Top 8 LLM Observability Tools: Complete Guide for 2025

Large language models are now ubiquitous in production AI applications. If you don't have some AI feature in 2026, are you even a tech company? With AI features hitting production, observability has become critical for building reliable AI products that users can trust. LLM observability goes far beyond basic logging, requiring real-time monitoring of prompts and responses, tracking token usage, measuring latency, attributing costs, and evaluating the effectiveness of individual prompts across your entire AI stack. Without robust observability frameworks, teams face significant risks: AI systems may fail silently, generate harmful outputs, or gradually drift from their intended behavior, degrading quality and eroding trust.

This guide explores the fundamentals of LLM observability, showing what to prioritize when selecting platforms and discovering the leading observability tools in 2025. At LangWatch, we offer a comprehensive LLMOps platform combining observability, evaluations, and optimization capabilities with OpenTelemetry-native integrations. While other solutions may focus on logging alone, LangWatch empowers teams with the full development lifecycle from debugging traces to running experiments to optimizing prompts.

Understanding LLM observability in production

LLM observability monitors Large Language Model behavior in live applications through comprehensive tracking, tracing, and analysis capabilities. LLMs now power everything from customer service chatbots to AI agents that generate code and handle complex multi-step tasks. Observability helps teams understand system performance effectively, detect issues before users notice problems, and maintain operational excellence at scale.

Modern LLM observability extends far beyond traditional application monitoring. They track prompts, responses, and token usage. Teams monitor latency and attribute costs accurately. They analyze error patterns and assess quality. Effective platforms capture complete LLM interaction lifecycles, tracking everything from initial user input to final output delivery, making every step in the AI pipeline visible.

LLM observability combines real-time monitoring with historical analysis to give teams a complete picture. Real-time dashboards track current system performance, alert on anomalies, and visualize model behavior as it happens, while historical analysis identifies trends over time, optimizes performance based on patterns, enables compliance reporting, and supports sophisticated pattern recognition. Advanced platforms combine both approaches intelligently, allowing teams to maintain service quality while iterating quickly on improvements.

The process monitors production performance through detailed metrics, analyzes user interactions, and detects drift before it impacts users. LangWatch provides comprehensive ecosystems that integrate seamlessly with popular AI frameworks through OpenTelemetry standards, helping teams maintain transparency and control at scale.

The mechanics of modern LLM observability

LLM observability uses sophisticated monitoring architectures to capture model behavior across all environments. The process involves three core components: data collection, analysis pipelines, and actionable insights delivery.

Comprehensive data collection and tracing

Observability begins with detailed telemetry capture. This includes prompt content and model responses. Teams track input and output token usage. They measure latency and error rates. Contextual metadata provides additional insights into user sessions, application states, and environmental conditions. Modern observability platforms support tracing across complex workflows, linking operations through structured Traces and Spans to enable complete understanding of multi-step AI agent interactions. Tools like LangWatch have made this level of comprehensive tracing accessible through OpenTelemetry-native integration that works with all major frameworks.

Intelligent analysis and pattern detection

Advanced frameworks implement analysis tools that process monitoring data to identify patterns, detect anomalies, and find optimization opportunities. Cost analysis tracks spending across users and models, performance monitoring identifies latency bottlenecks, and quality assessment detects hallucinations and bias in model outputs. Platforms such as LangWatch have built comprehensive evaluation systems that combine automated scoring with human-in-the-loop workflows to establish baselines and detect deviations from expected behavior.

Actionable insights and alerting

Production systems generate actionable insights through intuitive dashboards that visualize performance trends and cost analytics in real time. Teams configure alerts and triggers for various conditions including cost overruns, latency spikes, and error rate increases that trigger immediate notifications. Advanced platforms like LangWatch use webhooks to integrate with incident management tools such as Slack and PagerDuty, ensuring rapid response when issues arise.

The observability process creates continuous feedback loops that inform model optimization, guide prompt engineering decisions, and enable better infrastructure choices. This approach improves AI application performance while reducing costs and maintaining quality.

Platform Comparison at a Glance

Selecting the perfect LLM observability platform

Choosing the right observability platform is crucial because it determines whether AI applications achieve the reliability users expect. As LLMs become mission-critical components of modern software, visibility into their behavior, performance, and health matters more than ever.

Seamless integration and developer productivity

Effective observability requires frictionless integration with your existing stack. Platforms should support popular frameworks like LangChain, LlamaIndex, Vercel AI SDK, and OpenTelemetry without requiring extensive configuration or code changes. Native SDKs reduce configuration overhead significantly. Automatic instrumentation saves implementation time, letting developers focus on building features instead of setting up monitoring infrastructure. Solutions like LangWatch exemplify this approach by offering OpenTelemetry-native integrations that work across all major frameworks and LLM providers.

Multi-dimensional monitoring capabilities

Choose platforms offering comprehensive monitoring coverage across all dimensions of LLM behavior. Essential capabilities include real-time latency tracking, token usage analytics for cost visibility, error monitoring, and quality assessment of model outputs. Advanced platforms provide alerting that identifies anomalies and recognizes patterns, surfacing issues before they impact users. LangWatch and similar tools have made multi-dimensional monitoring accessible through unified dashboards that present all critical metrics in one place.

Production-grade reliability and alerting

Enterprise tools must support high-availability monitoring that stays up even when other systems fail. They need configurable alerts for performance issues, production dashboards that update in real time, anomaly detection that catches unusual patterns, and automated evaluation gates that help ship without regressions. Integration with tools like Slack and PagerDuty helps teams respond quickly when problems arise.

Scalability and performance impact

Observability platforms must handle production workloads without becoming bottlenecks themselves. They shouldn't impact application performance in ways users notice. Consider tools with asynchronous processing capabilities that don't block requests, intelligent sampling that reduces overhead while maintaining visibility, and cloud-native architecture that ensures low latency for users globally. Platforms like LangWatch demonstrate how properly architected observability can maintain minimal performance overhead even at scale.

Enterprise security and governance

Enterprise deployments require stringent security measures that protect sensitive data and meet compliance requirements. Look for SOC2 certification and GDPR compliance, configurable data retention policies that provide control over how long data is stored, support for self-hosting when regulations require it, and API-level access controls that protect sensitive prompts and outputs. Leading platforms including LangWatch prioritize security certifications and compliance as foundational requirements through their self-hosting options.

A comprehensive platform accelerates development velocity while ensuring production transparency and scalability. Prioritize seamless integration and actionable insights when making your selection.

Leading LLM observability platforms for 2025

The LLM observability landscape has matured significantly over the past year, with platforms now offering sophisticated monitoring and optimization capabilities that serve different monitoring needs. These tools enable transparent AI deployment at scale. Here is our list of the industry-leading platforms:

1. LangWatch

LangWatch defines the category for LLM observability, trusted by leading companies globally including PagBank, Mercari & Skai-Kenshoo. With over 4,000 stars on GitHub and a growing community. LangWatch represents the gold standard for teams building reliable AI applications.

The complete LLMOps platform

LangWatch integrates observability, evaluations, datasets, and prompt optimization seamlessly into a single platform, eliminating the development friction that comes from juggling multiple tools. Engineers flow smoothly from analyzing traces to running batch experiments to optimizing agents, fundamentally changing how teams iterate on AI systems.

The platform has a unique and lovable way to connect the UI and code, enabling both technical and non-technical team members to collaborate effectively. Product managers contribute through intuitive interfaces while engineers maintain full code/API control for complex evaluations, prompts and agents.

Framework integration - OpenTelemetry-native ecosystem

LangWatch is framework- and LLM-agnostic with native OpenTelemetry support that works seamlessly with Google ADK, DSPy, Langflow, Vercel AI SDK, LangFlow, Agno, and others. LLM provider integrations include OpenAI, Azure, Bedrock, Gemini, Deepseek, Groq, MistralAI, VertexAI, LiteLLM, and more. This use of open and community-driven standards enables teams to stay flexible in the rapidly changing AI ecosystem without worrying about vendor lock-in or compatibility issues.

Integration requires minimal configuration effort. For example, the Vercel AI SDK integration is a simple way to add to your OpenTelemetry setup. Implementation takes minutes, not weeks, helping teams achieve comprehensive observability quickly without derailing feature development.

Each integration is built for production AI applications, delivering out-of-the-box cost tracking, latency monitoring, evaluation metrics, production alerts, and full tool tracing. Framework-specific optimizations are preconfigured, so everything works immediately—no guesswork required.

Production-first architecture and performance

LangWatch's architecture is built for production scale with asynchronous processing that doesn't block requests. The platform captures every LLM call, tool usage, and user interaction with detailed Traces, Spans, and Threads that make the complete conversation flow visible. Cloud-native deployment ensures low latency regardless of user location, while intelligent filtering optimizes system overhead.

For teams with strict data residency or security requirements, LangWatch offers comprehensive self-hosting options through Docker or Helm-Chart deployment, giving complete control over data storage and processing while maintaining access to the full platform capabilities. LangWatch is available through the AWS, Azure of Google Marketplace and offer a flexible Hybrid hosting option through these.

Security forms the foundation of LangWatch architecture, not an afterthought. ISO27001 certification ensures robust controls, GDPR compliance protects data privacy across jurisdictions, and API-level controls provide comprehensive protection for sensitive prompts and model outputs.

AI agent testing and simulations

AI agent testing capabilities through LangWatch Scenario framework. Teams can create agentic tests (scenario's) that simulate real user interactions, with User Simulator Agents that automatically generate realistic conversations and Judge Agents that evaluate outcomes in real-time. This enables comprehensive testing of multi-turn agent behaviors that traditional testing approaches miss entirely.

Simulation Sets group related scenarios into test suites, while Batch Runs execute all scenarios together in CI/CD pipelines. Debug mode allows developers to step through conversations turn-by-turn, intervening with their own inputs to debug agents from the middle of interactions.

Collaborative evaluation system

LangWatch offers both real-time evaluation that runs on production traffic and offline evaluation for batch testing against datasets. The platform includes built-in evaluators for common quality checks like faithfulness, relevance, toxicity, and PII detection, while also supporting custom evaluators written in code.

Teams can evaluate via code for programmatic workflows or use the UI for visual evaluation building. The evaluation system integrates directly with traced production data, making it easy to convert real user interactions into test cases and golden datasets.

You can run evaluations side-by-side with prompts and agents, all in one place.
It’s powerful  for engineers, with a full code counterpart that runs in your CI/CD — and simple enough for PMs to iterate without touching code. It feels like a tabular format great for Data Scientist/Engineers to collaborate with PM's / Developrs.

Revolutionary observability approach for AI systems

Traditional monitoring breaks down with AI applications because it assumes software behaves predictably. LLM-powered systems don’t. Their behavior is probabilistic, context-dependent, and constantly evolving. LangWatch is built for that reality, treating uncertainty as a first-class concern rather than an edge case.

Multi-dimensional monitoring makes the entire AI lifecycle observable. LangWatch tracks inputs, intermediate behavior, and outputs end to end—across preprocessing, prompt construction, model configuration, tool calls, raw model responses, post-processing logic, and the final user-facing result. Every request is fully traceable, giving teams complete visibility into how decisions are made and where things go wrong.

Semantic monitoring goes beyond system metrics to evaluate what actually matters: output quality. Automated evaluations score responses for factual correctness, relevance to user intent, safety, and format compliance—at a scale that manual review simply can’t reach. Combined with real user feedback as ground truth, this creates a continuous feedback loop that turns production data into actionable improvements.

Human-in-the-loop workflows

LangWatch's annotation system enables collaborative data review between engineers and domain experts. Teams can inspect production traces, add labels, score outputs, and share insights across engineering, product, and business stakeholders. These annotations feed directly into evaluation datasets and help identify edge cases that automated systems miss.

The platform accelerates better data creation by combining domain expert input with smart workflows, helping teams quickly generate high-quality labels for fine-tuning and evaluation purposes.

Flexible alerting and triggers

LangWatch provides comprehensive alerting and triggers that notify teams when issues arise. Configure alerts for quality regressions, cost overruns, latency spikes, or custom conditions based on your business logic. Alerts integrate with Slack, Teams and other tools via webhooks, ensuring teams get notified through their preferred channels.

Triggers can also enforce automated guardrails, blocking problematic outputs before they reach users or automatically routing certain interactions for human review.

User event tracking

Beyond basic tracing, LangWatch tracks detailed user events including thumbs up/down feedback, whether users waited for responses, text selection events, and custom events. These events provide crucial signals about user satisfaction and engagement that inform evaluation strategies and guide improvements.

Proven track record and customer success

Teams using LangWatch report shipping AI agents 8x faster through the platform's integrated workflow from observability to agent evaluations. Customer testimonials highlight how the platform brings the kind of progress they were hoping for, with some noting it reminded them of how they used to evaluate models in classical machine learning—systematic, measurable, and reliable.

Leading companies across industries trust LangWatch to maintain quality at enterprise scale, demonstrating the platform's effectiveness for production deployments serving millions of interactions.

2. Arize

Arize delivers advanced AI observability with comprehensive support for LlamaIndex, LangChain, DSPy, and multiple model providers. OpenTelemetry instrumentation provides flexibility for custom integrations. Phoenix evolved from ML monitoring origins, bringing mature practices to LLM observability.

Advanced drift detection: Phoenix monitors embedding drift effectively, tracking changes in vector representations that indicate semantic shifts in model behavior. Traditional metrics miss these subtle but important issues that can degrade quality over time.

RAG-specific observability: The platform monitors RAG pipelines with special attention to retrieval quality. It analyzes retriever performance thoroughly, making knowledge gaps apparent quickly. Content relevance tracking improves the quality of retrieved documents.

Enterprise monitoring infrastructure: Phoenix supports production-grade deployment reliably with real-time dashboards that provide immediate visibility, automated alerting that catches issues early, and cost attribution that enables effective budget management.

3. Comet Opik

Comet's Opik provides comprehensive LLM observability with native support for OpenAI, LangChain, LlamaIndex, DSPy, and agent frameworks. The platform integrates smoothly with existing ML workflows for teams already using Comet.

Open-source transparency: The open-source foundation ensures transparency and allows teams to self-host completely when requirements demand it. Managed services reduce operational overhead for teams that prefer cloud hosting. Enterprise features add value without sacrificing openness.

Development workflow integration: The platform integrates with CI/CD pipelines naturally. Automated monitoring catches regressions early in development. Pull request validation prevents quality issues from reaching production.

3. Helicone

Helicone provides proxy-based observability supporting OpenAI, Anthropic, and Google Gemini with a focus on simplicity. The platform makes monitoring accessible to teams without deep observability expertise.

Instant monitoring architecture: The proxy approach enables immediate observability by simply changing API endpoints without touching application code. Existing applications gain complete visibility instantly, making Helicone good for teams that need monitoring now.

Multi-provider visibility: Unified monitoring spans multiple model providers, letting you compare performance and costs easily across OpenAI, Anthropic, and others. Quality metrics appear in unified dashboards, providing a single view that simplifies management across your entire AI stack.

Cost intelligence: Helicone is strong at cost tracking with detailed usage analytics, budget alerts that prevent overruns, and virtual API keys that enable accurate cost attribution across teams, projects, or customers.

4. Langfuse

Langfuse offers open-source integrations with support for OpenAI, LangChain, and LlamaIndex, providing essential observability features while maintaining complete transparency through its open-source model.

Transparency: Open-source code provides transparency that proprietary tools can't match. Teams customize monitoring logic freely to fit their specific needs. Functionality extensions are possible when standard features aren't enough. Complete control remains with your team.

Flexible deployment options: Various deployment models provide flexibility for different requirements. Self-hosting maintains complete control over data and infrastructure. Cloud deployment reduces operational overhead for smaller teams. Hybrid architectures meet complex requirements.

5. MLflow

MLflow provides enhanced LLM support that it beyond traditional ML workflows, bringing experiment tracking to modern AI systems. Auto-tracing works with major frameworks while multi-provider monitoring adds value for teams using multiple models.

ML/AI observability: MLflow monitors both classical ML models and modern LLMs from a single platform, simplifying toolchain management for teams working across both paradigms. Classical models and LLMs coexist in the same workflow.

Lifecycle management: End-to-end versioning tracks everything from experiments to production deployments. Experiment tracking captures all changes and their results. Deployment monitoring ensures reliability once models ship to users.

Enterprise infrastructure integration: MLflow integrates seamlessly with existing enterprise infrastructure including data warehouses and business intelligence tools, connecting AI observability to broader analytics workflows naturally.

6. Galileo AI

Galileo provides enterprise-focused AI observability with support for major LLM providers and comprehensive agent framework integration, serving organizations with strict security and compliance requirements.

Enterprise security focus: Advanced access controls protect sensitive data throughout the platform. Comprehensive audit logging tracks all activity for compliance purposes. Industry compliance certifications meet regulatory requirements for enterprises in regulated industries.

Custom monitoring models: Proprietary models like Luna-2 enhance assessment accuracy for specific monitoring tasks. Purpose-built models improve consistency compared to general-purpose LLMs used for evaluation.

7. DeepEval

DeepEval focuses on testing framework integration with pytest-like functionality designed specifically for LLM applications. Basic LlamaIndex support works adequately, with development monitoring receiving priority over production observability.

Synthetic data monitoring: Synthetic test datasets enable comprehensive coverage even when real-world data is limited or unavailable. Testing becomes possible early in development before production data exists.

CI/CD pipeline integration: Development pipelines integrate seamlessly with automated monitoring that prevents regressions. Quality gates protect production by catching issues before deployment.

8. RAGAS

RAGAS provides specialized RAG observability with deep LlamaIndex support and basic LangChain compatibility, focusing exclusively on RAG monitoring rather than trying to cover all LLM use cases.

RAG-specific metrics: Research-backed metrics serve RAG systems with measurements like faithfulness scoring that ensures accuracy and answer relevancy scoring that improves quality. Specialized metrics outperform generic evaluation approaches.

Research-backed methodology: Academic research validates monitoring approaches through peer-reviewed studies. Best practices guide implementation, giving teams confidence in evaluation methods.

Specialized domain focus: RAG applications receive unmatched depth of monitoring. Document-heavy systems benefit greatly from specialized approaches. Focus on one domain enables excellence that generalist tools can't match.

The future of AI transparency: Why choose LangWatch?

LLMs now form the foundation of modern AI applications, making comprehensive observability mission-critical for any team shipping AI features to production. The right observability platform provides the essential visibility that lets teams deploy AI systems confidently, knowing they can understand and control behavior even as complexity scales.

LangWatch extensive integration ecosystem is one of the most comprehensive in the space, combining breadth with real production depth. The platform is engineered for reliability at scale, holding up under demanding, real-world workloads where AI systems are constantly changing. While specialized tools like RAGAS and DeepEval perform well within their specific niches, LangWatch brings those capabilities together into a unified platform—designed to support end-to-end evaluation, monitoring, and continuous improvement across the full AI lifecycle.

Choose platforms with comprehensive framework integration that works with your existing stack through open standards like OpenTelemetry. Production monitoring capabilities matter significantly since development-only tools create blind spots. Operational workflow alignment saves time by reducing context switching. Robust observability infrastructure pays substantial dividends through dramatically decreased debugging time and measurably improved operational efficiency.

The observability landscape continues evolving rapidly as LLMs become more complex and widely deployed. Open standards remain essential as new frameworks emerge. Production-grade reliability ensures success when stakes are high. Developer-friendly integration accelerates adoption across teams and as your team scales, more less technical people will join (PM's). Choose tools that scale appropriately with your ambitions while providing transparency and control, then build AI deployments that users trust.

Frequently asked questions

What are the best LLM observability tool alternatives to Arize Phoenix?

LangWatch provides a comprehensive alternative with OpenTelemetry-native support that works across all major frameworks and LLM providers. The platform uniquely combines observability with evaluation, agent testing, and prompt optimization in a single workflow. Teams switching to LangWatch gain access to advanced features like agentic testing with User Simulator and Judge Agents (agent simulations), DSPy optimization capabilities, and collaboration between dev's & product manager workflows. The source-available foundation ensures transparency while providing self-hosting options for maximum control.

Which LLM observability platforms detect quality drift most effectively?

LangWatch provides comprehensive monitoring tools combined with evaluation capabilities that help teams identify drift and quality issues systematically. Teams can set up real-time evaluations that run on production traffic, configure alerts for quality degradation based on score thresholds, and use the annotation system to have domain experts review edge cases. The platform's integration with user events like thumbs up/down feedback provides additional signals about quality changes. By converting production traces into datasets and running batch evaluations, teams can track quality metrics over time and catch drift before it impacts users.

What's the most comprehensive LLM evaluation and observability platform?

LangWatch provides the only fully open-source platform that combines observability, evaluation, agent testing, datasets, annotations, and prompt optimization in a single integrated system. Teams eliminate multiple disconnected tools that create friction and data silos. The workflow efficiency improves dramatically when traced production data flows directly into evaluation datasets, which inform optimization experiments that deploy back to production. The platform's OpenTelemetry-native approach ensures compatibility with existing tools while providing specialized AI-focused capabilities.

Which LLM observability platforms support full on-premise deployment?

LangWatch offers a unique hybrid architecture that cleanly separates the data plane from the control plane. All sensitive data, logs, traces, prompts, and model inputs/outputs, stays entirely within your own infrastructure. The data plane can be deployed in your existing AWS, Azure, Google, or on-prem environment using Terraform or Docker, giving teams full ownership from day one.

The hosted LangWatch UI acts purely as a control and visualization layer, querying your data plane securely at runtime without persisting customer data. This design delivers strong data isolation without sacrificing the speed and convenience of a managed product experience.

As a result, teams retain complete control over data residency, retention policies, access controls, and infrastructure decisions—while still benefiting from a modern, centralized platform for AI evaluation, monitoring, and testing.

How do LLM observability tools secure sensitive prompt data?

Enterprise platforms like LangWatch implement encryption for data at rest and in transit to protect sensitive information. Role-based access controls ensure that only authorized team members can access specific projects and data. For teams with strict data residency requirements, self-hosting options ensure that prompts, responses, and user data never leave your infrastructure. The platform's architecture separates concerns so that even in cloud deployments, teams maintain control over sensitive data through proper configuration.

What hybrid deployment options exist for LLM observability platforms?

LangWatch supports flexible deployment architectures including fully cloud-hosted for operational simplicity, fully self-hosted via Docker for maximum control, and hybrid approaches where teams can use cloud features while keeping sensitive data on-premise. The OpenTelemetry-native approach means teams can start with one deployment model and migrate to another as requirements change, without rewriting instrumentation code or losing historical data.

Which LLM observability tools integrate with OpenTelemetry?

LangWatch is built on OpenTelemetry standards from the ground up, making it the most comprehensive OpenTelemetry-native LLMOps platform. The platform automatically understands OpenTelemetry traces and spans, extracting LLM-specific information without manual configuration. This native integration works seamlessly with any framework or tool that supports OpenTelemetry, providing maximum flexibility and future-proofing as the ecosystem evolves. Teams using OpenTelemetry instrumentation can send traces to LangWatch and immediately gain AI-specific insights like cost tracking, quality evaluation, and agent behavior analysis.

What LLM observability platforms work best with LangChain?

LangWatch provides seamless LangChain integration through OpenTelemetry that captures complete workflow context including all intermediate steps in chains and agents. The platform supports both LangChain and LangGraph, with specialized handling for agent workflows that involve loops, conditional execution, and tool usage. Setup requires minimal configuration, and the integration automatically captures prompts, responses, tool calls, and metadata without manual instrumentation.

How do LLM observability platforms integrate with existing monitoring tools?

LangWatch integrates with existing monitoring infrastructure through webhooks and standard alerting mechanisms. Teams can send alerts to Slack, PagerDuty, and other tools when specific conditions are met in production traces or evaluation results. The platform's export capabilities allow analytics data to be embedded in existing dashboards and BI tools. OpenTelemetry support means teams can send traces to multiple destinations simultaneously, maintaining existing observability stacks while gaining AI-specific insights from LangWatch.

Which LLM observability platforms offer the best free tiers?

LangWatch provides a generous free tier that includes observability, evaluation, and optimization features without requiring a credit card initially. The open-source nature means teams can also self-host for free with no usage limits, paying only for infrastructure costs. This removes friction from getting started and allows thorough evaluation before committing to paid plans. Production features work immediately rather than being artificially limited, letting teams build confidence in the platform before scaling.

What's the best LLM observability tool for mid-size engineering teams?

LangWatch scales effectively for teams of ten to fifty engineers through its combination of intuitive UI for non-technical users and comprehensive API for programmatic access. Usage-based pricing prevents overpaying during experimentation while scaling naturally as applications grow. The platform's role-based features ensure developers get powerful debugging tools, domain experts can contribute through annotations and evaluations, and business teams gain analytics visibility—all without creating access bottlenecks or forcing difficult decisions about who can use the platform.

How do LLM observability platforms handle latency spikes?

LangWatch captures detailed latency metrics for every trace and span, making it easy to identify which components cause slowdowns. The trace view shows timing breakdowns across each step in multi-step agent workflows. Teams can configure alerts that trigger webhooks when latency exceeds defined thresholds, ensuring rapid response to performance degradation. Historical analysis helps identify patterns and trends in latency over time, while real-time monitoring catches spikes as they happen.

Which LLM observability platforms best support team collaboration?

LangWatch excels at cross-functional collaboration through features designed for different roles. Engineers work programmatically through comprehensive SDKs and APIs. Domain experts use the annotation system to review traces, add labels, and provide feedback without writing code. Product managers access analytics dashboards showing quality metrics, user engagement, and cost tracking. The evaluation system allows both technical and non-technical team members to build and run tests, creating shared ownership of quality. Comments and annotations enable discussions directly on traces and evaluation results, keeping context centralized.

What's the best LLM observability tool for healthcare applications?

Healthcare requires strict compliance features that meet regulatory requirements for patient data. LangWatch offers self-hosted deployment options with complete data isolation that ensure sensitive health information never leaves your infrastructure. The open-source foundation allows security teams to audit the code and customize deployment to meet specific compliance requirements. Comprehensive audit logging tracks all access and modifications for regulatory review. The evaluation system enables rigorous quality checks that are essential when errors can have serious consequences for patient safety.

How do LLM observability platforms support prompt experimentation?

LangWatch uniquely combines experimentation with production monitoring through integrated workflow. The Prompt Playground allows rapid iteration and testing of prompt variations before deployment. Teams can run batch evaluations comparing different prompts across datasets to see which performs best. The Optimization Studio with DSPy enables systematic prompt improvement using structured experimentation rather than manual trial and error. Production traces automatically feed into datasets that power experiments, and successful experiments deploy seamlessly back to production, creating a continuous improvement loop.

Which LLM observability tools act as AI proxies?

While LangWatch focuses on observability and evaluation rather than proxying, it integrates with proxy solutions and captures telemetry regardless of how LLM calls are made. The OpenTelemetry-native approach means teams using any proxy or gateway can send traces to LangWatch for analysis and evaluation. This architecture provides flexibility to use best-of-breed tools for different purposes rather than forcing all functionality through a single gateway.

What's the easiest LLM observability platform to implement?

LangWatch provides quick integration for most modern frameworks through OpenTelemetry instrumentation. For many setups, adding LangWatch requires just installing the SDK and adding the LangWatch exporter to existing OpenTelemetry configuration. The platform provides detailed quickstart guides for popular frameworks including Vercel AI SDK, LangChain, and others. Most teams get basic observability working within an hour, then gradually add advanced features like evaluations and optimization as they become familiar with the platform.

What are implementation best practices for LLM observability tools?

Start with basic observability to understand system behavior before adding advanced features. Instrument your main LLM calls first, then expand to capture tool usage and multi-step workflows. Set up alerts conservatively to avoid alert fatigue from too many false positives. Convert interesting production traces into test cases and datasets as you encounter them, building evaluation coverage organically. Review metrics regularly in team meetings to build shared understanding of quality trends. Establish baseline metrics early before problems occur so you know what normal looks like. Use the annotation system to capture domain expert knowledge as issues are discovered. Start with built-in evaluators before investing time in custom ones, and iterate based on what actually matters for your application.