Top 15 LLM Observability Tools To Catch Agent Failures Before Users Do

The stakes for LLM reliability have never been higher. The global large language model market, valued at $5.6 billion in 2024, is projected to explode to nearly $35 billion by 2030—a staggering 37% annual growth rate.

Simultaneously, the AI observability market is surging from $1.4 billion in 2023 to $10.7 billion by 2033, reflecting the urgent need for sophisticated monitoring as enterprises deploy trillions of dollars worth of AI systems.

When core LLM technology scales this rapidly, the observability layer must evolve in parallel to prevent cascading failures that erode user trust and damage bottom lines. Unlike basic logging, LLM observability gives you complete visibility into your AI systems.

We'll look at 15 leading LLM observability tools through depth of tracing and evaluation features, integration ease with popular frameworks, pricing transparency, and enterprise capabilities like compliance controls and on-premises deployment.

Use this guide to find the platform that matches your stack and risk tolerance before the next bug or budget overrun catches you unprepared.

We recently explored this topic on our Chain of Thought podcast, where industry experts shared practical insights and real-world implementation strategies

LLM observability tool #1: Galileo AI

Many agents fail mysteriously in production, leaving teams scrolling through endless logs without finding root causes. Traditional monitoring tools miss the nuanced decision paths that agents take, making debugging a time-consuming nightmare.

Galileo AI solves this by tracing each step of a chain—from the initial prompt through every tool call—then surfacing anomalies in real time, letting you debug within minutes instead of hours.

Cost control comes standard with Galileo's proprietary Luna-2 small language models, which evaluate safety, accuracy, and bias at 97% less cost than GPT-4. This drops analysis expenses to just $0.02 per million tokens while maintaining competitive F1 scores of 0.88. You can afford 100% sampling and still stay on budget.

The Agent Graph visualizes every branch in multi-agent conversations, while the Insights Engine flags hallucinations, prompt injections, and latency spikes as they happen. Runtime protection guardrails block problematic outputs before they reach users, preserving trust in regulated environments.

Galileo targets high-volume, high-stakes deployments—healthcare chatbots, autonomous support agents—where SOC 2 certification and optional on-premises installation matter. Smaller projects may find the enterprise focus excessive, but when reliability is non-negotiable, Galileo delivers the depth and speed you need.

LLM observability tool #2: Lunary

Your support bot starts giving bizarre answers to simple questions, and you need to trace what went wrong without deploying a massive observability platform. Lunary fills this exact gap with its Apache-2 licensed project that offers 1,000 tracked events daily on its free tier—perfect for debugging conversations before budget approvals drag on for months.

Lunary captures every prompt, response, and latency metric across any model you're using. Native integrations with LangChain and OpenAI SDK mean you can instrument existing chatbots with just a few lines of code instead of rebuilding from scratch.

The standout "Radar" module automatically categorizes outputs as helpful, off-topic, or toxic, revealing patterns that manual log reviews would miss entirely.

This conversational focus creates Lunary's biggest limitation. Complex retrieval pipelines or multi-agent workflows will eventually hit feature gaps that comprehensive platforms handle better.

But for customer service bots, documentation assistants, or any chat-first application, Lunary provides an accessible open-source foundation with commercial support available when you need to scale beyond the basics.

LLM observability tool #3: Helicone AI

You might be racing to get a demo in front of stakeholders, yet you still need basic telemetry before someone asks, "How much did that prompt just cost?" Helicone solves this with a proxy-first approach that requires just a single URL swap—no SDK changes, no code rewrites.

The platform forwards every request while collecting the data you wish you'd instrumented from day one. Your dashboard immediately shows request breakdowns by model, user path, and response time.

Cost analytics appear alongside performance heat maps, letting you correlate spend with latency instead of juggling spreadsheets. Built-in caching can reduce costs, while prompt tracking gives you an audit trail when outputs drift.

The trade-off for zero-code integration is depth—Helicone tracks metrics but doesn't evaluate quality or safety. You'll also see 50-80ms additional latency from the proxy layer.

This fits perfectly for cost-sensitive startups, rapid prototyping, or simple applications where visibility matters most. At higher volumes, test throughput limits and confirm your privacy requirements before routing sensitive data through the service.

LLM observability tool #4: Traceloop (OpenLLMetry)

Imagine your infrastructure team spent months perfecting OpenTelemetry pipelines, but model calls vanish into observability blind spots. Most teams bolt on separate monitoring stacks, creating data silos and doubling maintenance overhead.

Traceloop's OpenLLMetry specification solves this by extending your existing OpenTelemetry infrastructure to capture every interaction, giving you unified visibility without abandoning your current monitoring investments.

The project ships open-source and free, automatically enriching traces from LangChain, LlamaIndex, Haystack, and OpenAI clients. OpenLLMetry adds specific span attributes—error, retry, and truncation tags—while maintaining full OTLP compliance.

You can follow prompts through complete chain executions and measure latency, token usage, and error rates alongside traditional service metrics. Since data lands in your existing backend, alerting rules, dashboards, and retention policies continue working unchanged.

This vendor-neutral approach requires trade-offs. You'll craft UIs and alerts yourself, and solid OpenTelemetry knowledge becomes mandatory. If your team embraces observability-as-code, though, Traceloop provides the fastest path to standardized tracing without introducing another dashboard to maintain.

LLM observability tool #5: TruLens

You probably rely on numbers—latency, token usage, cost—to decide whether your pipeline is healthy. TruLens shifts the focus to questions you can't answer with metrics alone: Is the output factual? Does it sound biased? This research-backed framework lets you score every response against qualitative criteria without leaving your notebook.

Rather than bolting evaluations onto production logs after failures surface, you wrap your LangChain or native OpenAI calls with a single decorator. TruLens then records full traces, renders them in an open-source dashboard, and applies evaluator modules—factual accuracy, toxicity, relevance, bias—to each step.

Those evaluators come from Carnegie Mellon research, so you inherit academically vetted methods without writing custom scoring code. The result: quick visibility into subtle failure modes that numerical dashboards miss.

TruLens excels when you need deep qualitative insight, not around-the-clock uptime monitoring. It's free, Apache-licensed, and easy to self-host, but you'll still pair it with a runtime observability layer for latency and cost.

If your governance workflow demands human review, the built-in annotation UI and exportable JSON traces slot neatly into compliance audits, turning subjective judgments into reproducible artifacts.

LLM observability tool #6: Portkey

Managing multiple providers means building custom routing logic, retry mechanisms, and cost tracking from scratch. Portkey eliminates this overhead by acting as a unified proxy layer—swap your endpoint once and immediately gain comprehensive telemetry across every request.

Each prompt, completion, and error passes through a single pipeline, delivering unified metrics on latency, token consumption, and costs.

Once your traffic flows through Portkey, you can define sophisticated routing rules: direct critical queries to GPTs when accuracy matters most, or automatically failover to cheaper alternatives when rate limits hit.

Built-in exponential backoff and fallback logic prevent silent failures, while granular cost dashboards catch runaway spending before it damages your budget.

Portkey's smaller community means fewer integration examples and lighter documentation compared to established tools. This tradeoff delivers fine-grained request control and vendor independence—valuable for enterprises managing multi-provider strategies.

If their SLA terms and data privacy controls meet your requirements, Portkey provides lightweight orchestration and observation without touching your existing application code.

LLM observability tool #7: Datadog

Your dashboards already live in Datadog, so why spin up another console just to monitor language models? Open-source emitters like OpenLLMetry forward traces over OpenTelemetry, funneling prompt inputs, completions, latency, and token usage into the same panels tracking CPU, memory, and network traffic.

That single pane of glass becomes the platform's biggest advantage: one alerting system, one RBAC model, one data lake.

The trade-off hits in depth. Datadog crushes time-series analytics, but ships without purpose-built hallucination detectors, guardrails, or agent-step visualizations. You'll configure custom monitors, build widgets, and absorb ingestion fees for every trace.

Teams craving turnkey evaluators find that DIY tax painful; teams valuing consolidation consider it worthwhile.

This path makes sense when Datadog already handles your APM, your SREs monitor everything through it, and you want telemetry following the same incident-response playbook. OpenTelemetry compatibility means enterprise security controls, SSO, and existing vendor contracts come ready-made. You extend observability without renegotiating your stack.

LLM observability tool #8: PostHog

Product teams face a frustrating reality: your insights live in one dashboard while user behavior data sits in another. PostHog bridges this gap by extending its familiar event-driven architecture to capture calls alongside every click and feature flag in your product.

Observability best practices stress this unified approach—capturing both application events and traces reveals silent failures and cost leaks you'd miss with separate tools.

Rather than jumping between dashboards, you pipe prompts, completions, token counts, and latency directly into the funnels you already track. When churn spikes correlate with hallucination surges, or conversion drops align with slower completions, you spot patterns that pure observability tools can't surface.

PostHog's open-source foundation lets you host everything in your EU or US cloud, meeting enterprise data residency requirements.

The trade-off: PostHog won't replace specialized guardrail engines or automated failure detection. You'll still need dedicated tools for hallucination scoring or bias checks.

But if your priority is correlating user behavior with model performance—shipping experiments quickly, toggling feature flags, proving ROI—folding events into PostHog's workflow keeps you moving fast without scattering data.

LLM observability tool #9: Opik (Comet)

You already log every hyperparameter and metric in Comet, yet your pipeline still feels like a black box. Opik bridges that gap by capturing request-level traces, letting you inspect prompt versions, and displaying live cost and latency curves—all without abandoning the experiment-tracking habits you've built.

Unlike standalone observability tools, Opik keeps your model experiments and telemetry under one roof, so version diffs and experiment comparisons work exactly as you expect. If you already rely on Comet for governance, you inherit its lineage tracking automatically. That makes audits far less painful than stitching multiple tools together later.

The trade-off is straightforward. Open-source extensions like Opik focus on core telemetry, which means you'll build custom evaluators and safety checks yourself. Enterprise platforms bundle these features out of the box, but they also require wholesale tooling migrations.

For Comet-centric teams, Opik offers the fastest path to seeing inside your prompts and completions without disrupting workflows that already work.

LLM observability tool #10: Weights & Biases

If you already track vision or tabular experiments in Weights & Biases, extending that same workspace eliminates the need for separate observability stacks. Every prompt, completion, and evaluation metric lands beside your existing runs, creating a unified timeline to audit changes and compare performance across model families.

Versioning becomes your safety net here. W&B stores each prompt template, dataset slice, and model checkpoint as artifacts, so you can roll back when a new chain degrades quality without warning.

Teams managing both classical ML and language model pipelines find this centralized approach essential for maintaining quality standards. Rich visualizations help you spot cost spikes, latency regressions, or drift without building custom dashboards.

This comprehensive approach has trade-offs. W&B feels heavyweight if you only need basic tracing, and usage-based billing climbs with token volume. However, if you're running both traditional ML and language model workflows, the unified model registry, collaborative reports, and SOC 2-ready on-premises deployment options streamline governance.

You also get to keep your entire model lifecycle—training, evaluation, deployment, and monitoring—in one place.

LLM observability tool #11: MLflow

You might already rely on MLflow to version traditional models and compare experiments. That same open-source backbone now stretches into large-language work, letting you log prompts, completions, and evaluation results without surrendering ownership of your data.

Because the platform is vendor-agnostic, you can plug in any model or hosting provider and keep a single registry for every artifact—an approach that open-source guides applaud for its flexibility and cost control.

The real attraction is MLflow's community gravity. Thousands of engineers contribute integrations, and new plug-ins keep appearing for LangChain, LlamaIndex, and other orchestration layers.

That ecosystem gives you guardrail components, prompt templates, and evaluation scripts ready to drop into existing pipelines. When you need enterprise guarantees, Databricks layers managed SLAs and role-based access on top, so you can scale tracking from a single notebook to a regulated production cluster without rewriting code.

The trade-off surfaces once you want rich, specialized dashboards. Teams often stitch together custom visualizations or query raw logs to surface metrics like hallucination rate or context-window drift.

If you're comfortable with wiring Grafana or building Streamlit apps, that freedom is empowering. Otherwise, be prepared for an extra engineering lift compared with turnkey observability suites.

LLM observability tool #12: Coralogix AI

You've spent months building your observability pipeline around Coralogix for microservices—now your applications are scattered across different dashboards. Coralogix's AI Observability module solves this fragmentation by folding telemetry into your existing pipeline. 

No additional vendor relationships, no separate login credentials, just your prompts and completions flowing through the same data architecture you already trust.

Span-level tracing captures every prompt as it hops between functions, correlating with infrastructure metrics in real-time. When p95 latency spikes or token spend exceeds budget, your existing alerting rules trigger instantly.

Because everything lands in the same data lake, you query events using the same syntax that surfaces application logs—zero learning curve required.

The trade-offs matter here: enterprise licensing means higher costs than specialized startups, and Coralogix arrived later to the space than purpose-built platforms. But if compliance drives your architecture decisions, the platform's region-specific storage, RBAC controls, and audit trails make it practical for regulated teams who refuse to fragment their observability stack.

LLM observability tool #13: Langfuse

You might prefer owning your observability stack instead of routing sensitive prompts through a third-party SaaS. That's exactly where Langfuse shines with its MIT-licensed core that lets you self-host the entire platform.

This gives you full control of your data while tapping into an open-source community that has already earned more than 15,700+ GitHub stars—a signal of steady maintenance and plugin growth.

Once installed, Langfuse automatically captures every request, response, and intermediate step in your pipeline. You can scrub through prompt history, chart latency distributions, and track token spend without writing custom dashboards.

Event-level tracing integrates cleanly with LangChain, so you instrument a chain and immediately see granular traces pop up in the web UI. Real-time dashboards surface anomalies before users notice them, while raw JSON exports keep you free to analyze data in your own warehouse.

The catch? Advanced features such as SLAs, SSO, and role-based access live behind commercial add-ons. If you're running sprawling multi-agent architectures, you may outgrow Langfuse's scope.

For early-stage startups and engineering teams seeking code ownership without hefty licensing fees, it's a practical first step into observability.

LLM observability tool #14: LangSmith

Generic monitoring platforms fumble on LangChain's nested callbacks and complex chain structures. LangSmith steps in as the ecosystem's native observability layer, capturing every prompt, tool call, and model response with almost no code change.

One environment variable turns it on, after which you see latency, token spend, and cost breakdowns for each chain component.

For rapid iteration, this tight coupling pays dividends. You can replay failed traces, attach manual or automated ratings, and diff prompt versions to catch regressions before they reach production.

The free cloud tier handles 5,000 traces monthly, while the enterprise add-on enables self-hosting for teams with strict data control requirements.

The trade-off is framework lock-in. Move to another framework and LangSmith's insights stay behind, plus it provides fewer real-time guardrails than agent-centric platforms. Use it when you're prototyping or running a pure LangChain stack—chatbots, RAG pipelines, lightweight agents—where quick debugging trumps deep policy enforcement.

For larger teams, self-hosting and SOC 2 compliance preserve vendor neutrality while keeping you inside the LangChain universe.

LLM observability tool #15: Phoenix (by Arize)

If your pipeline already feeds petabytes of metrics into Grafana or Datadog, Phoenix feels instantly familiar. The project ships as a lightweight OSS library you can drop into any Python service, emitting OpenTelemetry spans for every prompt, retrieval call, and model response.

You decide where the data lives—Jaeger for quick traces or an existing observability lake for long-term analysis—avoiding infrastructure duplication or vendor lock-in. The managed variant, AX, adds SSO, RBAC, and turnkey storage when you'd rather skip the plumbing.

Phoenix treats each retrieval-augmented generation (RAG) hop as its own span, so you can pinpoint whether latency spikes originate in the vector store, the model, or orchestration code. Built-in tags capture errors, retries, truncations, token counts, and cost metadata, giving you raw material for custom alerts without hidden sampling.

This flexibility is Phoenix's chief advantage—and its main challenge. You'll need engineers comfortable with OpenTelemetry pipelines to extract full value, or budget for AX's hosted backend.

For teams running both classical ML and language model workloads, Phoenix offers a single trace format, letting you correlate model drift, infrastructure health, and behavior in one unified dashboard.

Scale your LLM operations with Galileo's comprehensive observability

The right observability platform transforms debugging from guesswork into guided problem-solving. Whether you prioritize cost optimization, quality evaluation, or seamless integration with existing infrastructure, these tools offer distinct approaches to the same critical challenge: making your language model applications reliable at scale.

Here's how Galileo naturally supports your comprehensive observability needs:

• Agent-specific failure detection: Galileo's Insights Engine automatically surfaces the exact failure patterns—tool errors, planning breakdowns, infinite loops—that generic observability tools miss, reducing debugging time

• Cost-effective evaluation at scale: With Luna-2 SLMs delivering evaluation at 97% lower cost than GPT-4 alternatives, you can afford comprehensive monitoring across all your LLM interactions without breaking your budget

• Runtime protection and guardrails: Unlike passive monitoring tools, Galileo's Agent Protect intercepts problematic outputs before they reach users, providing the only real-time intervention capability in the market

• Enterprise-grade compliance: SOC 2 certification and flexible deployment options (on-premise, hybrid, cloud) ensure your observability strategy meets the strictest regulatory requirements while maintaining development velocity

• Multi-framework integration: Built to work seamlessly with LangChain, CrewAI, AutoGen, and any other framework in your stack, eliminating vendor lock-in while providing unified visibility across your entire AI infrastructure

Discover how Galileo can transform your AI debugging experience and prevent costly production failures before they impact your users.