AI apps fail differently than traditional software — hallucinations, prompt drift, token cost spikes, and retrieval quality issues slip past standard APM. These five LLM-native observability tools trace every model call, evaluate outputs, and track cost and latency so you can actually debug what matters.
AI applications fail in ways traditional monitoring tools were never built to catch. A hallucinated response, a prompt that drifts over time, a token cost spike from an unexpected retrieval pattern, a RAG pipeline returning irrelevant chunks — none of these show up as HTTP 500s or latency anomalies in Datadog or New Relic. Standard APM sees request latency and error rates, but it doesn't see token counts, prompt quality, embedding drift, or retrieval relevance1.
A new category of LLM-native observability tools has emerged to fill that gap. These tools instrument at the model-call level, capturing prompts, completions, embeddings, costs, and latency — the signals that actually matter when you're debugging an AI system. Some also handle prompt versioning, automated evaluation, and provider routing.
Here are the five best options, ranked by what they do best.
Arize Phoenix is an open-source observability tool built on OpenTelemetry, specializing in RAG evaluation and embedding visualization1. If you want full control over your data and don't want to ship traces to a third-party SaaS, Phoenix is the strongest self-hosted option.
What makes it stand out: Phoenix is OpenTelemetry-native, which means it plays well with existing OTel pipelines and instrumentation. It includes RAG-specific metrics — you can evaluate retrieval quality, not just trace latency — and embedding drift detection, which helps you spot when your vector store or embedding model starts behaving differently over time1.
Who it's for: Teams running RAG pipelines who want deep, self-hosted observability without a per-seat SaaS bill. The price is free and open-source1.
Limitations: Phoenix focuses on observability and evaluation. It doesn't include prompt management, cost budgeting, or provider routing — pair it with another tool if you need those.
Langfuse is an open-source LLM engineering platform focused on end-to-end tracing, prompt management, and cost analytics2. It's self-hostable like Phoenix, but it leans harder into the operational side: prompt versioning, detailed cost tracking, and a managed cloud option for teams that don't want to run infrastructure.
What makes it stand out: Prompt versioning lets you track changes to prompts over time and roll back bad iterations — something that's surprisingly painful without a dedicated tool. The cost analytics break down spend by model, user, or session, which is essential when token costs are unpredictable2.
Who it's for: Teams that need prompt lifecycle management and granular cost visibility alongside tracing. The open-source self-hosted option is free; cloud plans start at $29/mo for teams that want managed hosting2.
Limitations: Langfuse's evaluation capabilities are more basic than dedicated eval platforms like Braintrust. If automated LLM-as-a-judge evaluation is your priority, look elsewhere.
LangSmith is the observability and evaluation platform built specifically for teams using LangChain and LangGraph3. If your stack is already LangChain, the integration is essentially zero-config — traces, evaluations, and prompt management plug directly into your existing chains and agents.
What makes it stand out: Deep LangChain integration means you get automatic tracing of every step in your chain or graph without manual instrumentation. The automated LLM-as-a-judge evaluations let you score outputs programmatically, and the prompt playground lets you iterate on prompts interactively3.
Who it's for: Teams whose AI stack is built on LangChain or LangGraph. The free tier covers small projects; Plus plans start at $39/user/mo for larger teams3.
Limitations: LangSmith is tightly coupled to the LangChain ecosystem. If you're using a different orchestration framework or raw API calls, you'll get less value and may want a framework-agnostic tool instead.
Portkey takes a different approach: it's an AI gateway first, with observability built in4. Instead of adding observability to your existing stack, Portkey sits between your application and your LLM providers, handling routing, failover, caching, and monitoring in one place.
What makes it stand out: Automatic failover between LLM providers means if OpenAI goes down, Portkey can route to Anthropic or another provider without code changes. It also includes a prompt CMS for managing prompts across environments and production observability for tracing and cost tracking4.
Who it's for: Teams that need provider routing and resilience alongside observability — especially production teams running multiple LLM providers who want automatic failover and unified monitoring.
Limitations: Portkey's observability is tied to its gateway. If you already have a routing layer (like LiteLLM8) or want standalone observability without changing your API gateway, Portkey may be more than you need.
Braintrust is an enterprise-grade AI development platform that tightly integrates evaluation, prompt iteration, and production monitoring5. It's designed for larger teams where data scientists, ML engineers, and non-technical stakeholders all need to collaborate on prompt quality and model performance.
What makes it stand out: Braintrust combines a high-performance log database, a non-technical collaborator UI for stakeholders who aren't developers, and hybrid deployment options for teams with strict data residency requirements5. The evaluation workflows are more sophisticated than most competitors — you can run complex eval suites, compare prompt versions side by side, and monitor production performance in the same platform.
Who it's for: Enterprise teams that need a full evaluation-to-monitoring pipeline with support for non-technical collaborators. A free tier is available; Pro plans start at $249/mo5.
Limitations: The pricing puts Braintrust firmly in the enterprise tier. For smaller teams or individual developers, the free tier may suffice but the Pro plan is a significant jump from the other tools on this list.
Phoenix vs Langfuse — Both are open-source and self-hostable, but they optimize for different things. Phoenix wins on OTel-native instrumentation and RAG-specific depth (embedding drift, retrieval evaluation). Langfuse wins on operational features: prompt versioning and detailed cost analytics1. If you're primarily debugging RAG quality, choose Phoenix. If you're managing prompt lifecycles and tracking spend, choose Langfuse. Some teams run both.
LangSmith vs Braintrust — Both offer evaluation and monitoring, but LangSmith is the natural choice if you're in the LangChain ecosystem3. Braintrust is framework-agnostic and built for enterprise scale, with hybrid deployment and non-technical collaborator support5. Choose LangSmith for LangChain shops; choose Braintrust if you need framework-agnostic enterprise eval workflows.
Portkey as the gateway-first option — If your primary need is provider routing and resilience with observability as a bonus, Portkey combines both in one tool4. Compare it against LiteLLM8 for open-source gateway needs, or W&B Weave6 and Logfire7 if you're exploring alternatives outside this top five.
Tools like Datadog and New Relic are excellent for monitoring traditional web services — HTTP latency, error rates, infrastructure metrics. But AI applications introduce a new class of failure modes:
LLM-native observability tools instrument at the model-call level, capturing the actual prompts sent, completions returned, embeddings used, and costs incurred1. That's the granularity you need to debug AI-specific problems.
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