4. Datadog

Datadog added LLM observability features to its infrastructure monitoring platform. It captures traces for OpenAI and Anthropic calls and integrates them with APM data, giving teams who already use Datadog a way to add LLM visibility without adopting a new tool.

Datadog's LLM observability tracks inputs, outputs, latency, token usage, and errors across agent workflows. The platform automatically calculates estimated costs using providers' public pricing models. Where Datadog stands out is correlation: you can link LLM trace performance directly to infrastructure metrics, real user monitoring sessions, and application performance data. For teams already paying for Datadog's broader monitoring suite, this unified view saves time.

The tradeoff is cost and depth. Datadog's LLM observability pricing starts at $8 per 10,000 monitored requests (billed annually) with a minimum of 100,000 requests per month. That baseline adds up fast on top of existing Datadog infrastructure costs, which commonly run $50,000 to $150,000 per year for mid-sized companies. The LLM-specific evaluation and experimentation features are less mature than dedicated LLMOps platforms like Braintrust.

Pros

• Unified monitoring for infrastructure, APM, and LLMs in one platform
• Integrates LLM traces with existing Datadog deployments and dashboards
• Mature alerting, anomaly detection, and incident management
• Sensitive Data Scanner included for PII detection and redaction in LLM traces
• Experiments feature for testing prompt and model changes against production datasets
• SOC 2 compliant with enterprise security controls

Cons

• Expensive compared to dedicated LLM monitoring tools, especially at scale
• LLM evaluation capabilities are less developed than Braintrust's integrated loop
• Requires minimum 100,000 LLM requests per month commitment
• Adds significant cost on top of existing Datadog infrastructure monitoring bills
• LLM features feel added on to a general-purpose monitoring platform rather than designed for AI-specific workflows

Best for

Enterprises with existing Datadog infrastructure who want to add large language model monitoring to their current stack without adopting a separate tool.

Pricing

LLM Observability starts at $8 per 10,000 monitored requests per month (billed annually) or $12 on-demand. Minimum 100,000 requests per month. Trace retention is 15 days by default. Experiment data retained for 90 days.

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Top LLM application monitoring tools compared

Feature	Braintrust	Langfuse	Promptfoo	Datadog
Real-time LLM observability	Yes	Yes	No	Yes
Token usage monitoring	Yes	Yes	No	Yes
Cost attribution for LLM apps	Yes	Yes	No	Yes
AI quality evaluation	Yes	Yes	Yes (offline only)	Yes
Red-teaming / security scanning	Basic	No	Yes (industry-leading)	Basic
Prompt management	Yes	Yes	No	No
Self-hosting	Enterprise tier	Yes (free)	Yes (free)	No
Multi-step tracing	Yes	Yes	No	Yes
CI/CD integration	Native GitHub Action	Via SDK	Native	Via SDK
Free tier	1M trace spans	50K units/month	Unlimited OSS	100K requests min
Setup complexity	Low	Medium	Low	High

Ready to implement comprehensive LLM monitoring? Start monitoring with Braintrust for free — get 1M logged events per month and full access to evaluation, experimentation, and observability features.

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How to choose the right LLM monitoring tool

Match the tool to your deployment stage and technical requirements.

For early-stage products: Start with Braintrust's free tier (1M spans). You get monitoring, evaluation, and experimentation from day one. Teams that start with logging-only tools almost always need to add evaluation within weeks, so starting with a complete platform saves a migration later.

For quality-critical applications: Braintrust is the clear choice. It combines AI quality evaluation with comprehensive monitoring and experimentation in one platform. Custom scorers run on both CI/CD and production traffic, so quality regressions get caught in pull requests before they reach users.

For teams with strict open-source requirements: Langfuse provides full data control through self-hosting. The MIT license means no restrictions on modification or deployment. Budget for the DevOps overhead of running PostgreSQL, ClickHouse, Redis, and S3-compatible storage. Langfuse's evaluation features work well for basic needs, but teams needing sophisticated eval workflows and AI-assisted scoring may find Braintrust's integrated approach faster.

For security-focused teams: Promptfoo's red-teaming and vulnerability scanning fill a gap that most monitoring tools don't address. If your LLM application handles sensitive data or operates in a regulated industry, Promptfoo's security testing should be part of your pre-deployment pipeline. Pair it with Braintrust or Langfuse for production monitoring, since Promptfoo only covers testing, not live observability.

For cost-sensitive deployments: Token usage monitoring and cost attribution for LLM apps are what prevent budget surprises. Braintrust excels here with per-request cost breakdowns, tag-based attribution, and alerts that catch spending spikes early. Langfuse tracks costs too, but without the granular attribution or evaluation context that helps you optimize spending decisions. Datadog adds its own monitoring costs on top of LLM provider costs, which can double your observability bill.

For complex multi-agent systems: Full traces across chains are non-negotiable. Braintrust handles nested traces with detailed visualization and debugging tools, and runs evaluations on those traces to catch quality issues in specific steps. Langfuse offers similar trace capture through OpenTelemetry. Promptfoo can test agent workflows pre-deployment but cannot monitor them in production.

For enterprises already on Datadog: If your organization already runs Datadog for infrastructure monitoring and the team resists adopting new tools, adding Datadog's LLM observability is the path of least resistance. Be aware that evaluation depth is limited compared to Braintrust, and LLM-specific costs layer on top of your existing Datadog bill.

For teams shipping fast: Braintrust eliminates context switching by combining monitoring, evaluation, and experimentation in one view. When you're debugging a production issue, you see traces, evaluation scores, and prompt versions in a single interface. One platform means less time integrating tools, syncing data, or jumping between dashboards.

If you're building production LLM applications and need the complete development loop from monitoring through evaluation to optimization, Braintrust provides the most complete solution.

LLM monitoring best practices

Log everything. Capture inputs, outputs, metadata, user IDs, and timestamps for every request. Storage is cheap. Missing data during an incident costs engineering hours and user trust.

Set cost budgets early. Configure alerts when token usage monitoring shows spending exceeds thresholds. A runaway prompt can burn thousands of dollars overnight. Set alerts at 50%, 80%, and 100% of budget.

Automate quality checks. Manual review doesn't scale past a few hundred requests per day. Use AI quality evaluation scorers to flag potential issues automatically. Review flagged responses instead of sampling blindly.

Track token efficiency. Monitor average tokens per request over time. Increases signal prompt bloat or unnecessary context being passed to the model. Optimize prompts to reduce tokens without sacrificing output quality.

Version your prompts. Link every trace to a specific prompt version. When quality drops, you can identify which prompt change caused the regression. Production AI observability without prompt versioning leaves you guessing.

Separate logging from evaluation. Log everything immediately. Evaluate asynchronously. Running evaluations synchronously blocks user requests and adds latency. Batch scoring keeps responses fast while still catching quality issues.

Monitor full chains. Multi-step workflows can fail at any step. Trace the complete path from user input through retrieval, LLM calls, and post-processing. Identify the slowest or most expensive step, then optimize there first.

Use sampling for high-volume apps. Logging every request at scale gets expensive. Sample 10-20% of requests for detailed tracing. Log basic metrics like tokens, cost, and latency for all requests.

Set up anomaly detection. Real-time LLM observability should alert on unusual patterns. Latency spikes, cost jumps, or error rate increases all warrant automatic notifications. Configure alerts in your LLM monitoring tools to catch issues before users notice.

Test in production. Staging environments don't capture the full range of real user inputs. Run evaluations on production data with production AI observability to find edge cases that test suites miss.

Establish quality baselines. Measure average quality scores during stable periods. Detect regressions by comparing current scores to those baselines. A 5% drop in relevance scores might indicate a prompt regression or a model behavior change.

Review costs weekly. Cost attribution for LLM apps shows spending trends over time. Weekly reviews catch gradual increases before they balloon. Investigate any week-over-week cost growth exceeding 20%.

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Why Braintrust is the best LLM monitoring tool

While other LLM monitoring tools force you to choose between basic logging, security testing, or an expensive general-purpose platform, Braintrust delivers monitoring, evaluation, and experimentation in one system. No syncing data between tools. No context switching during debugging.

Leading companies including Notion, Zapier, Stripe, Vercel, Airtable, and Instacart choose Braintrust for their production AI applications. Notion went from fixing 3 issues per day to 30 after adopting Braintrust, a 10x improvement in development velocity that came from replacing manual testing with automated evaluation.

Our integrated approach means you catch quality issues before they reach users, identify cost optimization opportunities faster, and debug problems without jumping between separate dashboards. Braintrust's Loop AI assistant accelerates the process further by generating evaluation datasets, creating custom scorers, and suggesting prompt improvements automatically.

For teams serious about maintaining reliable, cost-effective AI applications, Braintrust is the clear choice. Try Braintrust free with 1M logged events per month and see how monitoring, evaluation, and experimentation work together to improve your AI applications.

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Frequently asked questions: Best LLM monitoring tools

What are LLM monitoring tools?

LLM monitoring tools track requests to language model APIs, capturing inputs, outputs, tokens, costs, and latency. They provide production AI observability by logging traces across multi-step workflows and surfacing issues in real time. Braintrust goes beyond basic monitoring by combining observability with built-in evaluation and experimentation in one platform.

Why do I need LLM production monitoring?

LLM production monitoring catches cost overruns, quality regressions, and performance issues before they impact users. LLMs are non-deterministic and expensive. Without monitoring, you can't debug failures or optimize costs. Braintrust helps teams improve development velocity through integrated monitoring, observability, and evaluation.

What's the difference between monitoring and observability?

Monitoring tracks predefined metrics like latency or error rates. LLM observability platforms capture detailed traces of every request, letting you explore and debug unexpected issues. Observability answers questions you didn't know to ask. Braintrust provides complete real-time LLM observability with multi-step trace visualization that shows exactly where problems occur in complex chains.

How does Promptfoo's OpenAI acquisition affect the LLM monitoring landscape?

OpenAI acquired Promptfoo in March 2026. Promptfoo remains open source and MIT licensed, and the team has committed to continuing development of the open-source CLI. However, Promptfoo's enterprise features will integrate into OpenAI's Frontier platform for building AI agents. Teams using Promptfoo for provider-neutral testing should monitor whether future development priorities shift toward OpenAI-specific use cases.

What are the best LLM monitoring tools in 2026?

The best monitoring tools in 2026 for LLM applications include Braintrust (comprehensive monitoring, evaluation, and experimentation), Langfuse (open source with self-hosting), Promptfoo (security testing and red-teaming, now part of OpenAI), and Datadog (enterprise infrastructure monitoring with LLM add-on). Braintrust stands out as the only platform that combines monitoring, evaluation, and experimentation in a single system, used by leading AI teams at Notion, Vercel, Instacart, and more.

Can I use multiple LLM monitoring tools together?

Yes. Many teams combine tools based on their strengths. A common pattern is using Promptfoo for pre-deployment security testing and red-teaming, then Braintrust for production monitoring, evaluation, and experimentation. Datadog users often add Braintrust alongside their existing infrastructure monitoring to get LLM-specific evaluation capabilities that Datadog's platform lacks.