Introduction
Observability in software is the ability to understand a system's internal state by analyzing its output. This helps teams diagnose problems, identify bottlenecks, and ensure the system works as intended.
For LLM observability, the focus is on ongoing monitoring, analysis, and assessment of outputs from LLM-based applications in production. Since LLMs behave non-deterministically, observability is essential for tracking their output over time, identifying regressions, catching latency or failure issues, and consistently evaluating response quality and consistency.
For an in-depth exploration of the current state of LLM observability, check out our 2025 guide, which discusses key principles, technical challenges, and leading implementation patterns. These concepts are demonstrated in our customer support benchmarking case study using GPT‑4o and Claude 3.5 within a real-world chatbot, and expanded by an overview of the five best LLM evaluation tools. This overview shows how observability and structured evaluation work together to drive ongoing model improvement across different use cases.
Core Elements of LLM Observability
To monitor and debug LLM applications effectively, you need to understand observability’s essential components. The key building blocks include:
Spans: The single unit of work in an LLM application, like a single call to a chain.
Traces: A set of spans involved in a complete operation. For example, when a chain calls an LLM, and that LLM invokes a tool, all these form one trace.
Project: A group of traces, which organizes observability across multiple projects or use cases.
Why Is LLM Observability Critical?
Here are some reasons observability is vital for LLM applications, particularly in production:
LLMs generate non-deterministic results; the same input can produce different outputs at different times, making behavior unpredictable and hard to replicate or debug.
Observability makes LLM operations fully traceable by recording inputs, outputs, and intermediate steps, allowing teams to review and analyze how unexpected outcomes occurred.
Continuous monitoring detects changes in output over time, which supports ongoing application improvements.
At scale, observability quantifies LLM performance objectively using evaluation metrics, enabling consistent performance tracking.
Observability supports anomaly detection for issues like latency, usage, or costs, and allows custom alerts if these metrics cross a threshold or a particular evaluation fails in production.
With the importance of LLM observability established, let’s review the top five LLM monitoring tools of 2025.
Top 5 LLM Monitoring Tools of 2025
1. Future AGI
Future AGI is a comprehensive platform for LLM observability and evaluation, focused on ensuring reliable, high-performing LLM applications in production. It unifies real-time monitoring, evaluation, anomaly detection, and tracing, which streamlines debugging and quick iteration for various deployment scenarios.
Key Features:
Real-Time Monitoring: Monitors latency, cost, token usage, and evaluation scores for every LLM interaction. Session management helps organize and analyze multi-turn applications.
Alerts & Anomaly Detection: Teams can define custom thresholds for key metrics. If any threshold is breached, alerts are sent to teams by email.
Automated Evaluation: More than 50 built-in evaluation templates and support for custom metrics allow flexible output assessment.
Prototyping: Experiment with prompt chains before deployment to benchmark and optimize.
Open-Source Tracing: The traceAI Python package integrates with frameworks and works with any OpenTelemetry-compatible backend. An npm package is also available for TypeScript.
User Experience: Ten span kinds allow granular trace analysis, and the prototyping environment supports experimentation and confident production releases.
2. LangSmith
Developed by the LangChain creators, LangSmith is an end-to-end platform optimized for both prototyping and monitoring production LLM applications in the LangChain environment. It’s also flexible enough for broader use cases through expanded instrumentation and telemetry exports.
Highlights:
Trace Python or TypeScript Code: Decorators and utilities enable smooth integration into both languages.
OpenTelemetry Support: Collect and export OTel-compliant traces with the SDK.
Integrated Alerts: Threshold-based alerts use integrations such as PagerDuty and webhooks for effective incident management.
3. Galileo
Galileo evolved from an NLP debugging tool into an observability solution for large-scale production LLM pipelines. Its workflow-based UI provides an easy path to insights with little setup complexity.
Distinct Capabilities:
Workflow-Based Observability: Insights are directly available in the Galileo UI, no need for complex trace propagation or exporters.
Alerting: System and evaluation metrics trigger alerts, delivered via email or Slack.
RAG Workflow Evaluation: Automatically monitors chunk-level metrics like context adherence when the SDK is integrated, making RAG evaluation straightforward.
4. Arize AI
Arize AI is a scalable observability platform for enterprise LLM operations, built for flexibility and compatibility with modern AI systems.
Notable Features:
OpenTelemetry Tracing: Allows seamless integration into vendor-neutral observability stacks.
Advanced Alerts: Teams receive notifications about metric shifts or anomalies, integrating with Slack, PagerDuty, and OpsGenie.
Evaluation on Traces: Assesses LLM interactions for output quality and relevance but currently lacks dedicated prototyping capabilities found in Future AGI.
5. Weave by Weights & Biases
Weights & Biases’ Weave brings LLM observability to the established MLOps platform, offering a developer-friendly UI, though with current limitations on OpenTelemetry compatibility.
Key Aspects:
UI for Traces and Runs: Developers can visualize and compare project runs and get up to speed quickly if they’re already familiar with W&B tools.
Streamlined Tracing: The @weave.op decorator allows easy capture of function calls and metadata into hierarchical traces.
OpenTelemetry Limitation: Weave does not generate spans with the OpenTelemetry API, which may affect its integration into vendor-neutral ecosystems.
If your goal is a vendor-neutral, cloud-agnostic, and future-proof solution with native support for standard exporters like OTLP, Jaeger, and Prometheus across LLM and non-LLM systems, then Future AGI’s traceAI is a strong candidate.
Teams working primarily with the LangChain ecosystem may find LangSmith most beneficial. However, the close linkage between LangChain and LangSmith could create interoperability challenges, especially given LangChain’s frequent breaking changes and shifting APIs, which pose maintainability issues.
If you want minimal setup, Galileo is easy to adopt. However, its lack of OpenTelemetry support might be a limitation for teams building vendor-agnostic observability setups.
Arize is excellent for enterprise scalability and vendor neutrality, though it does not have dedicated pre-deployment prototyping, which could affect experimentation workflows before deployment.
Teams already using W&B for ML experiments will find onboarding to Weave straightforward for LLM observability. However, without OpenTelemetry export capabilities, it lacks the flexibility needed for those aiming at cross-platform and future-proof observability.
Conclusion
As LLM-based applications move from research to production, the demand for robust observability platforms continues to grow. Tracing functions and logging output are no longer sufficient; development teams need detailed insights into model behavior, costs, performance, and evaluation metrics at scale.
Each tool reviewed here has its own strengths, but Future AGI stands out for its OpenTelemetry-native architecture and built-in support for evaluation and pre-deployment prototyping. This combination of tracing, evaluation, alerting, and experimentation gives teams the confidence to reliably deliver optimized LLM applications at scale.
Reference
https://futureagi.com/blogs/llm-observability-monitoring-2025
https://futureagi.com/customers/benchmarking-llms-for-customer-support-a-3-day-experiment
https://futureagi.com/blogs/top-5-llm-evaluation-tools-2025
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