4 best tools for monitoring LLM & agent applications in 2026

TL;DR: Top LLM monitoring tools to watch in 2026

• All-in-one: LangWatch — combines monitoring, evaluations, and experimentation in a single platform

• Open-source: Langfuse — self-hosted LLM observability with strong community adoption mostly solo-developers

• Fastest setup: Helicone — drop-in proxy integration with minimal or no code changes

• Enterprise: Datadog — end-to-end infrastructure monitoring with LLM visibility layered in not evals/prompt management.

If you’re running production LLM applications and need observability that goes beyond logs, built-in evaluations, token-level usage tracking, and accurate cost attribution, LangWatch stands out as the most comprehensive option today.

Shipping an LLM to production is easy. Keeping it reliable, affordable, and consistently high-quality is the hard part.

Without proper LLM production monitoring, you’re flying blind. You don’t actually know how your AI behaves once real users start interacting with it. Latency spikes, silent quality regressions, and runaway costs can creep in unnoticed. By the time customers complain, you’ve often already blown your budget—or worse, eroded trust.

LLM monitoring tools close this gap by tracing every request across your LLM pipeline. They capture prompts, responses, token usage, latency, and costs end to end. With this visibility, teams can evaluate output quality, debug failures, and continuously optimize performance using online evaluations—before small issues turn into production incidents.

Why monitoring LLM applications matters

LLM monitoring platforms typically focus on three core challenges:

Cost control
LLM APIs price per token, which means a single inefficient prompt or workflow can explode costs overnight. Token-level monitoring shows exactly where spend is coming from and highlights expensive calls. Without this insight, usage and bills grow unpredictably.

Quality assurance
LLMs are inherently non-deterministic. They hallucinate, lose context, and behave inconsistently as inputs vary. Monitoring enables continuous, automated quality evaluation in production. An assistant that performed flawlessly during testing can start returning incorrect or misleading answers once users ask novel or edge-case questions—monitoring is how you catch that early.

Performance debugging
Modern LLM systems are rarely a single call. They’re multi-step pipelines with tools, retrieval, and agent logic. Real-time observability makes it possible to pinpoint exactly where latency or failures occur, instead of guessing across the stack.

Ultimately, LLM monitoring shifts AI operations from reactive firefighting to proactive control—surfacing problems early, long before they escalate into customer-facing failures.

5 best LLM monitoring tools (2026)

1. LangWatch

LangWatch is an end-to-end platform built to monitor, evaluate, and improve LLM applications in production. Unlike generic observability tools or narrow point solutions that focus only on logging or offline evaluation, LangWatch brings LLM production monitoring, AI quality evaluation, and experimentation together in one integrated workflow.

LangWatch captures full traces across multi-step LLM and agent workflows, automatically logging inputs, outputs, metadata, token usage, and costs. Real-time observability lets teams follow live request flows with deep drill-down into individual traces—highlighting slow calls, high token consumption, and recurring failure patterns. Cost attribution breaks spend down by user, feature, model, or experiment, making it clear where optimization efforts will have the biggest impact.

What makes LangWatch stand out for large language model monitoring is its focus on continuous quality control across the entire LLM lifecycle. Detailed tracing across complex pipelines is paired with production-grade evaluations and agent simulations, so teams can validate whether changes actually improve behavior before and after deployment. From early development to production optimization, LangWatch is designed for teams that take AI reliability seriously.

Pros

• Real-time LLM observability
  Live dashboards show request flows with drill-down into individual traces, surfacing slowest calls, highest token usage, and error patterns.

• Token usage monitoring
  Per-request and aggregated cost visibility across providers, models, and prompts to quickly identify expensive interactions.

• Cost attribution for LLM apps
  Tag-based breakdowns by team, feature, user, or experiment, with trend analysis to keep spend under control.

• AI quality evaluation
  Custom evaluators and automated scorers run on production traffic, catching regressions before users notice.

• Multi-step trace visualization
  End-to-end visibility into chains, tools, and agent workflows to pinpoint exactly where failures or bottlenecks occur.

• Agent testing & simulations
  Scenario-based simulations let you test how agents behave under real-world conditions before releasing changes.

• CI/CD-friendly evaluations
  Run evaluations as part of your development workflow to validate improvements and prevent silent quality regressions.

• Prompt & pipeline experimentation
  Compare prompts, models, and pipelines side by side using real datasets to make evidence-based decisions.

Cons

• Purpose-built for LLM and agent systems rather than general infrastructure monitoring

• Most value comes from teams actively running evaluations and experiments, not just logging

Best for

Teams building production LLM or agent-based applications that need monitoring, quality evaluation, and experimentation in a single, cohesive platform.

Pricing

Free tier available. Paid plans scale with usage, with custom Enterprise options for larger teams. See pricing details →

2. Langfuse

Langfuse is an open-source platform focused on LLM observability, providing detailed logging for traces and user sessions. It supports hierarchical tracing for chains and agent workflows, organizes interactions into sessions, and keeps track of prompt iterations over time.

Pros

• Open-source with the option to self-host

• Session-based views that tie related LLM requests together

• Strong observability for multi-step chains and agent workflows

• Prompt version history linked directly to execution traces

Cons

• Most frameworks require manual setup and instrumentation

• Few built-in templates for automated quality evaluation

• Interface can become noisy when handling high trace volumes

Best for
Teams that prioritize data ownership and want a self-managed LLM observability stack.

Pricing
Self-hosted version is free. Managed cloud plans start at $29 per month with usage-based pricing.
Read our comparison of Langfuse vs. LangWatch.

3. Helicone

Helicone is a proxy-based monitoring tool for LLM applications. By routing API requests through Helicone’s proxy with a simple base URL change, teams can capture prompts, responses, token usage, and costs—without adding an SDK or modifying application code.

Pros

• No-code setup using a proxy-based integration

• Compatible with multiple major LLM providers

• Built-in caching to help reduce API spend

• Very fast to get started with basic LLM observability

Cons

• Introduces an extra network hop between your app and the LLM API

• Minimal built-in support for evaluations or quality scoring

• Does not support self-hosted or on-prem models

• Proxy reliance can become a single point of failure

Best for
Teams that want immediate LLM production visibility with minimal setup and no code changes.

Pricing
Free plan includes up to 10,000 requests per month. Paid plans start at $79 per month.

4. Datadog

Datadog has expanded its infrastructure observability platform to include LLM monitoring features. It records traces for calls to providers like OpenAI and Anthropic and links them directly with existing APM and infrastructure metrics.

Pros

• Single platform for both infrastructure and LLM observability

• Seamless fit for teams already using Datadog

• Advanced alerting, dashboards, and anomaly detection

Cons

• Higher cost than purpose-built LLM monitoring solutions

• Minimal support for AI quality evaluations

• LLM tooling feels added on rather than core

• Excessive complexity for teams focused mainly on AI workloads

Best for
Large organizations already standardized on Datadog that want basic LLM visibility without introducing a new observability tool.

Pricing
Includes a free tier with 1-day metric retention and up to five hosts. Paid plans start at $15 per host per month.

How to choose the right LLM monitoring tool

Pick a platform that fits your stage, risk profile, and team setup.

For early-stage products
If you just need quick visibility, Langfuse is often the fastest way to get started thanks to its proxy-based setup. As soon as cost control and output quality start to matter which happens very early in real production teams typically graduate to LangWatch, where evaluations, tracing, and experimentation live in one place.

For quality-critical applications
LangWatch is built specifically for teams where output quality is non-negotiable. It combines continuous AI evaluations with deep production observability and experimentation workflows. Maxim AI focuses on scoring, but requires separate tracing infrastructure, increasing operational overhead.

For teams with existing monitoring stacks
Langfuse is a solid option if you want open-source tooling and full self-hosting control. Datadog makes sense if you’re already deeply invested in their infrastructure monitoring. That said, both lack the tightly integrated evaluation and experimentation workflows that make LangWatch more effective for LLM-native development.

For cost-sensitive deployments
Fine-grained token tracking and cost attribution are essential. LangWatch provides per-request cost visibility, tagging, and alerts that help teams understand why costs increase not just that they did. Langfuse and Helicone track spend, but without the evaluation context needed to drive real optimization. Datadog adds additional platform costs on top of LLM usage.

For complex multi-agent systems
Multi-step chains and agent workflows demand full trace visibility. LangWatch captures end-to-end traces across agents, tools, and chains, then runs evaluations on specific steps to surface where quality breaks down. Langfuse captures traces but lacks built-in evaluation.

For enterprise teams
Security, compliance, and data residency matter. Langfuse supports self-hosting. Datadog and LangWatch both offer enterprise-grade plans with SOC 2 compliance. LangWatch’s advantage is delivering LLM-specific observability and evaluation without the complexity of a general-purpose infrastructure monitoring platform.

For teams shipping fast
LangWatch reduces tool sprawl by unifying monitoring, evaluation, and experimentation. Engineers debug faster because traces, evaluation scores, and prompt versions are visible in one workflow. This is significantly more efficient than stitching together separate logging, evaluation, and experimentation tools.

If you’re running production LLM systems and need more than basic logging, LangWatch offers the most complete approach.

Try LangWatch for free and see how production observability, evaluation, and experimentation work together in practice.

LLM monitoring best practices

Log everything
Capture inputs, outputs, metadata, user identifiers, and timestamps for every request. Storage is cheap; missing data during incidents is not.

Define cost budgets early
Set alerts when token usage crosses 50%, 80%, and 100% of budget thresholds. One runaway prompt can burn thousands overnight.

Automate quality checks
Manual review doesn’t scale. Use automated evaluators to flag risky or low-quality outputs and review only what matters.

Track token efficiency
Rising average token counts usually indicate prompt bloat or unnecessary context. Monitor trends and optimize aggressively.

Version prompts
Every trace should link to a prompt version. When quality drops, you need to know exactly which change caused it.

Decouple logging and evaluation
Log synchronously, evaluate asynchronously. Don’t block user requests to run scorers—batch evaluation keeps systems fast.

Observe full pipelines
Failures often happen outside the LLM call itself. Trace retrieval, tool calls, and post-processing to find real bottlenecks.

Use sampling at scale
For high-volume systems, sample 10–20% of requests for full traces while logging basic metrics for all traffic.

Enable anomaly detection
Alert on unusual patterns like sudden cost spikes, latency jumps, or error-rate changes—before users report issues.

Evaluate on production data
Staging environments miss real-world edge cases. Production traffic reveals what test suites can’t.

Set quality baselines
Track stable-period scores and alert on deviations. Even small drops can signal serious regressions.

Review costs weekly
Weekly spend reviews catch gradual increases early. Investigate any sustained growth over ~20%.

Why LangWatch stands out for LLM monitoring

Most tools force teams to choose between basic logging or assembling multiple platforms. LangWatch delivers monitoring, evaluation, and experimentation in a single system, eliminating context switching and data sync headaches.

This unified approach helps teams detect quality regressions earlier, identify cost optimizations faster, and debug complex agent behavior without jumping between dashboards.

For teams serious about building reliable, cost-efficient AI systems in production, LangWatch offers the most purpose-built solution.

Frequently asked questions

What are LLM monitoring tools?
They track LLM requests by capturing prompts, responses, tokens, costs, and latency. Advanced platforms add trace-based observability across multi-step workflows. LangWatch extends this with built-in evaluations and experimentation.

Why is LLM production monitoring necessary?
LLMs are expensive and unpredictable. Monitoring surfaces cost overruns, quality regressions, and performance issues before users notice. LangWatch helps teams move faster by making these issues visible early.

Monitoring vs. observability—what’s the difference?
Monitoring tracks known metrics. Observability lets you explore unknown failures through detailed traces. LangWatch provides real-time LLM observability with full pipeline visibility.

What are the best LLM monitoring tools in 2026?
Leading options include LangWatch (full monitoring, evaluation, and experimentation), Langfuse (open source), Helicone (proxy-based), Maxim AI (quality-focused), and Datadog (enterprise infrastructure). LangWatch stands out by combining all core LLM workflows in one platform.