LLM Observability: The 8 Best Tools for Production AI Systems

If you're evaluating LLM observability tools, you already know why you need one. Your team is running AI agents or LLM-backed features in production, and when something breaks (a hallucinated response, a runaway token bill, or an agent that calls the wrong tool three times before giving up), you need to know why, fast.

The real question in front of you isn't whether to add observability. It's whether to bring in a specialized, AI-native tool built specifically for LLM and agent tracing or extend the observability platform you already run into the LLM layer. Both paths work. But they come with different trade-offs in setup time, ongoing maintenance, and how many dashboards your team has to check during an incident.

This guide compares eight leading LLM observability tools for production AI systems so you can weigh what each gets right, where it falls short, and which team it fits best.

The best LLM observability tools for production AI systems

Before comparing tools feature by feature, it helps to agree on what you're measuring each one against. This guide focuses on the following criteria:

• Tracing and span depth: How much detail you get into each LLM call, tool call, and retrieval step
• Evaluation and quality scoring: Whether the tool can score outputs automatically (LLM-as-a-judge, semantic similarity, automated evaluators) and support human review
• Cost tracking: How clearly you can see token usage and spend by model, feature, or user
• Integration effort: How much instrumentation work is required, and whether the tool supports OpenTelemetry and frameworks like LangChain, LangGraph, or LlamaIndex
• Deployment model: SaaS, self-hosted, or both

While the list below covers eight of the most established platforms, it isn't exhaustive. Lighter, self-hosted loggers like Helicone solve a narrower slice of the same problem (cost and request logging) if spend visibility is all you need.

New Relic

New Relic's AI monitoring extends its existing observability platform into the LLM and agent layer so teams already running New Relic for infrastructure, application, and log monitoring get AI observability without standing up a second tool.

Key features:

• Distributed tracing from prompt to response, with token usage and cost tracked automatically
• An Agents Service Map that visualizes multi-agent workflows and dependencies in real time
• An Agent Performance Dashboard covering request volume, latency, throughput, and error rates per agent
• Quality checks for bias, hallucination, and toxicity with user feedback tied back to the trace that produced it
• Connections to vector databases like Pinecone for retrieval visibility
• Native OpenTelemetry support, with auto-instrumentation for LangChain, MCP servers, and agent frameworks including LangGraph, AutoGen, and Bedrock
• Natural-language querying of agent behavior through New Relic AI, the platform's built-in assistant

For a closer look at instrumenting agents with OpenTelemetry, see New Relic's guide to tracing AI agents with OpenTelemetry and Strands. For how this plays out across a full multi-agent system, read Beyond the black box: next-gen agentic AI monitoring. And for a walkthrough of taking an agent from demo to production, New Relic documents the process in a case study on a production-ready AI travel agent.

The trade-off: Evaluation tooling isn't as deep as a platform built solely for prompt experimentation, and the biggest win goes to teams already invested in the New Relic ecosystem.

Best for: Teams that already run New Relic or want AI observability unified with infrastructure and application monitoring without adding a second monitoring stack.

Langfuse

Langfuse is an open source LLM engineering platform built specifically for tracing, evaluation, and prompt management.

Key features:

• Full structured traces covering prompt, response, token usage, latency, tool calls, and retrieval steps
• LLM-as-a-judge evaluation and scoring
• Centralized prompt management with versioning and a playground for testing changes before they ship
• Native integrations with OpenAI, LangChain, and LlamaIndex
• Self-hosted (it's fully open source) or managed cloud deployment

The trade-off: Self-hosting means your team owns the infrastructure, and it doesn't correlate with infra or APM data the way a unified platform does.

Best for: Engineering teams that want a purpose-built, open source tool they control end to end and that don't need it tied to a broader observability platform.

LangSmith

LangSmith is LangChain's own observability and evaluation platform, built to support LangChain and LangGraph specifically.

Key features:

• Native tracing for popular agent frameworks and OpenTelemetry
• Real-time tracking of cost, quality, and tool performance
• Online and offline LLM-as-a-judge and code-based evaluations
• Automated insights that cluster failure patterns without manual tagging
• SmithDB, a purpose-built backend with sub-second query performance across millions of traces
• Support for OpenAI, Anthropic, the Vercel AI SDK, and LlamaIndex
• Managed cloud, bring-your-own-cloud, or fully self-hosted deployment within a VPC

The trade-off: The tooling is most tailored for LangChain and LangGraph-based stacks, and it's a separate system to check alongside infrastructure monitoring.

Best for: Teams building agents on LangChain or LangGraph that want framework-native tracing and evaluation.

Arize

Arize comes from the team behind OpenInference, the open standard that many LLM observability tools now trace against. It splits into two products: Arize Phoenix, a free, open source tool for self-hosted tracing and evaluation, and Arize AX, a managed enterprise platform.

Key features:

• Span, trace, and session-level evaluation at scale
• Alyx, an AI agent (Arize AX only) that runs evals, performs root cause analysis, and proposes fixes
• A proprietary datastore and online evaluations (Arize AX)
• More than 40 integrations, including LangGraph, LangChain, LlamaIndex, and CrewAI

The trade-off: It's a broad platform with a real learning curve, and the jump from Phoenix's free tier to AX's enterprise pricing is steep.

Best for: Teams that want systematic, large-scale evaluation and are comfortable starting on the open source Arize Phoenix before graduating to a managed platform.

Braintrust

Braintrust is an eval-first platform that ties cost tracking directly to quality scoring in one workflow.

Key features:

• Token cost attributed to specific traces by user, feature, or model
• Evaluations that run on the same production traces that surfaced the cost
• A GitHub Action that blocks merges when quality drops below a set threshold
• A Playground for experiment tracking, where you can test prompt and model variants side by side against real production traffic 
• Loop, an AI assistant that proposes prompt revisions based on failure patterns it identifies 

The trade-off: Braintrust runs primarily as SaaS, with self-hosting reserved for enterprise plans, and it leans toward evaluation workflows more than infrastructure correlation.

Best for: Teams that want to move from seeing what LLM calls cost to actually reducing that cost inside a single workflow tied to CI/CD.

Datadog

Datadog's Agent Observability extends its existing APM suite into LLM and agent tracing, the same core approach as New Relic.

Key features:

• End-to-end tracing from prompt through retrieval steps and tool calls
• Correlation of LLM spans with APM services and infrastructure signals
• Built-in and custom evaluators that catch hallucinations, prompt injection, and data exposure
• Billing scoped to LLM spans only, so cost scales with actual model usage rather than trace volume

The trade-off: It's a strong option for teams already standardized on Datadog but less compelling as a starting point without existing Datadog spend.

Best for: Teams already running Datadog for APM and infrastructure that want AI observability in the same place.

Comet (Opik)

Opik is Comet's open source LLM observability and evaluation platform, a lighter-weight, free-first alternative to fully commercial tools.

Key features:

• Full tracing and visualization of every agent action
• More than 30 LLM-as-a-judge metrics (relevance, precision, hallucination detection, task completion)
• Reference datasets to catch errors across thousands of traces at once
• Prompt optimization algorithms
• Cost tracking for model spend
• Guardrails for compliance and PII protection
• A free, self-hostable core feature set with enterprise scalability and compliance features behind a paid tier

The trade-off: Since the open source model puts more of the setup on your team, it's less turnkey than platforms built around one opinionated workflow.

Best for: Individual developers and teams that want a free, open source starting point with room to scale into paid tiers later.

Confident AI

Confident AI is the commercial platform from the team behind DeepEval, the open source evaluation framework many teams use for local and CI-based testing.

Key features:

• Tracing that captures inputs, outputs, tool calls, latency, and token cost
• Dataset auto-curation from production traffic
• Multi-turn chat simulation
• Git-based prompt versioning with eval gates that block a merge if quality metrics fail
• 20-plus integrations, including OpenAI, LangGraph, LangChain, and OpenTelemetry
• Managed cloud or fully self-hosted deployment

The trade-off: It's most differentiated on evaluation rigor as opposed to infrastructure correlation or a single unified dashboard.

Best for: Regulated industries and teams that need standardized, auditable evaluation across multiple teams, not just tracing.

What to look for in an LLM observability tool

Feature checklists only tell you so much. Here's how each criterion plays out in an actual production incident.

Tracing and span depth

When an agent fails, you need the full sequence: which tool call fired, what a retrieval step returned, how long each step took, and what the model actually received as input. Shallow tracing, which captures only the prompt and final response, hides the retrieval steps and tool calls that usually cause failures in multi-step agent workflows and RAG (retrieval-augmented generation) pipelines. Depth here is the difference between knowing something broke and knowing why, and it's how you diagnose performance bottlenecks instead of guessing at them.

Evaluation and quality scoring

Automated evaluators (including LLM-as-a-judge, semantic similarity scoring, and rule-based checks) catch obvious regressions fast, but they aren't a full replacement for people. LangChain's 2026 State of Agent Engineering report found that 59.8% of teams still rely on human review even as LLM-as-a-judge adoption climbs to 53.3%. Teams combining offline evaluation (testing against a fixed dataset before deployment) with online evaluation (scoring live production traffic) catch more failure modes than either alone. Look for a tool whose evaluation metrics cover coherence, factual accuracy, and task completion, not just whether a call technically succeeded.

Integration with your existing stack

Instrumentation effort is where the trade-offs become clear. Manually wrapping every agent step in custom code requires far more effort than relying on OpenTelemetry-native or auto-instrumented support for the frameworks you're already using. And if your team already runs an observability platform for infrastructure and application performance, consider whether LLM tracing lives in that platform or requires a second login during an incident.

One tool or two: extending your stack vs. adding a new one

Feature lists only tell part of the story. The real decision comes down to architecture.

AI-native tools like Langfuse, LangSmith, Arize, and Braintrust specialize in deep evaluation and experimentation. This shows in their prompt versioning, dataset curation, and eval-gated CI pipelines. But adopting a standalone tool means maintaining a second system: another dashboard, another set of credentials, another place to look when an incident spans your application layer and your AI layer.

New Relic takes a different approach. LLM and agent tracing live inside the same platform as APM, infrastructure, and log monitoring, requiring no new instrumentation for existing New Relic users. When an agent call slows down, engineers trace it from the initial request through the infrastructure layer to the model call and back in a single view, eliminating the need to correlate timestamps across two systems. This unifies quality checks and prompt-injection guardrails directly with infrastructure monitoring rather than isolating them in a parallel tool.

This architectural choice represents a practical trade-off. Teams doing heavy prompt evaluation, like running large batches of prompt versions against a test suite before every release, may still want a specialized AI-native tool alongside their observability platform. The core decision depends on whether your current challenge is establishing baseline visibility into the AI layer or adding advanced evaluation workflows.

Choosing the right fit for your team

The right tool depends on which gap you're actually closing, not which vendor has the longest feature list.

If you can't see what your AI agents are doing today (no tracing, no visibility into token usage, and no way to tell which tool call caused a failure), the fastest path forward is extending your existing observability platform into the AI layer. New Relic's AI Observability lives alongside your APM and infrastructure data, so agent tracing, error rates, and cost tracking show up with everything else your team monitors.

If your primary gap is evaluation depth, such as running large-scale experiments across prompt versions or gating deployments on quality scores, an AI-native tool like Langfuse, LangSmith, Arize, or Braintrust justifies maintaining a second system. Many teams adopt a hybrid approach: an integrated platform for day-to-day incident response paired with a specialized tool for evaluation-heavy prompt and logic tuning over time.

Gartner predicts that more than 40% of agentic AI projects will be canceled by the end of 2027, often over unclear ROI and inadequate risk controls. Observability isn't the whole fix, but it's hard to build a case for scaling an agent your team can't see clearly.

Discover how New Relic unifies large language models and agent tracing with your APM and infrastructure monitoring. Request a demo to see AI Observability running against your own stack.