Best of · AI
Top 8 LLM Observability Platforms for 2026
Building with LLMs introduces unique observability challenges beyond traditional metrics, from tracking token costs to evaluating response quality and detecting hallucinations. This guide ranks the top LLM Observability platforms that provide deep insights into prompt engineering, model performance, and user interactions. We evaluated these tools based on their integration capabilities, advanced debugging features, comprehensive cost tracking, and robust support for the modern AI stack, including RAG architectures.
- 1
LangSmith
Developed by the team behind LangChain, LangSmith is an observability and testing platform purpose-built for LLM applications. It provides detailed tracing of complex chains and agents, allowing developers to visualize execution flows, debug errors, and manage datasets for testing and fine-tuning.
Why it stands out: Choose LangSmith if you are building with LangChain for its seamless, native integration and unparalleled debugging capabilities for complex agentic workflows and RAG applications.
- 2
Arize AI
Arize is a mature machine learning observability platform that has extended its powerful capabilities to the LLM space. It excels at production monitoring, offering features for tracking prompt/response drift, detecting hallucinations, and setting up automated monitors and evaluations, including RAG-specific metrics.
Why it stands out: Pick Arize AI for robust, enterprise-grade production monitoring and automated evaluation of your LLM's performance and output quality, especially for detecting drift and hallucinations.
- 3
Weights & Biases
Primarily known as an MLOps platform for experiment tracking, Weights & Biases (W&B) offers a comprehensive suite of tools for the LLM lifecycle. Its 'Prompts' feature allows teams to debug, compare, and manage prompt templates, model outputs, and RAG retrievals systematically.
Why it stands out: W&B is ideal for teams that need to tightly integrate their LLM development, prompt engineering, and fine-tuning workflows with traditional ML experiment tracking.
- 4
Datadog
A leader in general infrastructure and application monitoring, Datadog has integrated LLM observability into its APM suite. It allows teams to monitor LLM performance, costs, and traces alongside the rest of their infrastructure, from cloud services to application code, often leveraging OpenTelemetry.
Why it stands out: Opt for Datadog if your organization is already heavily invested in its ecosystem and you want a single pane of glass for all observability needs, including LLM-powered services.
- 5
Helicone
Helicone is an open-source-first observability platform designed specifically for LLM applications, acting as an intelligent proxy. It provides request monitoring, caching, rate limiting, and user-centric analytics with a focus on ease of setup and developer experience, now with enhanced RAG support.
Why it stands out: Helicone is a great choice for startups and developers looking for a lightweight, open-source-friendly solution with essential cost, performance monitoring, and advanced caching features.
- 6
Galileo
Galileo is an AI data intelligence platform that focuses on improving unstructured data quality, which is critical for LLMs. It provides tools to quickly detect and fix issues like hallucinations, prompt toxicity, and data leakage during both development and production, with specialized RAG evaluation capabilities.
Why it stands out: Choose Galileo when your primary concern is the semantic quality and safety of your LLM's inputs and outputs, especially for hallucination detection and RAG evaluation.
- 7
New Relic
Similar to Datadog, New Relic is an established observability giant that has added LLM monitoring to its platform. It automatically instruments popular LLM libraries to provide insights on response times, token counts, and errors within the context of your application's transactions, often via OpenTelemetry.
Why it stands out: Select New Relic if it's already your organization's standard for APM and you want to seamlessly extend monitoring to your AI services, including detailed LLM insights.
- 8
WhyLabs
WhyLabs offers a data and AI monitoring platform complemented by the open-source LangKit library. LangKit extracts key signals like text quality, security risks, and relevance from LLM prompts and responses, which can then be monitored on the WhyLabs platform, now with enhanced RAG context analysis.
Why it stands out: WhyLabs is a strong option for teams that want a hybrid approach, using a powerful open-source library for data extraction with a managed platform for monitoring and AI guardrails.
Frequently asked questions
What is LLM Observability and how does it differ from traditional APM?
LLM Observability is a specialized practice of monitoring and understanding applications powered by Large Language Models. While traditional APM (Application Performance Monitoring) tracks metrics like latency, error rates, and resource usage, LLM Observability adds a new layer, focusing on prompt/response tracing, token consumption costs, model quality evaluations (e.g., hallucination detection), and data privacy, often incorporating advanced AI-driven anomaly detection.
Why can't I just use standard logging tools for my LLM app?
You can use standard tools like Splunk or an ELK stack to log raw API calls, but you'll miss crucial context. LLM Observability platforms are purpose-built to understand the structure of LLM interactions, automatically parsing prompts, responses, and tool usage. They provide out-of-the-box features for advanced cost analysis, semantic search over logs, and sophisticated quality evaluation (including RAG specific metrics) that would require significant engineering effort to build from scratch.
What is the difference between an LLM Gateway and an LLM Observability platform?
An LLM Gateway (like Portkey or LiteLLM) acts as a proxy to manage and standardize requests to various LLM providers, focusing on routing, load balancing, caching, and key management. An LLM Observability platform is the destination for the data generated by these requests, focusing on analysis, debugging, and monitoring. The two are complementary: you often route requests through a gateway which then sends logs and traces to an observability platform, often via OpenTelemetry standards.