# OpenLIT vs LangSmith

> LangSmith is LangChain's proprietary observability platform. OpenLIT is framework-agnostic, fully open-source (Apache 2.0), and works with any LLM provider or orchestration framework.

- HTML: https://openlit.io/compare/openlit-vs-langsmith
- Markdown: https://openlit.io/compare/openlit-vs-langsmith.md

Compare OpenLIT and LangSmith for LLM monitoring. OpenLIT is open-source, self-hostable, and framework-agnostic. LangSmith is a proprietary cloud platform optimised for LangChain.

## Feature comparison

### Core Architecture

| Feature | OpenLIT | LangSmith |
| --- | --- | --- |
| OpenTelemetry-native | Yes | No |
| Open Source | Apache 2.0 | No |
| Self-hostable | Yes | Enterprise plan only |
| Framework-agnostic | Yes | Best with LangChain; supports others via SDK |
| Free self-hosted tier | Yes | No |

### LLM Monitoring

| Feature | OpenLIT | LangSmith |
| --- | --- | --- |
| Token usage tracking | Yes | Yes |
| Cost per request | Yes | Yes |
| Latency / p95 metrics | Yes | Yes |
| Prompt & response logging | Yes | Yes |
| 60+ integrations (LLMs, frameworks, VectorDBs, GPUs) | Yes | Primarily via LangChain wrappers |

### Infrastructure Monitoring

| Feature | OpenLIT | LangSmith |
| --- | --- | --- |
| GPU monitoring (NVIDIA + AMD) | Yes | No |
| Vector DB tracing | Yes | No |
| Multi-environment tagging | Yes | Yes |
| Organisation management | Yes | Yes |

### Developer Tools

| Feature | OpenLIT | LangSmith |
| --- | --- | --- |
| Prompt Hub (versioning) | Yes | Yes |
| Evaluations | Yes | Yes |
| Secrets Vault | Yes | No |
| Fleet Hub (multi-deployment) | Yes | No |
| Custom model pricing | Yes | No |
| Prompt playground | No | Yes |
| Dataset management for evals | No | Yes |

### Pricing

| Feature | OpenLIT | LangSmith |
| --- | --- | --- |
| Free tier | Unlimited traces (self-hosted) | 5,000 traces/month |
| No credit card to start | Yes | No |
| Self-hosted free tier | Yes | No |
| Data ownership | Full (self-hosted) | LangChain cloud |

## When to choose OpenLIT

- You are not using LangChain and want framework-agnostic, auto-instrumented observability
- You need to self-host for data privacy, compliance, or cost control without an Enterprise plan
- You need GPU monitoring for locally-hosted models on NVIDIA or AMD hardware
- You want unlimited traces on self-hosted infrastructure at zero licensing cost
- You require OpenTelemetry compatibility to route data to Grafana, Datadog, or other backends

## When to choose LangSmith

- Your stack is deeply integrated with LangChain and you want first-class toolchain support
- You need LangSmith's prompt playground and dataset management for systematic eval workflows
- You are comfortable with cloud-only and want a managed service without any ops overhead

## Related

- All comparisons: https://openlit.io/compare.md
- Pricing: https://openlit.io/pricing.md
- Docs: https://docs.openlit.io/latest/openlit/quickstart-ai-observability