TL;DR — 10 LLM Observability Tools to Evaluate & Monitor AI in 2026
Confident AI is the best LLM observability tool for evaluation and monitoring in 2026 because it's the only platform where evaluation is the observability — every trace is scored with 50+ research-backed metrics, quality drops trigger alerts through PagerDuty, Slack, and Teams, production traces auto-curate into evaluation datasets, and the entire workflow is accessible to PMs, QA, and domain experts alongside engineers.
Other alternatives include:
- LangSmith — Deep integration with the LangChain ecosystem and annotation queues for human review, but evaluation depth outside LangChain is limited and workflows are engineer-driven.
- Langfuse — Open-source and self-hostable with strong OpenTelemetry support, but no built-in evaluation metrics and no quality-aware alerting.
- Arize AI — Enterprise-scale ML monitoring with Phoenix open-source, but the LLM evaluation layer is shallow and the platform is engineer-only.
Pick Confident AI if you need observability that evaluates AI quality — not just another dashboard that logs what happened.
Error logs tell you what broke. Latency charts tell you what's slow. Neither tells you whether your AI's output was faithful, relevant, or safe.
That's the gap LLM observability tools are supposed to fill — but most don't. The category has split into three camps. Traditional APM platforms (Datadog, New Relic) are adding LLM tabs that track tokens and latency alongside infrastructure metrics. AI-native tracing tools (Langfuse, LangSmith) go deeper on trace capture but stop at logging what happened. AI gateways (Helicone, Portkey) sit between your app and LLM providers to add routing, caching, and cost tracking with minimal code changes.
All three camps are useful. None of them, on their own, answer the question that actually matters: is your AI producing good outputs?
The tools that matter in 2026 close the gap between observing AI behavior and evaluating AI quality. They don't just show you traces — they score outputs, alert on quality degradation, detect drift across prompts and use cases, and feed production insights back into the development cycle.
This guide compares ten LLM observability platforms across their tracing, evaluation, monitoring, and collaboration capabilities. We prioritized tools that help teams act on what they observe — not just observe more.
The Best LLM Observability Tools at a Glance
Tool | Type | Pricing | Open Source | Best For |
|---|---|---|---|---|
Confident AI | Evaluation-first observability | Free tier; from $19.99/seat/mo | No (enterprise self-hosting available) | Eval-driven monitoring, cross-functional quality workflows, production-to-eval pipelines |
LangSmith | Observability & evaluation | Free tier; from $39/seat/mo | No | LangChain-native tracing, annotation queues, agent debugging |
Langfuse | LLM engineering platform | Free tier; from $29/mo | Yes (MIT) | Self-hosted tracing, prompt management, OpenTelemetry-native instrumentation |
Arize AI | AI observability & evaluation | Free tier; from $50/mo | Yes (Phoenix, ELv2) | Enterprise ML/LLM monitoring, high-volume production environments |
Datadog LLM Observability | APM extension | From $8/10K requests/mo | No | Unified LLM + infrastructure monitoring for existing Datadog users |
Helicone | LLM observability & AI gateway | Free tier; from $79/mo | Yes (Apache-2.0) | Proxy-based observability, cost tracking, multi-provider caching |
Portkey | AI gateway & LLM routing | Free tier; from $49/mo | Yes (MIT) | Production routing, fallbacks, load balancing with built-in logging |
Lunary | Observability & prompt management | Free tier; Team and Enterprise pricing | Yes (Apache-2.0) | Lightweight RAG and chatbot observability, JavaScript-first |
Weights & Biases | AI observability via Weave | Free tier; from $50/seat/mo | Yes (Weave, partial) | ML experiment tracking teams expanding into LLM observability |
New Relic AI Monitoring | APM extension | Consumption-based; free tier | No | Basic AI telemetry for existing New Relic users |
What to Look for in an LLM Observability Tool
Catching errors is table stakes. The harder problem is knowing when outputs are technically valid but wrong for your domain — a hallucinated policy, a drifting tone, a retrieval miss that produces a confident but incorrect answer. The best observability tools surface these ambiguous cases for review and action.
Evaluation Depth
Does the tool score outputs for faithfulness, relevance, hallucination, and safety? Or does it just log traces and count tokens? Tracing without evaluation is expensive logging. The tools that close the loop evaluate what happened, not just record it.
Tracing Granularity
You need visibility into every step of complex workflows: tool calls, retrieved documents, intermediate reasoning, branching paths. Black-box monitoring that only captures inputs and outputs doesn't work for multi-step agents or RAG pipelines.
Quality-Aware Alerting
Your existing APM catches latency spikes and 500 errors. LLM observability should alert on quality degradation — faithfulness drops, safety regressions, drift across prompts — not just infrastructure failures.
Collaboration Beyond Engineering
AI quality isn't an engineering-only concern. Product managers need to validate behavior. QA needs to test regressions. Domain experts need to flag edge cases. If every quality decision requires an engineer to write a script, engineering becomes the bottleneck.
Production-to-Development Loop
The tools that matter feed production insights back into development. Traces become evaluation datasets. Quality issues trigger the next test cycle. Without this loop, monitoring and development are disconnected silos.
How We Evaluated These Tools
We analyzed official documentation, GitHub repositories, public pricing pages, and community feedback from Reddit, Hacker News, and GitHub discussions for each platform. Real user feedback surfaces nuances that official docs don't.
For this analysis, we focused on six dimensions:
- Evaluation maturity: Are metrics research-backed? Is evaluation core to the product or bolted onto tracing?
- Observability depth: Can you drill into agent steps, query large trace volumes, and evaluate directly on production traffic?
- Alerting and drift detection: Can you set alerts that fire on quality drops — not just latency? Can you track quality changes across prompt versions and use cases?
- Cross-functional accessibility: Can PMs, QA, and domain experts participate in quality workflows — or is everything gated behind engineering?
- Framework flexibility: Does the tool work consistently across frameworks, or does depth depend on ecosystem lock-in?
- Pricing transparency: Is the pricing model clear and predictable at scale?
1. Confident AI
Type: Evaluation-first observability platform · Pricing: Free tier; Starter $19.99/seat/mo, Premium $49.99/seat/mo; custom Team and Enterprise · Open Source: No (enterprise self-hosting available) · Website: https://www.confident-ai.com
Confident AI is built around a simple premise: tracing without evaluation is just expensive logging. The platform scores every trace, span, and conversation thread with 50+ research-backed metrics automatically — turning observability from passive logging into active quality monitoring.
Where most observability tools stop at showing you what happened, Confident AI tells you whether it was good and alerts you when it stops being good. Quality-aware alerting triggers through PagerDuty, Slack, and Teams when evaluation scores drop below thresholds. Production traces are automatically curated into evaluation datasets, closing the loop between what you observe in production and what you test against before the next deployment.
The collaboration model is the widest gap between Confident AI and everything else on this list. PMs, QA, and domain experts run full evaluation cycles via AI connections (HTTP-based, no code), review traces, annotate outputs, and trigger evaluations against production applications — all without engineering involvement at every step.
Customers include Panasonic, Toshiba, Amdocs, BCG, and CircleCI.
Best for: Cross-functional teams that need AI quality monitoring — evaluation, alerting, drift detection, and annotation — accessible to the entire team, not just engineers.
Standout Features
- Evaluation on every trace: 50+ research-backed metrics (open-source through DeepEval) score production traces for faithfulness, relevance, hallucination, bias, toxicity, and more — automatically.
- Quality-aware alerting: Alerts fire when evaluation scores drop, not just when latency spikes. Integrates with PagerDuty, Slack, and Teams.
- Prompt and use case drift detection: Track how specific prompts and use cases perform over time. Catch degradation at the prompt level, not just the aggregate.
- Automatic dataset curation: Production traces are converted into evaluation datasets, so test coverage evolves alongside real usage.
- Cross-functional annotation: PMs, domain experts, and QA annotate traces directly. Annotations feed back into evaluation alignment and dataset curation.
- Multi-turn simulation: Generate realistic multi-turn conversations from scratch — what takes 2-3 hours of manual prompting takes minutes.
- Red teaming: Test for PII leakage, prompt injection, bias, and jailbreaks. Based on OWASP Top 10 and NIST AI RMF.
- CI/CD regression testing: Integrates with pytest. Evaluation results flow back as testing reports with regression tracking.
Pros | Cons |
|---|---|
Every trace is evaluated, not just logged — evaluation IS the observability | Cloud-based and not open-source, though enterprise self-hosting is available |
Quality-aware alerting catches silent failures that APM tools miss entirely | The breadth of the platform may be more than what's needed for lightweight tracing |
Cross-functional workflows mean PMs and QA own AI quality independently | Teams new to evaluation-first tooling may need a ramp-up period to forecast GB-based costs |
Unlimited traces at $1/GB-month — the most cost-effective option on this list | |
Framework-agnostic with native SDKs (Python, TypeScript), OTEL, and OpenInference |
FAQ
Q: Does Confident AI require DeepEval?
No. Confident AI is a standalone platform that works independently. DeepEval is the open-source framework through which the 50+ metrics are available, but Confident AI provides them natively — no separate library needed.
Q: How does pricing work?
Unlimited traces on all plans. $1 per GB-month for data ingested or retained. Seat-based pricing starts at $19.99/seat/month. Free tier includes 2 seats, 1 project, and 1 GB-month.
Q: Can non-engineers use Confident AI?
Yes. PMs, QA, and domain experts run evaluation cycles through AI connections (HTTP-based, no code), annotate traces, and review quality dashboards without engineering involvement. This is the primary differentiator from every other tool on this list.
2. LangSmith
Type: Observability and evaluation platform · Pricing: Free tier; Plus $39/seat/mo; custom Enterprise · Open Source: No · Website: https://smith.langchain.com
LangSmith is a unified platform from the LangChain team that provides tracing, evaluation, and prompt management. It creates high-fidelity traces that render the complete execution tree of an agent — tool selections, retrieved documents, and exact parameters at every step.
The platform's annotation queues are a genuine strength. Subject matter experts can review, label, and correct specific traces through a structured workflow. This domain knowledge flows into evaluation datasets, creating a feedback loop between production behavior and engineering improvements. LangSmith also supports LLM-as-a-judge evaluators for automated scoring.
The tradeoff is ecosystem coupling. LangSmith works with any framework via its traceable wrapper, but the deepest integration is with LangChain and LangGraph. Teams outside that ecosystem will find observability depth drops. Evaluation metrics require custom implementation — there's no deep library of pre-built, research-backed metrics to draw from.
Best for: Teams building on LangChain that want native tracing with annotation workflows and agent debugging, and don't need deep built-in evaluation metrics.
Standout Features
- Full-stack tracing capturing the execution tree of agents, including tool calls, document retrieval, and model parameters
- Annotation queues for structured human review — domain experts can rate output quality and add context
- LLM-as-a-judge evaluators for automated scoring of historical runs
- Multi-turn evaluation support for measuring agent performance across conversation threads
- Prompt management and versioning integrated with evaluation workflows
Pros | Cons |
|---|---|
Deep visibility into LangChain and LangGraph workflows with step-level tracing | Observability depth drops outside the LangChain ecosystem |
Annotation queues create structured feedback loops between domain experts and engineering | Limited built-in evaluation metrics — LLM-as-a-judge requires custom implementation |
Managed infrastructure reduces operational overhead | Self-hosting restricted to Enterprise tier |
Works with any framework via | Seat-based pricing at $39/seat/mo limits access for cross-functional teams |
FAQ
Q: Does LangSmith only work with LangChain?
No. LangSmith works with any LLM framework via a traceable wrapper. However, the deepest integration and best experience is with LangChain and LangGraph applications.
Q: What evaluation approaches does LangSmith support?
LangSmith supports offline evals (testing known scenarios), online evals (scoring production data), and multi-turn evaluations. You can use LLM-as-a-judge evaluators or human annotation workflows. Built-in metric coverage is limited — most evaluators require custom implementation.
Q: How does LangSmith handle production traffic?
LangSmith processes millions of traces per day for enterprise customers. The platform offers 14-day retention for base traces and 400-day extended retention, with volume-based pricing.
3. Langfuse
Type: LLM engineering platform · Pricing: Free tier; from $29/mo; Enterprise from $2,499/year · Open Source: Yes (MIT, except enterprise features) · Website: https://langfuse.com
Langfuse combines tracing, prompt management, and evaluation hooks in a single open-source platform. The MIT-licensed core makes it popular with teams wanting full control over their data through self-hosting. Community adoption is strong, with over 21,000 GitHub stars.
Automated instrumentation via callback handlers captures traces without modifying business logic. The platform supports OpenAI SDK, LangChain, LlamaIndex, LiteLLM, Vercel AI SDK, Haystack, and Mastra. For teams that already have internal evaluation pipelines, Langfuse provides a solid tracing backbone.
The gap is evaluation. Langfuse logs traces but doesn't score them out of the box. Quality monitoring — faithfulness, relevance, hallucination — requires custom implementation or external tooling. There's no native alerting, so teams can't get notified when output quality degrades without building custom integrations.
Best for: Engineering teams that want open-source, self-hostable tracing with full data ownership and are comfortable building evaluation logic themselves.
Standout Features
- OpenTelemetry-native trace capture covering prompts, completions, metadata, and latency
- Multi-turn conversation grouping at the session level
- Prompt management and versioning within the platform
- Token usage dashboards with cost attribution across models
- Self-hosting via Docker for complete data ownership
- 21,000+ GitHub stars with active community development
Pros | Cons |
|---|---|
Fully open-source (MIT) with self-hosting — complete ownership over trace data | No built-in evaluation metrics — scoring requires custom implementation |
Strong OpenTelemetry foundation integrates into existing infrastructure | No native alerting on quality degradation |
All-in-one platform reduces tool fragmentation for tracing + prompt management | Native SDK support limited to Python and TypeScript |
Large community and active development | Self-hosted version has occasional bugs; continued investment uncertain after ClickHouse acquisition |
FAQ
Q: Is Langfuse fully open source?
The core is MIT-licensed. Enterprise features in ee folders have separate licensing. Self-hosting is available via Docker.
Q: Can Langfuse evaluate LLM outputs?
Langfuse supports custom evaluation scoring, but there are no built-in research-backed metrics. Teams typically integrate external evaluation libraries or build custom LLM-as-a-judge implementations.
Q: What frameworks does Langfuse support?
OpenAI SDK, LangChain, LlamaIndex, LiteLLM, Vercel AI SDK, Haystack, and Mastra. Other languages require API wrappers.
4. Arize AI
Type: AI observability and evaluation · Pricing: Free tier (Phoenix); AX from $50/mo; custom Enterprise · Open Source: Yes (Phoenix, Elastic License 2.0) · Website: https://arize.com
Arize AI extends its ML monitoring heritage into LLM observability, offering span-level tracing, real-time dashboards, and agent workflow visualization at enterprise scale. Its open-source Phoenix library provides a local-first, notebook-friendly entry point that runs in Jupyter, locally, or via Docker with zero external dependencies.
Phoenix uses OpenInference (OpenTelemetry-based) instrumentation to support multiple frameworks without vendor lock-in — LlamaIndex, LangChain, Haystack, DSPy, and smolagents. The notebook-first experience is a real strength for ML engineers who want observability during experimentation, not just production monitoring.
Custom evaluators allow scoring LLM outputs, but built-in metric coverage for LLM-specific use cases (faithfulness, hallucination, conversational coherence) is limited compared to evaluation-first platforms. The platform's UX is designed for technical users, which limits involvement from cross-functional team members.
Best for: Large engineering organizations that need enterprise-scale LLM monitoring, particularly those already using Arize for ML observability.
Standout Features
- Span-level tracing with custom metadata tagging for granular production debugging
- Real-time performance dashboards tracking latency, error rates, and token consumption
- Visual agent workflow maps for understanding multi-step LLM pipelines
- Phoenix open-source library for local-first, notebook-friendly observability
- OpenInference instrumentation supports LlamaIndex, LangChain, Haystack, DSPy, smolagents
Pros | Cons |
|---|---|
Enterprise-scale infrastructure handles high-throughput production environments | The LLM evaluation layer is shallow — built for ML monitoring first, extended to LLMs second |
Phoenix runs locally with zero external dependencies — great for privacy-focused teams | Engineer-only UX limits involvement from PMs, QA, and domain experts |
Vendor-agnostic instrumentation via OpenInference | Advanced capabilities gated behind commercial tiers with only 14 days of retention |
Combines ML and LLM monitoring, reducing vendor count | Cost tracking focuses on tokens rather than dollar amounts |
FAQ
Q: What is the difference between Phoenix and AX?
Phoenix is the open-source, self-hosted library. AX provides managed cloud hosting with tiered limits: Free (25K spans/month), Pro, and Enterprise.
Q: Can Phoenix run completely locally?
Yes. Phoenix runs in Jupyter notebooks, locally, or via Docker with zero external dependencies. This makes it suitable for privacy-sensitive environments.
Q: Does Arize support LLM evaluation?
Arize supports custom evaluators for scoring outputs. However, built-in research-backed metrics for LLM-specific use cases are limited compared to evaluation-first platforms.
5. Datadog LLM Observability
Type: APM extension for LLM monitoring · Pricing: From $8/10K LLM requests/mo (annual), $12 on-demand; 100K request minimum · Open Source: No · Website: https://www.datadoghq.com/product/llm-observability/
Datadog LLM Observability extends Datadog's existing monitoring platform to cover LLM applications. It correlates LLM spans with standard APM traces, showing how model latency affects overall application performance. For teams already invested in Datadog, this means zero new vendor procurement — LLM traces sit alongside infrastructure metrics, error rates, and traditional monitoring.
The platform supports agentless deployment via environment variables, making it accessible for serverless environments. Automatic instrumentation of LangChain applications is available via dd-trace-py. The familiar Datadog UX means teams already comfortable with the platform can onboard quickly.
The tradeoff: AI observability is a feature module on a general-purpose APM platform, not a purpose-built AI quality tool. There are no built-in evaluation metrics for faithfulness, relevance, or safety. Alerts fire on latency and error rates, not on output quality degradation.
Best for: Teams already using Datadog for infrastructure monitoring that want LLM visibility in their existing stack — and don't need evaluation or AI-specific quality workflows.
Standout Features
- Correlation between LLM spans and standard APM traces for end-to-end latency analysis
- Agentless deployment mode for serverless and restricted environments
- Unified dashboards showing LLM performance alongside infrastructure metrics
- Mature alerting infrastructure applied to LLM operational metrics
- Automatic instrumentation of LangChain applications via
dd-trace-py
Pros | Cons |
|---|---|
Unified view of LLM and infrastructure metrics — no new vendor for Datadog users | No built-in evaluation metrics for output quality — can't score faithfulness, relevance, or safety |
Familiar interface for teams already using Datadog | No quality-aware alerting — alerts on latency and errors only |
Agentless mode simplifies deployment in restricted environments | Pricing scales with trace volume and can be expensive at scale |
Enterprise-grade alerting and dashboard infrastructure | Designed for SREs and infrastructure teams, not AI quality teams |
FAQ
Q: Do I need the Datadog Agent for LLM Observability?
No. Datadog supports an agentless mode via environment variables, though running the full agent provides additional capabilities.
Q: Can Datadog evaluate LLM output quality?
No. Datadog LLM Observability tracks operational metrics (latency, tokens, errors) but doesn't include evaluation metrics for output quality like faithfulness or relevance. Teams needing quality evaluation will need to supplement Datadog with a dedicated tool.
Q: Is pricing publicly available?
Partially. Starts at $8 per 10K monitored LLM requests per month (billed annually), or $12 on-demand, with a minimum of 100K LLM requests per month. Enterprise pricing requires contacting sales.
6. Helicone
Type: LLM observability and AI gateway · Pricing: Free tier (10K requests/mo); Pro $79/mo; Team $799/mo; custom Enterprise · Open Source: Yes (Apache-2.0) · Website: https://www.helicone.ai
Helicone takes a proxy-based approach to observability. It sits between your application and LLM providers — swap your API's base URL, and you gain observability, caching, and cost tracking with minimal code changes. The platform adds negligible latency overhead, making it suitable for production workloads where every millisecond matters.
The AI gateway supports 300+ models across OpenAI, Azure OpenAI, Anthropic, AWS Bedrock, Gemini, and more. Intelligent caching reduces API costs, and automatic failover improves reliability across providers. The fully open-source core supports managed cloud, self-hosted Docker, and enterprise Helm chart deployments.
Helicone provides some built-in scoring capabilities for basic quality checks, but evaluation features are limited compared to dedicated evaluation platforms. Monitoring operates at the gateway/request level — you get visibility into individual model calls but not into how outputs flow through your broader application or agent chains.
Best for: Teams that want observability and cost tracking without heavy SDK integration, particularly those managing multiple LLM providers.
Standout Features
- One-line integration by swapping the API base URL — minimal code changes required
- Negligible latency overhead suitable for latency-sensitive production environments
- Intelligent caching and automatic failover across providers
- Support for 300+ models via unified gateway
- Cost attribution, latency tracking, and budget threshold alerts
- Fully open-source core with flexible deployment options (cloud, Docker, Helm)
Pros | Cons |
|---|---|
Minimal code changes required — proxy-based setup is the fastest on this list | Monitoring scoped to request level — no visibility into multi-step workflows or agent chains |
Cost-saving caching reduces API spend | Evaluation capabilities are basic compared to dedicated eval platforms |
Open-source with multiple deployment options | Missing advanced governance features like granular RBAC and audit trails |
Excellent multi-provider visibility and failover | Adding a gateway layer introduces an extra hop in your infrastructure |
FAQ
Q: How much latency does Helicone add?
Negligible overhead, which is acceptable for most production workloads.
Q: What LLM providers does Helicone support?
OpenAI, Azure OpenAI, Anthropic, AWS Bedrock, Gemini, Ollama, Vercel AI, Groq, and 300+ additional models.
Q: Can I self-host Helicone?
Yes. The open-source core supports Docker and Helm chart deployments.
7. Portkey
Type: AI gateway and LLM routing · Pricing: Free tier (10K logs/mo); Production $49/mo; custom Enterprise · Open Source: Yes (MIT) · Website: https://portkey.ai
Portkey is primarily an AI gateway. It handles routing, fallbacks, and load balancing for LLM applications with a lightweight architecture (~122 KB footprint) that adds sub-millisecond latency overhead. Teams often adopt Portkey to replace custom LLM management code — the unified SDKs for JavaScript and Python handle failovers, retries, and routing logic that would otherwise require significant engineering effort.
Observability comes as a built-in feature of the gateway rather than the primary focus. Teams get request-level logging, cost tracking, and basic performance monitoring as part of the gateway functionality. For teams that need reliable production routing first and observability second, Portkey fills a specific niche.
The evaluation and quality monitoring layer is thin. Teams needing to score outputs for faithfulness, detect quality drift, or run evaluation metrics on production traffic will need to pair Portkey with a dedicated observability or evaluation platform.
Best for: Teams building production applications that need reliable LLM routing, fallbacks, and load balancing — with observability as a built-in bonus.
Standout Features
- High-performance gateway with ~122 KB footprint and sub-millisecond latency overhead
- Automatic failovers, custom routing, retries, and load balancing
- Unified SDKs (JavaScript, Python) simplify multi-provider management
- Integration with LangChain, LlamaIndex, Autogen, and CrewAI
- Request-level logging with cost and performance tracking
Pros | Cons |
|---|---|
Minimal latency overhead makes it ideal for production routing | Observability is secondary to gateway functionality — limited depth |
Built-in reliability features replace thousands of lines of custom code | No evaluation metrics for output quality |
MIT-licensed with 10,000+ GitHub stars | No quality-aware alerting or drift detection |
One of the fastest gateway options available | Pricing unclear for high-volume enterprise use |
FAQ
Q: Is Portkey an observability tool or a gateway?
Primarily a gateway. Observability (logging, tracing) is a built-in feature but not the primary focus. Teams needing deep evaluation workflows should pair it with a dedicated platform.
Q: How much latency does Portkey add?
Sub-millisecond overhead with a ~122 KB footprint.
Q: Can Portkey replace custom LLM management code?
Yes. Users report removing thousands of lines of custom failover, retry, and routing code by switching to Portkey's unified SDKs.
8. Lunary
Type: Observability and prompt management · Pricing: Free tier (10K events/mo); Team and Enterprise pricing on request · Open Source: Yes (Apache-2.0) · Website: https://lunary.ai
Lunary is a lightweight observability platform focused on RAG pipelines and chatbots. Setup takes about two minutes. It offers SDKs for JavaScript (Node.js, Deno, Vercel Edge, Cloudflare Workers) and Python, with a JavaScript SDK designed for compatibility with LangChain JS.
The platform provides specialized tracing for retrieval-augmented generation, including embedding metrics and latency visualization. The generous free tier (10K events/month with 30-day retention) makes Lunary accessible for early-stage projects and small teams. Its open-source core (Apache-2.0) allows self-hosting, though some features require Enterprise licensing.
Lunary's strength is simplicity. For teams that need basic tracing and cost monitoring for RAG or chatbot applications without enterprise complexity, it's a low-friction starting point. The tradeoff is depth — advanced evaluation, multi-provider routing, and cross-functional workflows are limited compared to larger platforms.
Best for: Teams building RAG pipelines or chatbots who need quick, lightweight observability without enterprise overhead — particularly JavaScript-heavy teams.
Standout Features
- Two-minute integration via lightweight SDKs
- Specialized RAG tracing with embedding metrics and latency heatmaps
- JavaScript SDK designed for compatibility with LangChain JS and multiple runtimes (Node.js, Deno, Vercel Edge, Cloudflare Workers)
- Prompt management and versioning
- Generous free tier with 10K events/month and 30-day retention
Pros | Cons |
|---|---|
Fast setup and lightweight SDKs across multiple JavaScript runtimes | Advanced features limited in lower tiers |
Specialized RAG visualization features | Self-hosting requires Enterprise license for some features |
Cost-effective for small teams and early-stage projects | Limited support for tracing images and attachments |
Clean, focused UX for simple use cases | Less depth for complex agent workflows or multi-step evaluation |
FAQ
Q: What JavaScript runtimes does Lunary support?
Node.js, Deno, Vercel Edge, and Cloudflare Workers.
Q: Can I self-host Lunary?
The core is open source under Apache-2.0, but some compliance features and convenient deployment configurations require an Enterprise license.
Q: What's included in the free tier?
10K events/month, 3 projects, and 30 days of log retention.
9. Weights & Biases (Weave)
Type: AI observability via Weave · Pricing: Free tier; Teams $50/seat/mo; custom Enterprise · Open Source: Yes (Weave, partial) · Website: https://wandb.ai/site/weave
Weights & Biases built its reputation in ML experiment tracking and has expanded into LLM observability through Weave, its tracing and evaluation product. For teams already using W&B for model training and experiment management, Weave adds LLM-specific observability to the same platform — structured trace capture, evaluation scoring, and dashboard visualization.
The experiment tracking heritage is a genuine strength. Model versioning, artifact management, and reproducibility features carry over from the core W&B platform. Teams that already live in W&B for their ML workflow get continuity without adding another vendor.
The LLM observability layer is newer and less mature than the core product. Real-time quality alerting is limited. Multi-turn conversation support and agent-specific debugging features are still developing. The platform is built for ML engineers, not cross-functional teams.
Best for: ML teams already using Weights & Biases for experiment tracking that want to add LLM observability without leaving the W&B ecosystem.
Standout Features
- LLM trace capture through Weave with structured logging
- Experiment tracking heritage with model versioning and artifact management
- Evaluation scoring capabilities within the Weave framework
- Dashboard and visualization tools for tracking quality over time
- Integration with the broader W&B ecosystem for ML workflow continuity
Pros | Cons |
|---|---|
Unified experiment tracking and LLM observability for teams already in W&B | Weave is newer — less mature for production LLM observability |
Strong model versioning and artifact management from ML heritage | No real-time quality alerting |
Good fit for research-oriented teams that value reproducibility | No cross-functional workflows — built for ML engineers |
Structured trace capture with evaluation hooks | No multi-turn conversation support or agent-specific debugging |
FAQ
Q: What is Weave?
Weave is W&B's tracing and evaluation product for LLM applications. It provides structured logging, evaluation scoring, and dashboard visualization.
Q: Is Weave open source?
Partially. Weave has open-source components, but the full W&B platform is commercial.
Q: Is Weave production-ready?
Weave is functional for production use, but it's a newer product compared to W&B's core experiment tracking. Teams with demanding production observability needs may find it less mature than purpose-built alternatives.
10. New Relic AI Monitoring
Type: APM extension for AI monitoring · Pricing: Consumption-based; free tier available · Open Source: No · Website: https://newrelic.com/platform/ai-monitoring
New Relic adds AI-specific telemetry to its established APM platform. For organizations already paying for New Relic, AI monitoring slots into existing dashboards and alerting workflows. The AI features focus on model performance tracking and token economics — useful for operational visibility within your existing monitoring stack.
Like Datadog, the approach is extending APM to cover AI workloads. You get latency, throughput, token usage, and cost tracking alongside your existing infrastructure monitoring. The established enterprise alerting and dashboard capabilities carry over.
The limitation is the same as Datadog's: AI observability is a module on an APM platform, not a purpose-built quality tool. No evaluation metrics for output quality. No scoring for faithfulness, relevance, or safety. No AI-specific workflows like annotation, dataset curation, or multi-turn evaluation.
Best for: Organizations already invested in New Relic that want basic AI telemetry in their existing stack — without adopting a separate tool.
Standout Features
- LLM trace capture integrated into New Relic's APM
- Model performance metrics including latency, throughput, and token usage
- Cost tracking across LLM providers
- Alerting on operational metrics within existing New Relic infrastructure
- Broad infrastructure correlation between AI performance and backend systems
Pros | Cons |
|---|---|
No new vendor for existing New Relic customers | AI features are a module on APM — not purpose-built for AI quality |
Established enterprise alerting and dashboards | No evaluation metrics for output quality |
Broad infrastructure correlation between AI and backend systems | No AI-specific workflows — no annotation, simulation, or dataset curation |
Free tier available for initial exploration | Consumption-based pricing can be unpredictable at scale |
FAQ
Q: Does New Relic evaluate LLM output quality?
No. New Relic AI Monitoring tracks operational metrics (latency, tokens, errors) but doesn't include evaluation metrics for quality dimensions like faithfulness or safety.
Q: How does pricing work?
New Relic uses a consumption-based model. Free tier is available with limited data retention. Costs scale with data ingest volume.
Full Comparison Table
Confident AI | LangSmith | Langfuse | Arize AI | Datadog | Helicone | Portkey | Lunary | W&B Weave | New Relic | |
|---|---|---|---|---|---|---|---|---|---|---|
Built-in eval metrics Research-backed metrics for faithfulness, relevance, safety | 50+ metrics | Custom evaluators | Custom evaluators | Custom evaluators | Basic scorers | Limited | ||||
Quality-aware alerting Alerts on eval score drops, not just latency | ||||||||||
Drift detection Track quality changes across prompts and models | Limited | Limited | ||||||||
Multi-turn monitoring Evaluate conversations across turns | Limited | |||||||||
Cross-functional workflows PMs and QA can review, annotate, and run evals | Limited | |||||||||
Agent tracing Capture tool calls, reasoning, and execution flow | ||||||||||
Production-to-eval pipeline Traces become test datasets | Limited | Limited | Limited | Limited | ||||||
Framework-agnostic Consistent depth across frameworks | Limited | |||||||||
Safety monitoring Toxicity, bias, PII detection on production traffic | ||||||||||
Open-source option Self-host or inspect codebase | Limited | Limited | ||||||||
Multi-provider gateway Routing, caching, and failover across LLM providers |
How to Choose the Right LLM Observability Tool
The decision starts with what you actually need to observe. These tools solve different problems, and the right choice depends on where you are and what matters most.
If you need to know whether your AI outputs are good — not just that they happened: Confident AI is the only platform on this list that runs metrics like faithfulness, relevance, and safety automatically on production traffic, with alerts when quality drops. Most tools log traces — Confident AI evaluates them.
If your entire stack is LangChain: LangSmith provides the tightest integration and the best trace visualization within that ecosystem. If your stack is LangChain today and will be LangChain tomorrow, the native experience has value. Evaluation depth outside LangChain is more limited.
If you need open-source and self-hosting: Langfuse (MIT) and Arize Phoenix (ELv2) offer the strongest open-source options. Langfuse gives you tracing with prompt management. Phoenix gives you notebook-first observability for experimentation. Both require building your own evaluation layer on top.
If you already run Datadog or New Relic: Adding LLM monitoring to your existing APM is the path of least resistance. You get operational metrics (latency, tokens, costs) in a familiar interface. But these tools complement an AI quality platform — they don't replace one. Neither evaluates outputs.
If you need a gateway with routing and failover: Portkey and Helicone solve the reliability and cost problem. Portkey excels at routing, fallbacks, and load balancing with minimal overhead. Helicone adds caching and cost tracking via a proxy. Both provide observability as a bonus, not the core product.
If non-engineers need to participate in AI quality: This is where the field narrows the most. If PMs, QA, or domain experts need to review traces, annotate outputs, and run evaluation cycles independently, Confident AI is the only option on this list with cross-functional workflows. Every other tool requires engineering involvement at most steps.
If you're just starting out: Lunary provides the fastest path from zero to basic observability for RAG and chatbot applications. Langfuse's free tier is generous for engineering teams that want tracing. Both are good starting points before investing in a full evaluation platform.
Why Confident AI is the Best LLM Observability Tool for Evaluation and Monitoring
There are strong options on this list for different needs. Langfuse and Phoenix are great open-source foundations. LangSmith provides deep LangChain debugging. Helicone and Portkey solve the gateway problem. Datadog and New Relic serve teams that want LLM metrics inside their existing APM.
But none of them solve the fundamental problem: knowing whether your AI's output was good, and catching it when quality degrades.
Confident AI is the only platform on this list where evaluation IS the observability. Every trace is scored automatically with 50+ research-backed metrics. When faithfulness drops, hallucination rates rise, or safety scores degrade, alerts fire through PagerDuty, Slack, or Teams. Production traces are automatically curated into evaluation datasets for the next test cycle. Drift detection tracks quality changes across prompt versions, model updates, and user segments — so you catch degradation at the source, not just the aggregate.
The collaboration model is the widest gap. On every other platform on this list, AI quality is an engineering responsibility. Confident AI makes it a team effort. PMs trigger evaluations against production applications via HTTP. Domain experts annotate traces. QA runs regression tests. Engineers maintain full programmatic control but aren't the bottleneck for every quality decision.
Multi-turn simulation generates dynamic test scenarios. Red teaming covers PII leakage, prompt injection, bias, and jailbreaks without a separate vendor. CI/CD integration catches regressions before deployment. At $1/GB-month with no evaluation caps, it's the most cost-effective platform on this list for teams running AI at scale.
Observability without evaluation is just expensive logging. Confident AI closes the loop.
Frequently Asked Questions
What are LLM observability tools?
LLM observability tools help teams monitor, trace, and evaluate AI system behavior in production. They go beyond traditional application monitoring by assessing output quality — faithfulness, relevance, safety, hallucination rates — not just infrastructure metrics like latency and error rates.
How is LLM observability different from traditional APM?
APM tools (Datadog, New Relic) monitor infrastructure — latency, uptime, error rates, resource usage. LLM observability monitors output quality. A model can return a 200 response in 50ms and still hallucinate, leak PII, or produce biased content. LLM observability evaluates the actual content of responses using metrics that APM was never designed to capture.
Do I need a separate tool if I already use Datadog or New Relic?
For infrastructure monitoring, no. But if you need to evaluate output quality, detect quality drift, alert on evaluation score drops, or involve non-engineers in quality workflows, you'll need a purpose-built AI observability tool alongside your APM. Confident AI is designed to complement — not compete with — your existing infrastructure monitoring.
What's the difference between an AI gateway and an observability tool?
AI gateways (Helicone, Portkey) sit between your application and LLM providers to handle routing, caching, and failover. Observability is a built-in feature, not the core purpose. Dedicated observability tools provide deeper tracing, evaluation, alerting, and quality monitoring. Many teams run both — a gateway for reliability and cost optimization, and an observability platform for quality monitoring.
Which LLM observability tools are open source?
Langfuse (MIT), Arize Phoenix (ELv2), Helicone (Apache-2.0), Portkey (MIT), and Lunary (Apache-2.0) all have open-source components. Open-source options provide data ownership and infrastructure control but typically require building your own evaluation layer, alerting, and quality workflows on top.
Can LLM observability tools monitor multi-turn conversations?
Some tools support session-level grouping (Langfuse, LangSmith), but true conversational monitoring requires evaluation across turns — measuring coherence, context retention, and quality drift within a conversation. Confident AI evaluates conversation threads natively with metrics designed for multi-turn interactions.
What metrics should I track for AI observability?
At minimum: faithfulness (is the output grounded in context), relevance (does it answer the question), and safety (is it free from toxicity, bias, or PII leakage). For RAG systems, add context relevance and answer correctness. For agents, add tool selection accuracy and planning quality. For conversational AI, track coherence across turns. Operational metrics like latency and cost still matter but shouldn't be your only signals.
Can non-engineers use LLM observability tools?
On most platforms, no — observability workflows require engineering skills. Confident AI is the exception, with cross-functional workflows that let PMs, QA, and domain experts review traces, annotate outputs, and run evaluation cycles through a no-code interface.
How do I choose between so many options?
Start with the problem you're solving. If you need operational metrics in your existing APM, use Datadog or New Relic. If you need open-source tracing, use Langfuse or Phoenix. If you need a gateway, use Helicone or Portkey. If you need to know whether your AI outputs are actually good — with evaluation, alerting, drift detection, and cross-functional workflows — use Confident AI.