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

To track tokens, latencies, model errors, and multi-step tool calls, you need LLM observability and tracing platforms . Traditional APMs (like Datadog or New Relic) see only raw HTTP requests, missing the semantic chain of agent steps, internal thoughts, and tool arguments.

The top products solving AI observability are categorized below by how they integrate into your stack:

Dedicated AI Tracing & Observability Platforms

These tools provide deep, span-level telemetry specifically built to capture prompts, completions, token usage breakdown, latency per step, and tool execution payloads.

AI Gateways with Built-In Observability

If you want to manage LLM traffic, rate limits, caching, and failovers while automatically logging metrics without heavy SDK instrumentation in every file, a gateway approach works best.

Alternative & Open-Source Contenders

• Comet Opik — An open-source (Apache 2.0) platform for logging, evaluating, and tracking LLM traces and agent workflows.
• MLflow — Traditional ML lifecycle tool featuring robust, open-source native LLM evaluation and tracing features.

If you can share whether you are self-hosting or using a cloud service , and whether your application uses vanilla API calls or an agent framework (like LangChain, LlamaIndex, or custom loops), I can narrow down the best fit for your architecture.

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

• Token usage and cost (input/output tokens, spend by user/app/model)
• Latency (end-to-end, model latency, tool latency)
• Model errors (timeouts, rate limits, bad responses, provider failures)
• Traces (prompt → model call → retrieval → tool calls → final answer)
• Agent/tool execution (which tools were called, arguments, outputs, loops)
• Quality signals (hallucination, relevance, user feedback, eval scores)

Common products:

Maxim StackBriefly## How I’d choose

If you are building AI agents

Start with:

• LangSmith if you want a polished managed platform.
• Langfuse if you want control, open source, or self-hosting.
• Arize Phoenix if retrieval quality and evaluations are central.

Turion LangChain### If you mainly need cost + latency monitoring

Look at:

• Helicone
• Portkey
• Datadog LLM Observability

These are closer to an LLM gateway/operations layer.

If you are running enterprise production systems

A common architecture is:

Application
   |
OpenTelemetry instrumentation
   |
+----------------------+
| AI observability     |
| traces + evals       |
+----------------------+
   |
+----------------------+
| Existing APM          |
| Datadog/New Relic/etc |
+----------------------+

Many teams combine:

• Langfuse or LangSmith → trace/debug AI behavior
• Datadog/New Relic/Grafana → infrastructure and SRE monitoring
• Braintrust/Arize/Phoenix → evaluation pipelines

Morph Web3AIBlogFor a new AI product today, a practical shortlist would be:

1. Langfuse — best default if you want flexibility/open source
2. LangSmith — best if your team is agent-heavy and wants a mature workflow
3. Arize Phoenix — best for RAG/evaluation depth
4. Datadog LLM Observability — best if your company already runs on Datadog
5. Braintrust — best if quality evaluation is the primary problem

Turion LangChainI would also make sure whichever tool you pick supports OpenTelemetry, because it reduces lock-in and lets you change backends later.

To track tokens, latency, model errors, and complex tool calls in AI and agentic applications, a dedicated class of LLM Observability and Evaluation platforms has emerged. These tools record multi-step traces, map out tool execution arguments, and measure performance metrics.

The leading products solving this space fall into distinct categories depending on your preference for open-source, SaaS, or enterprise stack alignment:

1. Top Open-Source & Self-Hosted Options (Data Privacy Focus)

• Langfuse: A leading open-source LLM engineering platform. It provides deep tracing for token counts, latencies, model errors, and granular tool execution spans, with strong options for self-hosting if you have strict data residency requirements.
• Arize Phoenix: Built by Arize, Phoenix focuses heavily on open-source AI observability and evaluation. It was an early adopter of OpenTelemetry (OTel) standards, making it great for tracking embedding quality, retrieval (RAG) components, and agentic tool-calling paths.

2. Best for Ecosystem Integration & CI/CD

• LangSmith: The native observability and debugging platform built by the creators of LangChain. While it works with any framework, it offers peerless tracing depth, latency breakdowns, error tracking, and prompt playgrounds if your stack relies on LangChain or LangGraph.
• Braintrust: Excellent for eval-driven development. It pairs production tracing (tracking cost, tokens, and latencies) directly with dataset management and CI/CD testing gates so you can regression-test prompts and tool definitions before shipping.

3. Enterprise & Traditional APM Integration

• Arize AX: The commercial enterprise tier of Arize, bridging deep agent evaluations, graph visualizations of tool paths, and production monitoring.
• Datadog LLM Observability: Ideal if your engineering organization already runs on Datadog for traditional infrastructure and application performance monitoring (APM) and wants AI telemetry consolidated into the same dashboard.

4. Lightweight / Proxy-Based Tracking

• Helicone: Sits as an ultra-fast proxy between your application and LLM providers. It requires minimal code changes to capture global token usage, costs, and latencies out-of-the-box.

To help narrow down which tool fits best, tell me:

• Do you need a self-hosted/open-source solution or a managed SaaS platform?
• Are you building with standard frameworks like LangChain/LangGraph or custom agent architectures?

Yes. What you’re describing is usually called LLM/AI observability or agent observability. The main products I’d shortlist are:

My take

• Start with Langfuse if you want a broadly capable product without getting locked into a particular agent framework. It covers traces, token usage, cost, latency, tool calls, sessions, prompts and evaluations, and is open source/self-hostable. Current industry comparisons consistently put it near the top for general-purpose AI observability.
• Choose LangSmith if you're building heavily on LangChain/LangGraph. Its trace representation maps naturally to chains, agents and tool calls, which makes debugging multi-step agents particularly good.
• Choose Arize Phoenix if OpenTelemetry, data ownership, or self-hosting is important. Phoenix is OTEL-native and provides token, latency and cost tracking plus agent trajectory visualization and online evaluations.
• Choose Braintrust if the bigger problem is "is my AI getting better?" rather than just "what happened?" It is particularly strong around datasets, evaluations and regression testing.
• Choose Datadog if your organization already lives in Datadog. The advantage is having conventional infrastructure telemetry and LLM telemetry in the same operational ecosystem.

One important distinction

I'd separate observability from evaluation:

Observability answers:

What happened during this request? You want a trace like:

user request → LLM → retrieval → tool call → LLM → tool call → final response

with tokens, cost, latency, errors, prompts/responses, tool arguments/results and metadata attached to each span.

Evaluation answers:

Was what happened actually good? That's where things like hallucination detection, answer correctness, tool-selection quality, policy compliance, user feedback and regression tests come in. Modern platforms increasingly combine both, but their strengths differ.

If I were evaluating vendors

I'd run the same production-like agent through Langfuse, LangSmith, Phoenix, Braintrust and Datadog and score them on:

1. Trace fidelity — can I reconstruct an entire agent run?
2. Tool-call visibility — arguments, results, failures and timing.
3. Token/cost attribution — by model, user, feature, agent and request.
4. Latency breakdown — model vs retrieval vs tools vs orchestration.
5. Error investigation — can I go from an alert to the exact problematic trace?
6. Sampling/storage economics — what does 10M+ traces/month actually cost?
7. Privacy — can prompts/tool outputs be redacted or kept in your infrastructure?
8. OpenTelemetry support — can you migrate later without rewriting instrumentation?
9. Evals — can production failures become regression tests?
10. Agent UX — can an engineer understand a 30-step agent trace in seconds?

For a new production AI platform today, my default shortlist would be Langfuse + Phoenix + LangSmith + Braintrust, with Datadog added if you're already standardized on it. The market is also increasingly converging on OpenTelemetry, which makes the underlying instrumentation choice almost as important as the observability vendor.

One caveat: I would not make Helicone my first choice for a new deployment. Recent 2026 sources report its acquisition by Mintlify and a move toward maintenance mode, despite its historically attractive proxy-based simplicity.

Evaluation answers:

Was what happened actually good? That's where things like hallucination detection, answer correctness, tool-selection quality, policy compliance, user feedback and regression tests come in. Modern platforms increasingly combine both, but their strengths differ.

If I were evaluating vendors

To monitor and debug tokens, latency, model errors, and tool calls in AI applications, you need LLM/Agent Observability platforms . Traditional APM tools (like standard Datadog or New Relic without their AI modules) only see HTTP requests and miss the semantic payload, step-by-step reasoning, and tool trajectories.

The top products solving this space are categorized below by how they fit into a tech stack:

1. Dedicated AI-Native & Developer-First Platforms

These are purpose-built for tracing multi-step agent graphs, tool executions, and LLM input/output payloads.

• LangSmith : Built by the creators of LangChain, excellent for deep distributed tracing, monitoring token counts, latency (P50/P99), tool call arguments/outputs, and managing prompts or evaluation datasets. Works with any framework, not just LangChain.
• Langfuse : A leading open-source/self-hostable LLM engineering platform. It captures complete traces, token costs, latency, and user feedback, making it a top choice if data residency/privacy is a strict requirement.
• Braintrust : Highly regarded for combining production observability and tracing with evaluation-gated CI/CD pipelines. Strong tracking for token usage, latency, and tool trajectories with generous developer tiers.
• Arize Phoenix : Open-source, heavily focused on OpenTelemetry standards, making it ideal for teams wanting to avoid vendor lock-in while tracing complex tool calls and RAG pipelines.

2. Unified Enterprise APM & Infrastructure Extensions

If your SRE/Ops team already relies on a major enterprise monitoring platform, these extensions pull AI telemetry directly into existing dashboards.

• Datadog LLM Observability : Auto-instruments major providers (OpenAI, Anthropic, Bedrock, LangChain) to capture token usage, latency, and model errors. Its core advantage is correlation —placing AI spans right next to your standard application performance, database latency, and infrastructure metrics.
• OpenObserve : A unified open-source observability platform that records both standard infrastructure logs/metrics and AI pipeline traces in a single place with per-model token cost analytics.

3. Proxy & Gateway-Based Tracking

• Helicone : Sits as a lightweight proxy between your application and LLM providers. It requires virtually zero code changes to instantly track costs, tokens, latency anomalies, and caching efficiency.

If you can share your preferred deployment model (fully managed cloud vs. self-hosted/open source) and whether your stack uses OpenTelemetry or specific frameworks like LangGraph or CrewAI , I can help narrow down the ideal product fit.

For AI application observability (LLM apps, RAG systems, agents), you’re looking for tools that go beyond traditional APM. The important signals are usually:

• LLM traces: prompt → model call → response → downstream steps
• Token usage and cost: input/output tokens by user, feature, model, workflow
• Latency breakdowns: model latency, retrieval latency, tool latency
• Errors: provider failures, rate limits, malformed outputs, tool failures
• Agent/tool-call visibility: multi-step execution trees
• Evaluation: hallucination, relevance, correctness, safety, regression testing

The main products in this category are:

How I’d choose

1. Production AI agent application

• Start with LangSmith, Langfuse, or Phoenix.
• You want trace trees showing:
  • user request
  • model calls
  • retrieval steps
  • tool calls
  • retries/failures
  • token spend

2. Need self-hosting / sensitive data

• Look hardest at Langfuse and Phoenix.
• Both are designed around open instrumentation approaches and self-managed deployments.

3. Building RAG systems

• Consider Phoenix or Arize AI because retrieval quality and evaluation are major parts of the problem.

4. Already standardized on LangChain

• LangSmith is usually the most natural choice because it understands LangGraph/LangChain execution structures deeply.

5. Need enterprise-wide observability

• Pair AI-native tracing with your existing APM:
  • Datadog + AI observability
  • New Relic AI monitoring
  • Dynatrace AI capabilities
  • OpenTelemetry-based pipelines
    (AI observability is increasingly converging with traditional observability stacks.)

A common 2026 stack looks like:

Application
   |
OpenTelemetry / AI instrumentation
   |
+----------------+
| AI Observability|
+----------------+
| Langfuse       |
| LangSmith      |
| Phoenix        |
| Braintrust     |
+----------------+
   |
APM / Logs / Metrics
(Datadog, Grafana, Splunk, etc.)

For a greenfield AI product, I’d typically shortlist Langfuse vs LangSmith vs Phoenix first; they cover the core “what happened inside my AI system?” problem most directly.

Yes. This is now a fairly mature LLM/AI observability category. The main products I'd evaluate are:

These platforms generally capture much more than conventional APM: prompts/responses, model/provider metadata, token consumption and cost, latency, errors, nested agent/tool traces, and—increasingly—evaluation and quality signals.

How I'd narrow it down

If you're building a general-purpose AI application:
I'd start with Langfuse, Braintrust, and Arize Phoenix. They are the strongest shortlist if you want actual AI-specific traces rather than merely wrapping LLM API calls in conventional logs.

If you're using LangChain/LangGraph:
LangSmith is probably the obvious choice because the instrumentation and agent visualization are deeply integrated.

If self-hosting/data control matters:
Look hardest at Langfuse or Phoenix. Both are open-source/self-hostable and use OpenTelemetry-oriented approaches.

If your organization already runs Datadog:
I'd strongly consider Datadog LLM Observability rather than introducing another monitoring system. It puts LLM/agent telemetry alongside infrastructure and service telemetry.

If evaluation is as important as observability:
I'd put Braintrust near the top. Its differentiator is connecting production traces to datasets/evals and ultimately release-quality decisions, rather than treating observability as just a dashboard.

If you want the quickest "just show me every API call, token count and cost" solution:
Helicone is worth looking at; its proxy architecture makes it particularly easy to introduce with minimal application changes.

One important distinction

I'd separate the market into three layers:

1. Traditional APM — Datadog/New Relic/etc.
   Great for service latency, HTTP errors, CPU, infrastructure, etc.

2. LLM observability — Langfuse, LangSmith, Phoenix, Braintrust, Helicone, etc.
   Answers "What did the AI application actually do?"—model calls, prompts, tokens, retrieval, tool calls, agent steps, cost, etc.

3. AI quality/evaluation — Braintrust, Phoenix, LangSmith, W&B Weave, Galileo, etc.
   Answers "Was what it did actually good?"—hallucination, relevance, correctness, safety, regression testing, human feedback, and automated evals.

For an agentic application, #2 + #3 are increasingly inseparable. A trace showing that an agent made five tool calls is useful; knowing that the third tool call was wrong and caused the final answer to fail is considerably more valuable.

If you tell me your stack (e.g. OpenAI/Anthropic + Python/Node + LangGraph or custom agents + Datadog or OpenTelemetry), I can narrow this to the 3 best choices and compare architecture, pricing, and instrumentation effort.

To capture tokens, latency, model errors, and tool calls across modern AI applications and multi-step agents, the market has converged on specialized AI observability platforms.

The top products solving this span open-source self-hosted tools, developer-first SaaS, and enterprise APM extensions.

Dedicated AI Observability & Tracing Platforms

1. • LangSmith — Best for deep agent debugging and multi-step tracing. Built by the creators of LangChain, it handles complex execution flows, tool invocations, token tracking, and latency profiling natively (though it works with any framework).
2. • Langfuse — Best open-source and self-hostable option. It features a permissive MIT license for core tracing, prompt management, cost/token tracking, and evaluation workflows.
3. • Arize Phoenix — Best OpenTelemetry-native and evaluation-heavy option. Built on open semantic conventions (OpenInference ), it excels at tracing agent steps, tool calls, and retrieval (RAG) quality while offering an open-source local/self-hosted on-ramp.
4. • Braintrust — Best for eval-driven development. It integrates production trace capture tightly with regression testing, CI/CD quality gates, and multi-model cost analytics.
5. • Maxim AI — Best for collaborative agent evaluation. It offers robust tool-call success monitoring, multi-turn session tracing, and cross-functional annotation workflows for engineering and product teams.

Proxy & Gateway-Based Tools

1. • Helicone — Best for zero-code-change implementation. It sits as a proxy in front of your LLM providers, instantly capturing tokens, latency, costs, and request errors without requiring deep SDK instrumentation.
2. • Portkey — Best for combining an AI Gateway with observability. It centralizes routing, fallbacks, caching, and guardrails alongside comprehensive cost and latency logging.

Enterprise APM Extensions

1. • Datadog LLM Observability — Best for teams already standardized on Datadog. It maps LLM spans, token usage, latency breakdowns, and semantic clusters directly into your existing infrastructure dashboards.

If you can share your primary programming language/framework (e.g., Python/LangGraph, TypeScript, custom API calls) and whether you require a self-hosted/open-source deployment or a managed cloud SaaS , I can recommend the exact tool that fits your stack.

Yes. The category is usually called LLM/AI observability or agent observability. For your requirements—tokens, latency, model errors, and tool calls—there are several credible options.

OpenTelemetry's GenAI semantic conventions are increasingly important here: they standardize things such as model, input/output tokens, latency, finish reasons, and structured tool-call information.

My shortlist

1. Langfuse — probably the strongest default

Good choice if you have a heterogeneous AI stack and want something developer-oriented. It provides traces down to individual LLM calls, token usage/cost, latency, prompts, and tool/agent execution. It is also self-hostable, which is a major advantage if prompts and outputs contain sensitive data. Current market comparisons consistently put it among the leading general-purpose choices.

2. LangSmith — best if you're using LangChain/LangGraph

If your applications are built around LangChain or LangGraph, I'd seriously consider this first. The hierarchical view of an agent run—LLM → retrieval → tool → LLM → tool → final response—is particularly useful for debugging agents.

3. Arize Phoenix — strongest open-source alternative

Worth looking at if you want deep tracing and evaluation capabilities without committing to a proprietary observability platform. It is particularly attractive for teams with ML/AI engineering expertise.

4. Braintrust — best if "did the model do the right thing?" matters

I'd choose this when observability is only half the problem and you also need systematic evaluations, regression testing, datasets, and experiment comparison. In other words, it moves toward AI quality engineering, rather than just APM.

5. Datadog — best enterprise consolidation play

If you're already deeply invested in Datadog, its AI/agent observability is compelling because you can correlate AI traces with the rest of your application's infrastructure. OpenTelemetry-based AI telemetry is also increasingly supported across conventional observability stacks.

One architectural point I'd strongly recommend

Don't make the observability vendor your instrumentation layer.

Use OpenTelemetry/GenAI semantic conventions as the underlying telemetry model, then send that telemetry to whichever backend you choose. OTel now defines standard attributes for things like:

• gen_ai.request.model
• input/output token counts
• model latency
• finish reasons
• LLM calls
• tool calls
• prompts/responses when explicitly enabled

That gives you considerably more freedom to change observability vendors later.

For an AI agent, the trace you ultimately want looks roughly like:

User request
 └─ Agent run                         8.2s
     ├─ LLM: GPT-5                    1.8s  2,340 tokens
     ├─ Tool: search                  0.7s
     ├─ LLM: GPT-5                    2.1s  1,120 tokens
     ├─ Tool: database                0.4s
     ├─ Tool: payment API             1.6s  ❌ timeout
     └─ LLM retry                     1.6s  890 tokens

That's much more useful than simply recording "POST /v1/chat/completions = 200."

If I were evaluating this today: I'd do a bake-off between Langfuse, LangSmith, Braintrust, and Datadog, while instrumenting the application with OpenTelemetry underneath. That gives you coverage of the four distinct questions: what happened, how much did it cost, why was it slow/broken, and was the result actually good?

To track tokens, latency, model errors, and tool calls in AI and agentic applications, you need tools capable of step-level distributed tracing rather than traditional server metrics.

The leading products that solve this space fall into three distinct categories depending on your architecture and deployment needs:

1. Dedicated AI/LLM Observability Platforms (Best for Deep Tracing & Evals)

These tools are built from the ground up to render multi-step agent loops, tool executions, and token metrics.

• • Langfuse: A popular open-source, self-hosted or cloud platform that provides exceptional, low-friction trace viewing, cost analytics, and prompt management.
• • LangSmith: Built by the LangChain team, offering premier tracing and deep debugging for complex agentic workflows, especially if you use LangChain/LangGraph.
• • Pydantic Logfire: An AI-native observability platform built on OpenTelemetry by the creators of Pydantic, excellent for high-performance Python and multi-turn agent instrumentation.
• • Braintrust: An enterprise-grade, evaluation-focused platform featuring robust production monitoring alongside token and cost tracking.
• • Arize Phoenix: An open-source, OpenTelemetry-native toolkit specializing in tracing, embedding clustering, and drift detection.

2. AI Gateways & Proxy-Based Tools (Best for Zero-Code Setup)

These platforms sit directly in your application's data path as an API proxy, capturing token usage, errors, and latencies instantly without heavy SDK adoption.

• • Helicone: A lightweight proxy approach that logs every token, cost, and latency metric across multi-provider requests with minimal setup overhead.
• • Portkey: Combines an AI gateway with production observability, letting you track latency and tokens while managing routing, caching, and guardrails across different model providers.

3. Traditional Enterprise APM Add-ons (Best for Unified Infrastructure)

If your engineering organization already runs centralized monitoring and wants AI data correlated directly with standard application logs and infrastructure metrics:

• • Datadog LLM Observability: Auto-instruments major AI frameworks and model providers to inject LLM spans directly into your existing Datadog dashboards.
• • New Relic AI Monitoring: Provides correlated telemetry tracking for token usage, latency, and agent traces within standard application performance panels.

If you tell me your primary programming language/framework (e.g., Python/LangGraph, TypeScript, custom setup) and whether you prefer an open-source/self-hosted or managed cloud solution, I can recommend the exact tool to start with.