OpenTelemetry vs LangSmith for LLM apps

Here’s the practical difference:

Short version

• OpenTelemetry (OTel) = general-purpose observability standard

  • Best for distributed tracing, metrics, logs across any app, service, or vendor.
  • Great when you want portability and a single observability layer.

• LangSmith = LLM/app-specific observability and evaluation platform

  • Built specifically for debugging, tracing, prompt inspection, datasets, evals, and LLM workflow analysis.
  • Best when your main problem is understanding and improving LLM application behavior.

What each is best at

OpenTelemetry

Use it if you want:

• Vendor-neutral tracing/metrics/logging
• End-to-end visibility across microservices, queues, APIs, databases
• Integration with existing observability stacks like Jaeger, Grafana Tempo, Datadog, Honeycomb, etc.
• A standard way to instrument your app regardless of language/framework

For LLM apps, OTel can capture:

• Requests, spans, tool calls
• Latency, errors, token usage as custom attributes
• Correlation across your app and infra

But out of the box, it usually doesn’t give you:

• Prompt/version management
• LLM-specific evals
• Dataset-based regression testing
• Rich prompt/response inspection tuned for LLM workflows

LangSmith

Use it if you want:

• Tracing specifically for chains, agents, tools, prompts
• Deep visibility into LLM inputs/outputs
• Experiment tracking and evaluations
• Dataset curation and regression tests
• Faster debugging of prompt issues and agent behavior

LangSmith is especially useful for:

• Prompt engineering
• RAG debugging
• Agent tool-call debugging
• Comparing model/prompt versions
• Human/automatic evaluation workflows

But it’s more specialized:

• Less of a general infra observability solution
• More tied to LangChain/LangGraph ecosystem, though it can be used more broadly
• Not a replacement for your standard production observability stack

When to choose which

Choose OpenTelemetry if:

• You already use Datadog/Grafana/New Relic/Honeycomb/etc.
• You need one standard across backend services and LLM components
• Your main concern is production observability, reliability, and latency
• You want vendor independence

Choose LangSmith if:

• You’re actively building and iterating on LLM apps
• You need to understand why a prompt/chain/agent behaved badly
• You want evaluations, datasets, and prompt experiment tracking
• You’re using LangChain/LangGraph heavily

Best answer in practice: use both

A common pattern is:

• OpenTelemetry for system-wide observability
• LangSmith for LLM-specific tracing and evaluations

That gives you:

• Infra-level monitoring and alerting from OTel
• LLM-specific debugging and quality workflows from LangSmith

Rule of thumb

• If your question is “Is my app healthy?” → OpenTelemetry
• If your question is “Why did my LLM behave like this, and how do I improve it?” → LangSmith

If you want, I can also give you:

1. a feature-by-feature comparison table, or
2. a recommended stack for a production LLM app.