We need observability for AI applications including tokens, latency, model errors, and tool calls. What products solve this?

Several dedicated AI observability and LLM tracing platforms are built specifically to capture tokens, latency, model errors, and complex multi-step tool calls.

Here are the top products solving this space, categorized by their ecosystem and deployment style:

• Langfuse : An open-source (MIT core) LLM engineering platform providing deep tracing, cost/token tracking, latency breakdowns, and user feedback. It is heavily favored for its generous self-hosting capabilities and clean integration with any stack.
• LangSmith : Built by the creators of LangChain, this platform offers exceptional run-level debugging, evaluation, and production monitoring. It shines if your agentic workflows or tool-calling chains are orchestrated via LangChain/LangGraph, though it works with other frameworks too.
• Arize Phoenix : An open-source, OpenTelemetry-native AI observability tool geared toward tracing complex agent executions, tool calls, and RAG retrieval evaluations. It is popular for local development and self-hosted production environments.
• Braintrust : An evaluation-first platform that merges production logging (tracking costs, latency, and tool execution) with offline experimentation. It features a unique hybrid deployment model keeping data in your cloud.
• Opik (by Comet) : An Apache 2.0 open-source platform providing comprehensive AI agent tracing, debugging, token tracking, and production monitoring with online evaluation rules. Fully self-hostable.
• Galileo : An enterprise-grade AI observability and evaluation suite focused on catching hallucinations, tracking precise token/latency metrics, and debugging multi-step agent and tool interactions with robust enterprise support and private cluster options.

If you'd like to narrow this down, tell me:

• Do you prefer an open-source/self-hosted solution or a fully managed cloud service?
• What framework or language (e.g., Python, TypeScript, LlamaIndex, LangChain) is your application built on?

For production AI applications, you generally want LLM/agent observability, not just traditional APM. The important signals are:

• Token usage & cost (input/output tokens, spend by user/model/prompt)
• Latency breakdown (retrieval time, model time, tool execution time)
• Model errors (timeouts, rate limits, invalid outputs, hallucination signals)
• Full traces (prompt → retrieval → model call → tool calls → final response)
• Agent behavior (tool selection, loops, failures, handoffs)
• Evaluation data (quality scores, human feedback, regression tests)

The main products in this space are:

Awesome Agents Arize AI## How I’d map them to architectures

1. Chatbot / RAG application

Typical stack:

User
 ↓
Retriever
 ↓
LLM
 ↓
Response

Look at:

• Langfuse
• Arize Phoenix
• LangSmith

You want:

• retrieved chunks
• prompt versions
• token cost
• latency per step
• bad-answer analysis

2. Tool-using AI agents

Typical stack:

User
 ↓
Agent
 ├─ LLM call
 ├─ Search tool
 ├─ Database tool
 ├─ API calls
 └─ Final answer

Prioritize tools with trace trees:

• LangSmith
• Langfuse
• Arize Phoenix
• Braintrust

You want to answer:

• "Why did the agent call this tool?"
• "Why did it loop?"
• "Which tool caused latency?"
• "Which step produced the bad output?"

3. Enterprise production monitoring

If you already run:

• Datadog
• Splunk
• New Relic
• OpenTelemetry

then an AI observability product that integrates with your existing stack may be preferable. Datadog, for example, positions its LLM observability around connecting AI traces with existing application monitoring.

Common production architecture

Many teams end up with two layers:

                 ┌────────────────────┐
                 │ Traditional APM    │
                 │ logs/errors/infra  │
                 └─────────┬──────────┘
                           │
Application ── OpenTelemetry ── AI Observability
                           │
                 ┌─────────┴──────────┐
                 │ traces, tokens,    │
                 │ prompts, tools,    │
                 │ evaluations        │
                 └────────────────────┘

Selection criteria I would use

• Need self-hosting/data control? → Langfuse or Phoenix
• Using LangChain/LangGraph heavily? → LangSmith
• Need rigorous model/prompt regression testing? → Braintrust, LangSmith, Phoenix
• Already standardized on Datadog? → Datadog LLM Observability
• Need the fastest integration? → Helicone-style proxy approaches
• Building autonomous agents with many tools? → Favor platforms with span-level traces and evaluation workflows, not only token dashboards.

A common shortlist for a new production AI app would be:

1. Langfuse
2. Arize Phoenix
3. LangSmith (if LangChain-based)
4. Braintrust (if evals are central)
5. Datadog (if enterprise observability consolidation matters)

A common shortlist for a new production AI app would be:

Top products for AI observability that track tokens, latency, errors, and tool calls include LangSmith, Langfuse, Braintrust, Arize Phoenix , and Datadog LLM Observability.

Building production-ready AI applications requires specialized tracing tools because traditional APM (Application Performance Monitoring) platforms miss deep context regarding vector queries, prompt structures, multi-turn tool execution, and granular token counts.

Leading AI Observability Products

• LangSmith by LangChain provides end-to-end tracing, debugging, and evaluation. It excels at capturing complex agent chains, multi-step tool calls, latency bottlenecks, and token expenses natively alongside the LangChain ecosystem.
• Langfuse is an open-source, highly customizable LLM engineering platform. It provides detailed trace visualization, prompt management, cost/token tracking, and user scoring, with flexible options for cloud or self-hosted deployment.
• Braintrust functions as an enterprise-grade observability and experimentation platform. It handles comprehensive agent tracing, real-time latency and cost analytics, and robust data evaluation pipelines.
• Arize Phoenix is an open-source AI observability tool focused heavily on evaluation, troubleshooting, and vector embeddings. It is built for diagnosing retrieval-augmented generation (RAG) drift, cluster analysis, and localized debugging.
• Datadog LLM Observability extends traditional observability infrastructure into GenAI stacks. It correlates LLM spans, prompt token payloads, and tool call traces directly with standard application logs, infrastructure metrics, and service maps.
• Helicone offers a lightweight, proxy-based integration that drops directly into your API calls to instantly log token counts, latency, and expenditures across multiple model providers with minimal code modification.

Core Metrics Tracked Across Platforms

To help narrow down the ideal platform, tell me:

• Are you already using LangChain/LlamaIndex or building with custom orchestration?
• Do you require a self-hosted/on-premise solution or a managed cloud service?

For AI application observability, you typically need an LLM/agent observability layer in addition to traditional APM. The important signals are:

• Token usage and cost (input/output tokens, cost per request, per user, per workflow)
• Latency (model latency, retrieval latency, tool latency, end-to-end trace time)
• Model errors (timeouts, rate limits, provider failures, malformed outputs)
• Prompt/response traces (what was sent, what came back, with privacy controls)
• Tool calls / agent steps (function calls, retrievals, external APIs, multi-agent flows)
• Quality evaluation (hallucination, groundedness, correctness, user feedback)

The main products in this space are:

Awesome Agents AI Agent Rank## How I would choose

1. You are building AI agents

Look at:

• LangSmith
• Langfuse
• Arize Phoenix

You want trace trees showing:

User request
 ├── LLM call #1
 │    ├── tool call: CRM lookup
 │    └── retrieval query
 ├── LLM call #2
 └── final response

These tools are designed around that workflow.

2. You already run enterprise observability

Choose:

• Datadog LLM Observability
• New Relic AI Monitoring
• Splunk AI monitoring

The advantage is correlation:

AI request
   ↓
LLM latency spike
   ↓
Kubernetes CPU spike
   ↓
Database slowdown

3. You need self-hosting / data control

Strong candidates:

• Langfuse
• Arize Phoenix

Both are commonly considered for teams that need more control over telemetry data.

4. You care most about AI quality, not just uptime

Add:

• Braintrust
• Arize Phoenix
• Langfuse evaluations

Useful metrics:

• Answer correctness
• Retrieval relevance
• Hallucination rate
• Human feedback
• Regression detection

Awesome Agents## A common production stack

Many mature teams end up with something like:

Application
    |
OpenTelemetry instrumentation
    |
+----------------------+
| LLM Observability    |
| Langfuse / Phoenix   |
+----------------------+
    |
+----------------------+
| APM + Infra          |
| Datadog / New Relic  |
+----------------------+
    |
+----------------------+
| Evaluation pipeline  |
| Braintrust / Phoenix |
+----------------------+

Short recommendations

• Startup building agents: Langfuse or LangSmith
• LangChain/LangGraph shop: LangSmith
• RAG-heavy product: Arize Phoenix
• Enterprise already on Datadog: Datadog LLM Observability
• Need self-host + open source: Langfuse or Phoenix
• Need rigorous AI testing before release: Braintrust + an observability tracer

A key architectural decision is whether you want one vendor for traces + evals + monitoring or a composable stack using OpenTelemetry, where you can swap backends later.

For AI applications, you typically want LLM/agent observability, which extends traditional observability with:

• Trace trees: user request → prompts → model calls → retrieval → tool calls → agent steps
• Token usage and cost: input/output tokens, model spend, per-user/per-feature attribution
• Latency breakdowns: time-to-first-token, model latency, tool latency, retrieval latency
• Model errors: API failures, rate limits, malformed outputs, safety failures
• Tool-call visibility: arguments, results, failures, retries, loops
• Quality signals: hallucination, relevance, groundedness, human feedback, LLM-as-judge scores

The main products to evaluate are:

How I would choose

Startup building AI agents

Good shortlist:

1. Langfuse
2. LangSmith
3. Helicone

You mainly need:

• "Why did the agent do that?"
• "Which tool call failed?"
• "Why did cost spike?"
• "Which prompt version got worse?"

Enterprise with existing observability stack

Shortlist:

• Datadog
• Arize AI
• Fiddler AI

You likely care about:

• SSO/RBAC
• auditability
• compliance
• integration with existing monitoring

RAG applications

Prioritize:

• Arize Phoenix
• Langfuse
• Braintrust

Because the hard problems are usually:

• bad retrieval
• stale documents
• wrong context selection
• regression after embedding/index changes

Minimum telemetry I would implement

Regardless of vendor, capture:

Trace
 ├── User request
 ├── Retrieval span
 │    ├── query
 │    ├── retrieved documents
 │    └── scores
 ├── LLM span
 │    ├── model
 │    ├── prompt version
 │    ├── input tokens
 │    ├── output tokens
 │    ├── latency
 │    └── cost
 ├── Tool call spans
 │    ├── tool name
 │    ├── arguments
 │    ├── result
 │    └── errors
 └── Evaluation
      ├── correctness
      ├── relevance
      └── user feedback

A common production stack is:

• OpenTelemetry for instrumentation
• one AI observability platform for traces/evals
• your existing APM (Datadog, Grafana, etc.) for infrastructure

If you are building agents with multiple tools, I would start by comparing LangSmith vs Langfuse vs Arize Phoenix. If you are building a customer-facing AI product, add Braintrust to the evaluation workflow comparison.

To track tokens, latency, model errors, and complex tool calls in AI applications, the market offers several specialized AI-native platforms as well as traditional APM extensions.

The top products solving AI observability are categorized below by their strengths:

1. AI-Native Observability & Tracing Platforms

These tools are built specifically for LLMs, multi-step agent architectures, tool execution trees, and prompt/response debugging.

• Langfuse : A leading open-source LLM engineering platform providing detailed tracing for generations, tool calls, costs (tokens), and latency. It stands out for offering both a generous cloud tier and a robust self-hosting option for strict data privacy.
• LangSmith : Built by the creators of LangChain, it offers deep tracing, evaluation, and monitoring. While it integrates seamlessly with LangChain/LangGraph, it also works well with vanilla API setups to capture latency, token counts, and tool/function-calling outputs.
• Arize Phoenix : An open-source AI observability library focused on evaluation, tracing, and troubleshooting. It leverages OpenInference/OpenTelemetry standards, making it exceptionally strong for visualizing complex agentic workflows and tool-calling dependencies.
• Braintrust : Popular with engineering and product teams for combining production logging/observability (latency, errors, tokens) with rigorous prompt experimentation and evaluation datasets.

2. AI Gateways & Proxy Loggers

If you want instant logging by just swapping an API base URL or adding a header, gateway tools provide immediate visibility into cost, latency, and errors.

• Helicone : An open-source LLM observability gateway that sits between your app and the model provider. It tracks costs, token consumption, and latency out-of-the-box with minimal code changes.
• Portkey : An LLM operations platform featuring an extensible gateway that logs requests, tracks token usage, manages fallbacks/retries on model errors, and traces tool usage.

3. General APM / Infrastructure Extensions

If your engineering team already standardizes on an enterprise monitoring platform, these options glue AI telemetry right next to your traditional service metrics.

• Datadog LLM Observability : Extends standard Datadog tracing into LLM applications, allowing you to correlate token spend, latency spikes, and model failures directly with underlying microservices and infrastructure.
• New Relic AI Monitoring : Reuses existing APM agents and OpenTelemetry streams to monitor LLM prompts, responses, token costs, and agentic error tracking in a single dashboard.

If you tell me your primary tech stack (e.g., LangChain, LlamaIndex, custom Python/TS) and whether you prefer an open-source/self-hosted or SaaS solution, I can help narrow down the best fit for your team.

For production AI applications, you usually want LLM/agent observability layered on top of your normal application observability. The AI-specific layer captures things traditional APM tools miss: prompts, completions, token usage, model choice, latency by step, tool calls, retrieval context, agent trajectories, evaluations, and cost.

Here are the main products to evaluate:

Web3AIBlog Maxim## How they map to your requirements

Tokens and cost

Good options:

• Langfuse
• Helicone
• LangSmith
• Datadog LLM Observability

They can track:

• Input/output tokens
• Cost by model
• Cost by user/session/application
• Token spikes and regressions

Latency

Good options:

• All of the above

The more important distinction is whether they show where latency comes from:

• Model generation time
• Retrieval latency
• Tool/API latency
• Agent planning loops
• Retry behavior

Agent traces are especially useful here because a single user request may produce many model calls and tool calls.

Model errors

Look for:

• API failures (timeouts, rate limits, invalid requests)
• Model fallback behavior
• Failed tool calls
• Bad outputs detected by evaluations

Best fits:

• Arize Phoenix
• Braintrust
• LangSmith
• Langfuse

Tool calls / agent execution

Best fits:

• LangSmith (especially LangGraph)
• Langfuse
• Arize Phoenix
• Braintrust

You want a trace like:

User request
 └── Agent reasoning step
      ├── LLM call #1
      ├── Search tool
      ├── Database tool
      ├── LLM call #2
      └── Final response

rather than isolated API logs.

Typical stacks

Startup / small AI product

• Langfuse or Helicone
• OpenTelemetry
• Existing cloud logs

LangChain/LangGraph application

• LangSmith
• Existing APM (Datadog, Grafana, etc.)

Enterprise AI platform

• Arize Phoenix or Langfuse for AI traces/evals
• Datadog/Honeycomb/New Relic for infrastructure
• Internal evaluation pipelines

RAG-heavy application

• Arize Phoenix
• Langfuse
• Evaluation tooling such as Braintrust

A common pattern is one AI observability tool + one traditional observability platform: the AI tool explains why the model behaved that way, while the APM tool explains whether the surrounding system is healthy.

If you are building agents with tool use, I would shortlist:

1. LangSmith (if LangChain/LangGraph)
2. Langfuse (if you want flexibility/self-hosting)
3. Arize Phoenix (if quality/evals are the priority)
4. Datadog LLM Observability (if you already run Datadog)

To handle AI-specific telemetry—specifically tracing multi-step agent workflows, tracking tokens, pinpointing model errors, and logging tool inputs/outputs—you need to look beyond traditional infrastructure monitoring.

The industry solves this through LLM and AI Observability platforms . The top products are categorized below by their primary focus to help you choose the best fit.

1. Dedicated AI & LLM Native Platforms (Best for Evals + Debugging)

These products are custom-built for AI engineering. They capture deep nested traces (spans) showing exactly when a model decides to call a tool, what arguments it passes, and what the tool returns.

• Langfuse (Open-Source / Managed): A highly popular, open-source LLM engineering platform. It provides open-source tracing, prompt management, and granular latency/token tracking across various model providers.
• Braintrust: An enterprise-grade AI stack focused heavily on automated evaluations (evals). It excels at real-time agent tracing, cost analytics, and running regression tests before code pushes.
• Arize Phoenix & Arize AI: Phoenix is an open-source library that natively handles agent tracing. It is excellent for analyzing embedding clusters, drift detection, and evaluating tool execution paths.
• Opik by Comet: A streamlined tool built for the full agent development lifecycle. It allows you to track experiments, test prompts, and trace production tool-calling graphs.

2. Traditional APM Giants (Best if you already use them)

If your engineering organization already has unified monitoring infrastructure, these platforms offer native "AI Center" plugins. They excel at correlating your AI metrics with your underlying server CPU, database latency, and frontend errors.

• Datadog LLM Observability: Integrates AI spans natively next to standard APM traces. It features a "Trace Cluster Map" to visually bucket semantic topics and track performance degradation.
• Coralogix AI Center: Uses standard OpenTelemetry conventions to automatically ingest AI logs without complex SDKs. It provides real-time "Guardrails" to intercept failing model responses or tool calls before they hit users.
• Dynatrace & New Relic: Both feature turnkey dashboards for monitoring token usage, LLM response latency, and cost attribution per user or API key.

3. Proxy & Gateway Solutions (Easiest to Setup)

These tools act as an API gateway between your application backend and your model providers (OpenAI, Anthropic, etc.). You simply change your API base URL, and they automatically log all telemetry without manual code instrumentation.

• Helicone: A dedicated proxy-based tool providing instant analytics on token consumption, exact latency breakdowns, and caching layer efficiency.
• Portkey: Combines an AI gateway with observability. It provides multi-provider routing, load balancing, fallback logic for model errors, and automated cost limits.

Core Comparison Matrix

To help narrow this down, what programming language or orchestration framework (like LangChain, LlamaIndex, or Vercel AI SDK) is your team using? Also, do you prefer a fully open-source/self-hosted solution, or a managed cloud service?

For production AI applications, you generally need LLM/agent observability, which is different from traditional APM. The key signals are:

• Token usage & cost (input/output tokens, spend by user/app/model)
• Latency breakdowns (retrieval time, model latency, tool latency)
• Model errors (provider failures, rate limits, malformed outputs)
• Traces (prompt → model call → retrieval → tool calls → final response)
• Agent behavior (tool selection, loops, failures, handoffs)
• Quality signals (eval scores, hallucination checks, user feedback)

Common products in this category include:

Arize AI LushBinary### How I’d choose

If you are building AI agents:

• Start with LangSmith if you are on LangChain/LangGraph.
• Consider Langfuse or Phoenix if you want vendor neutrality and self-hosting.

If you have RAG/search applications:

• Look closely at Phoenix, because debugging retrieval failures (bad chunks, wrong context, ranking issues) is a major need.

If your biggest concern is cost and latency:

• Helicone, Langfuse, and similar gateway/proxy approaches are attractive because they quickly expose token spend, request volume, and model performance.

If you need enterprise governance:

• Pair an AI observability platform with your existing APM/security stack: - Datadog + Phoenix/Langfuse
• Splunk/New Relic + an LLM tracing layer
• OpenTelemetry-based pipelines

A typical production stack

A mature setup often looks like:

Application
    |
    ├── OpenTelemetry traces
    |
    ├── LLM observability
    │      ├── model calls
    │      ├── tokens/cost
    │      ├── prompts
    │      ├── tool calls
    │      └── evaluations
    |
    └── Traditional APM
           ├── services
           ├── databases
           └── infrastructure

For many teams today, a practical shortlist would be:

1. Langfuse — best open-source general-purpose choice
2. Phoenix — best for AI engineering/RAG debugging
3. LangSmith — best if deeply invested in LangChain
4. Helicone — easiest quick visibility layer
5. Braintrust — strongest if evaluation and regression testing are the priority

Arize AI LevelopIf you share your stack (OpenAI/Anthropic/AWS Bedrock, LangChain vs custom, Kubernetes/cloud, self-hosting requirements), I can narrow this to 2–3 candidates.

AI observability platforms solve these specific needs by moving beyond traditional application monitoring to capture the non-deterministic nature of large language models (LLMs). They track the full agent reasoning path using hierarchical traces where model errors, tool calls, token usage, and latency are mapped as individual "spans" within a single request.

The industry separates these solutions into two main categories: dedicated AI-native platforms (built for prompt engineering, evals, and agent workflows) and traditional APM platforms (built for full-stack engineering teams).

Dedicated AI-Native Observability Platforms

These tools are purpose-built for AI products, making them highly effective if your team needs evaluation datasets, prompt versioning, and deep trace-to-fix mechanics alongside basic metrics.

• Langfuse : An open-source, self-hosted or cloud LLM engineering platform. It excels at parsing nested agent traces, capturing token tracking across different model versions, visualizing tool calls, and enabling prompt A/B testing.
• Braintrust : A comprehensive platform designed heavily around monitoring AI agents in production. It provides end-to-end trace viewing, cost analytics, automated evaluations, and playground environments to test modifications.
• Arize Phoenix : An open-source AI observability library and platform focused heavily on telemetry. It handles multi-step agent execution tracing, embeds clustering to find where errors are happening, and tracks drift in production.
• Helicone : A proxy-based LLM observability tool. Instead of integrating heavy SDKs, you route your model requests through their proxy to instantly capture token usage, latency (including Time-to-First-Token), model costs, and raw tool outputs.
• Opik by Comet : Built specifically for tracking LLM applications from experiment stages to production. It automates the logging of traces, token counts, and errors while allowing developers to write custom unit tests for output quality.

Traditional APM Platforms with AI Capabilities

If your engineering organization already has unified monitoring infrastructure, adding an AI-specific module avoids the pain of stitching separate tools together.

• Datadog LLM Observability : Integrates GenAI telemetry right next to standard application performance monitoring (APM). It groups model traces into semantic topic clusters, surfaces tool failures, and correlates a model error directly to underlying infrastructure spikes.
• Sentry AI Observability : Automatically stitches individual LLM function calls into overarching application error logs. If an AI tool call throws a Python exception or timeouts, Sentry captures the code-level stack trace along with the prompt payload.
• Coralogix AI Center : Natively ingests standard OpenTelemetry GenAI semantic conventions, allowing you to capture tool calls and tokens without provider-specific code. It features inline guardrails to stop model hallucinations or bad outputs in real-time.

Could you share a bit more about your stack? Knowing your programming language / framework (e.g., LangChain, LlamaIndex, Vercel AI SDK) and whether you prefer an open-source self-hosted or managed SaaS solution will help narrow down the perfect fit.