OpenTelemetry
How the 9 leading AI models rank OpenTelemetry in LLM observability tools.
How each AI ranks it
average position · verbatim answers kept
ChatGPTread the answer9th
As of September 2026, the LLM observability tools I’d seriously shortlist are:
Short answer: top picks
- Langfuse — best default for most teams that want open-source/self-hostable LLM tracing + prompt management + evals.
- LangSmith — best if you’re using LangChain/LangGraph or want a polished hosted, end-to-end developer workflow.
- Arize Phoenix / Arize AX — best for OpenTelemetry-native tracing, RAG/agent debugging, and enterprise-grade monitoring.
- Braintrust — best if your main problem is evaluation, regression testing, and release gates.
- Helicone — best lightweight gateway/proxy for logging, cost, latency, caching, and quick setup.
- W&B Weave — best if your ML/AI team already lives in Weights & Biases.
- Datadog LLM Observability / Agent Observability — best if your company already standardizes on Datadog.
- Comet Opik — strong open-source alternative focused on evals, monitoring, prompt optimization, and self-hosting.
Recommended ranking
| Rank | Product | Best for | Why people use it |
|---|---|---|---|
| 1 | Langfuse | Open-source / self-hostable LLM observability | Probably the best “default” if you want traces, prompt management, datasets, evals, metrics, and self-hosting in one tool. Langfuse publicly claims 50,000+ companies, ~34k GitHub stars, 50M+ SDK installs/month, and 6M+ Docker pulls; it was acquired by ClickHouse in January 2026 while stating it remains open-source and self-hostable. (langfuse.com) |
| 2 | LangSmith | LangChain / LangGraph apps, agent tracing, hosted workflow | The obvious choice if your app is built around LangChain or LangGraph. LangSmith’s docs position it as observability for LLM apps from traces to production-wide metrics, with evaluation, prompt engineering, and deployment workflows included. (docs.langchain.com) |
| 3 | Arize Phoenix / Arize AX | OpenTelemetry-native tracing, RAG/agent evals, enterprise monitoring | Phoenix is a strong open-source observability/evaluation option built around OpenTelemetry and OpenInference, with tracing for model calls, retrieval, tool use, and custom logic; Arize AX is the enterprise/online monitoring side. Phoenix also publicly notes 10k+ GitHub stars. (arize.com) |
| 4 | Braintrust | Evaluation-first LLM development | If you care more about systematic evals, prompt/model experiments, online scoring, CI/CD regression gates, and turning production logs into test datasets, Braintrust is one of the strongest specialist tools. (braintrust.dev) |
| 5 | Helicone | Quick API gateway/proxy observability | Good for teams that want fast setup around request logs, latency, token/cost tracking, caching, rate limits, routing, and provider visibility without fully buying into a heavier eval platform. Helicone describes itself as both an AI Gateway and LLM observability platform. (docs.helicone.ai) |
| 6 | W&B Weave | Teams already using Weights & Biases | Weave is W&B’s observability/evaluation platform for LLM apps, with tracing of LLM calls, function tracing, debugging, evaluations, and production workflow tracking. It’s especially attractive if your org already uses W&B for ML experiments. (docs.wandb.ai) |
| 7 | Datadog LLM Observability / Agent Observability | Enterprises already on Datadog | Best when you need LLM traces and metrics integrated with the rest of your infra/APM/security dashboards. Datadog describes LLM observability as tracking inputs, outputs, token usage, latency, and evaluation signals across model chains, with provider support such as OpenAI and Bedrock. (datadoghq.com) |
| 8 | Comet Opik | Open-source eval + observability alternative | Opik is a credible open-source platform for tracing, automated LLM-as-judge/heuristic evaluations, prompt optimization, prompt versioning, online eval rules, cost/latency/error monitoring, and self-hosting via Docker or Kubernetes. (comet.com) |
My practical recommendations
If you want the safest default
Use Langfuse.
It has the best blend of open-source credibility, adoption, self-hosting, tracing, prompt management, evals, and production usability. It’s the one I’d pick for a startup or product team that wants to own its data and avoid getting locked into a hosted-only workflow.
If you’re building with LangChain or LangGraph
Use LangSmith.
It is the most natural choice for LangChain/LangGraph workflows because tracing, debugging, evaluation, and monitoring are deeply aligned with that ecosystem. (docs.langchain.com)
If evals are your biggest pain
Use Braintrust or Comet Opik.
Braintrust is the cleaner “eval-first” commercial workflow. Opik is compelling if you want open-source/self-hosting plus evals and prompt optimization.
If you need enterprise ML/LLM monitoring
Use Arize Phoenix + Arize AX.
Phoenix is strong for local/open-source tracing and eval workflows; Arize AX is the more enterprise production-monitoring layer. Phoenix’s OpenTelemetry/OpenInference orientation also makes it appealing if you care about telemetry portability. (arize.com)
If you just want fast cost/latency/request logging
Use Helicone.
It’s often the easiest path if your immediate problem is “I need to see every LLM request, cost, latency, model, user, errors, cache hits, and rate limits.”
If you already use Datadog or W&B
Use Datadog LLM Observability or W&B Weave, respectively.
Don’t add another platform unless the AI-native tools give you something you truly need. The operational advantage of keeping traces, alerts, infra metrics, and incidents in one stack is real.
Selection guide
| Your situation | Pick |
|---|---|
| Open-source, self-hosted, general-purpose | Langfuse |
| LangChain / LangGraph-heavy app | LangSmith |
| RAG / agents / OpenTelemetry-first tracing | Arize Phoenix |
| Enterprise model + LLM monitoring | Arize AX |
| Evaluation-first development | Braintrust |
| Cheap/simple gateway logging | Helicone |
| Existing W&B organization | W&B Weave |
| Existing Datadog organization | Datadog LLM Observability |
| Open-source eval-heavy alternative | Comet Opik |
Tools I’d treat as complementary, not primary
- Promptfoo, DeepEval, Ragas — useful for evaluation/testing, but not full observability platforms.
- OpenLLMetry / Traceloop / OpenInference — useful instrumentation layers, especially if you want OpenTelemetry-style portability; OpenTelemetry’s semantic conventions provide common naming/attribute schemes for spans, metrics, and logs. (opentelemetry.io)
- LiteLLM, Portkey, Requesty — more gateway/routing/provider-management oriented; useful alongside observability.
- Humanloop — worth considering if prompt management, evals, human feedback, and product-team workflows matter more than raw tracing. Humanloop describes itself as an enterprise LLM evals platform with evaluation, prompt management, and observability. (humanloop.com)
- AgentOps — worth a look for agent-specific debugging/monitoring, especially with CrewAI/AutoGen-style workflows. (docs.agentops.ai)
Final recommendation
For most teams, I’d start here:
Default stack:
Langfuse for observability + prompt management + evals.
If LangChain-heavy:
LangSmith instead.
If evals become the bottleneck:
Add or switch to Braintrust.
If enterprise monitoring matters:
Use Arize or Datadog, depending on whether you want AI-native monitoring or to stay inside an existing observability stack.
If you only need cost and request visibility fast:
Start with Helicone.
Claudenot ranked
Perplexitynot ranked
DeepSeekread the answer13th
Here’s a practical rundown of the LLM observability tools that people actually use, what they’re best at, and how to choose.
Quick recommendations
- Best open-source / self-hostable: Langfuse or Arize Phoenix
- Best if you live in LangChain: LangSmith
- Simplest / most lightweight: Helicone
- Best for full-stack enterprise monitoring: Datadog LLM Observability or New Relic
- Best if you already use W&B: Weights & Biases Weave
- Best if you already use MLflow / Databricks: MLflow LLM Tracking
Top tools in detail
1. Langfuse
Type: Open-source + cloud | Best for: Teams that want a full LLM engineering platform without vendor lock-in.
Langfuse is one of the most popular open-source LLM observability tools. It gives you:
- Trace/span tracking across chains, agents, and tools
- Prompt versioning and prompt management
- Evaluations and feedback collection
- Cost and latency analytics
- Self-hosting via Docker or a managed cloud option
It also integrates with OpenAI, Anthropic, LangChain, LlamaIndex, and can ingest OpenTelemetry traces.
Why people use it: Open source, actively maintained, strong community, and it’s more than just tracing — it’s a full prompt engineering platform.
2. LangSmith
Type: SaaS (by LangChain) | Best for: Anyone building production apps with LangChain.
LangSmith is the official observability platform from the LangChain team. It provides:
- Detailed traces of every step in a LangChain or agent workflow
- Dataset creation for regression testing
- Built-in evaluation suites
- Debugging tools for prompt/LLM failures
- Monitoring dashboards for production
Why people use it: If you use LangChain heavily, LangSmith is the smoothest experience because it’s deeply integrated. If you avoid LangChain, it’s less relevant.
3. Arize Phoenix
Type: Open-source | Best for: Data scientists and ML engineers who want local, panel-style experimentation.
Phoenix is Arize’s open-source LLM observability and evaluation library. It’s very popular for:
- Local tracing of LLM calls
- Embedding visualization and clustering
- Running evaluations automatically
- Experimenting before deploying to production
- Works with OpenTelemetry
Groknot ranked
Copilotnot ranked
Google AInot ranked
Google AI Modenot rankedSources AI cited for OpenTelemetry
Pages on opentelemetry.io that AI models referenced in their answers about LLM observability tools. Receipts for the ranking, not an input to it.
How this is measured
OpenTelemetry’s AI Recommendation Score (13/100) reflects how widely and often the 9 AI models recommend it for LLM observability tools: share of voice, mention rate and how early the AI models name it. Cited sources are published as receipts, never as a score input. Every monthly refresh asks each AI model the same buyer question once, and the exact run count behind every edition is published in its JSON record. Placement is determined solely by AI recommendation data; it reflects what AI recommends and is not an endorsement by CiteHawk. Read the full methodology →
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Rankings are computed from AI responses only · Positions are not for sale