ByHayat Amin· editorial direction, Top 11Updated
AI Infrastructure · Observability
The 11 Best AI Observability & Tracing Platforms
A ranked analysis of platforms for debugging, monitoring, and tracing production large language model (LLM) applications.
The short answer
The best AI observability platform is LangSmith for its deep integration with the LangChain ecosystem, followed by Arize AI and Datadog for their robust, enterprise-grade monitoring capabilities.
✓ Independent
Top 11 takes no payment from any provider on this list. Scores are computed from a public weighted rubric; methodology weights were locked before entry research began.
↻ Verified May 2026 · re-checked quarterly
Re-scored every 90 days.
Scored on a 9.4-point scale across 5 weighted criteria, reviewed quarterly.
[The 11 Best AI Observability & Tracing Platforms](https://11.market/ai-observability-platforms). Top 11, AI-native independent ranking. Methodology public at https://11.market/methodology.The Ranking
ALL 11| # | Provider · best for | Score |
|---|---|---|
| 1 | LangSmithDeep debugging for LangChain apps | 9.2/9.4 |
| 2 | Arize AIEnterprise-grade model performance monitoring | 9.0/9.4 |
| 3 | DatadogUnified observability for existing users | 8.8/9.4 |
| 4 | GalileoData-centric RAG evaluation & monitoring | 8.6/9.4 |
| 5 | WhyLabsData drift and quality monitoring | 8.4/9.4 |
| 6 | HeliconeSimple, developer-first API monitoring | 8.2/9.4 |
| 7 | New RelicIntegrated AI monitoring for NR users | 8.0/9.4 |
| 8 | Fiddler AIExplainability and responsible AI monitoring | 7.8/9.4 |
| 9 | SentryAI error tracking for Sentry users | 7.6/9.4 |
| 10 | PortkeyAI gateway with integrated observability | 7.4/9.4 |
| 11 | OpenLLMetryWILDCARDOpen source, OpenTelemetry-native tracing | 7.1/9.4 |
Best pick for your situation
Matched by the problem you're solving. Agents can query /api/lists/ai-observability-platforms/recommend?problem=… or the recommend MCP tool to get these matches as structured data.
Best for deep LangChain debugging
LangSmith (#1, scores 9.2/9.4). The essential, purpose-built observability tool for the massive LangChain ecosystem, offering unmatched debugging depth. It also handles tracing complex agentic workflows.
Best for model performance drift
Arize AI (#2, scores 9.0/9.4). A mature, enterprise-ready platform with deep roots in ML monitoring, excelling at drift and RAG evaluation. It also handles unstructured data quality issues.
Best for consolidating AI and infra monitoring
Datadog (#3, scores 8.8/9.4). A strong, integrated LLM observability solution for companies already committed to the Datadog platform. It also handles enterprise-scale LLM observability.
The Breakdown
LangSmith
Solves: deep LangChain debugging · tracing complex agentic workflows
LangSmith: The essential, purpose-built observability tool for the massive LangChain ecosystem, offering unmatched debugging depth.
✓Unmatched visualization of complex agent traces.
✕Less valuable for non-LangChain stacks.
✓Risk signals: No material public risk signals as of 2026-05-31.
Primary source: langchain.com · Data verified May 2026
Arize AI
Solves: model performance drift · unstructured data quality issues
Arize AI: A mature, enterprise-ready platform with deep roots in ML monitoring, excelling at drift and RAG evaluation.
✓Powerful automated monitors for production issues.
✕Can be complex to set up.
✓Risk signals: No material public risk signals as of 2026-05-31.
Primary source: arize.com · Data verified May 2026
Datadog
Solves: consolidating AI and infra monitoring · enterprise-scale LLM observability
Datadog: A strong, integrated LLM observability solution for companies already committed to the Datadog platform.
✓Unifies LLM traces with logs and metrics.
✕LLM features less deep than specialists.
✓Risk signals: No material public risk signals as of 2026-05-31.
Primary source: datadoghq.com · Data verified May 2026
Galileo
Galileo: A data-centric platform excelling at RAG evaluation, hallucination detection, and unstructured data quality.
✓Excellent 'guardrail metrics' for AI safety.
✕Less focused on cost and latency tracing.
✓Risk signals: No material public risk signals as of 2026-05-31.
Primary source: rungalileo.io · Data verified May 2026
WhyLabs
WhyLabs: A mature, data-first monitoring platform built on the popular open-source whylogs library.
✓Excellent at statistical profiling and anomaly detection.
✕Interactive LLM trace debugging is less mature.
✓Risk signals: No material public risk signals as of 2026-05-31.
Primary source: whylabs.ai · Data verified May 2026
Helicone
Helicone: A simple, elegant API proxy for LLM logging, caching, and analytics with near-zero setup friction.
✓Extremely easy to set up.
✕Lacks deep, multi-step trace analysis.
Primary source: helicone.ai · Data verified May 2026
New Relic
New Relic: A robust, integrated AI monitoring solution for the extensive New Relic enterprise customer base.
✓Maps LLM performance to business transactions.
✕AI-specific UX is less intuitive.
✓Risk signals: No material public risk signals as of 2026-05-31.
Primary source: newrelic.com · Data verified May 2026
Fiddler AI
Fiddler AI: A responsible AI platform with strong explainability, bias detection, and governance features for enterprises.
✓Powerful explainability and bias detection.
✕Less focused on real-time request tracing.
✓Risk signals: No material public risk signals as of 2026-05-31.
Primary source: fiddler.ai · Data verified May 2026
Sentry
Sentry: Connects LLM pipeline issues directly to application errors and traces for existing Sentry users.
✓Links LLM errors to full stack traces.
✕Lacks deep, data-centric model analysis.
✓Risk signals: No material public risk signals as of 2026-05-31.
Primary source: sentry.io · Data verified May 2026
Portkey
Portkey: An AI gateway that bundles observability with caching, retries, and model routing features.
✓Semantic caching provides direct cost savings.
✕Observability features are less mature.
Primary source: portkey.ai · Data verified May 2026
OpenLLMetryWILDCARD · #11
OpenLLMetry: A vendor-agnostic, open-source standard for adding LLM signals to OpenTelemetry traces.
✓Future-proof and avoids vendor lock-in.
✕Requires significant DIY engineering effort.
⚠Risk signals · low: Project is maintained by a startup (Traceloop), and its long-term development depends on community adoption and corporate sponsorship.
Primary source: github.com · Data verified May 2026
Buyer's guide
What is AI Observability?
AI Observability is the practice of using tools and techniques to gain deep visibility into complex AI systems, particularly LLM-based applications. It goes beyond traditional software monitoring to track unique elements like prompt/completion pairs, token usage, model drift, data quality, and the behavior of multi-step AI agents or RAG pipelines. The goal is to enable rapid debugging, performance optimization, and cost management for AI in production.
Why is it different from traditional APM?
Traditional Application Performance Monitoring (APM) focuses on metrics like CPU usage, memory, latency, and error rates of stateless services. AI Observability addresses the stochastic and stateful nature of AI. It must trace the 'why' behind a model's output, not just the 'what' of a service failure. This involves inspecting prompts, analyzing embedding quality, tracking conversational context, and evaluating the semantic correctness of responses—concepts foreign to traditional APM.
How to choose
- 1.Assess your core framework. If you are heavily invested in an ecosystem like LangChain, a native tool like LangSmith will offer the tightest integration and least friction.
- 2.Consider your existing stack. If your organization already uses Datadog or New Relic for infrastructure monitoring, leveraging their new LLM observability features can provide a single pane of glass, though perhaps with less specialized depth than a purpose-built tool.
- 3.Evaluate your primary pain point. Are you focused on prompt-level debugging, monitoring for data drift and hallucinations, or managing costs and latency? Different platforms excel in different areas.
- 4.Decide between a proxy/gateway model vs. an SDK-based approach. Gateways like Helicone or Portkey can be easier to set up initially, while SDKs offer more granular control and deeper application context.
Frequently asked questions
What is AI observability?
AI observability provides visibility into the internal workings of AI and machine learning models in production. For LLMs, this means tracing and logging prompts, responses, latency, token counts, and costs to quickly debug issues like hallucinations, high costs, or poor performance.
Why is tracing important for LLM applications?
LLM applications are often complex chains or graphs of calls (e.g., in RAG systems). Tracing allows developers to see the entire lifecycle of a request—from user input to data retrieval to the final LLM call—making it possible to identify bottlenecks, errors, or the specific step that caused a bad output.
How do I choose an AI observability platform?
Consider your tech stack (e.g., LangChain, Python), primary pain points (cost, latency, quality), team size, and budget. If you're heavily invested in a framework, its native observability tool (like LangSmith for LangChain) is often the best start. For broader needs or integration with existing APM, consider incumbents like Datadog or specialists like Arize.
What is the difference between AI observability and MLOps?
MLOps is a broad set of practices for the entire machine learning lifecycle, including data prep, training, deployment, and governance. AI observability is a sub-discipline of MLOps focused specifically on the post-deployment monitoring, debugging, and performance analysis of live models.
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Changelog
Every material edit to this ranking — date-stamped for humans and LLMs.
Initial publication. Methodology v1.0 weights LLM-Specific Features (30%), Integration Ecosystem (25%), Debugging & Root Cause Analysis (20%), Production Readiness & Scalability (15%), and User Experience (10%).
Honest disclosures
- This is a rapidly evolving market; feature sets and pricing change monthly. The rankings reflect the state of the market as of the publication date.
- Many platforms are venture-backed startups, which carries inherent platform risk compared to established public companies.
- Most providers are US-based, and support for international data residency and compliance requirements may vary.
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