Dotwork vs LangSmith
LangChain's platform for tracing, evaluating and monitoring LLM applications and agents, with an LLM gateway for runtime controls.
LangSmith gives engineering teams traces, evaluations, prompt management and runtime controls for LLM applications, and is the natural home for teams building on LangChain. Dotwork gives finance and leadership a view of what AI costs the enterprise and what it returns, placing AI spend in the same cost model as the rest of technology.
At a glance
LLM observability
LangSmith
Operating model
Dotwork
Usually runs alongside Dotwork
Dotwork connects to it and puts its data in the context of the whole enterprise.
The short version
What each is built for.
LangSmith
LangSmith is the observability and evaluation platform from LangChain. It traces LLM applications and agents with cost, tokens and latency per request, runs evaluations, manages prompts and monitors production. In 2026 it added an LLM gateway in public beta, with spend caps, rate limits, provider fallbacks and PII redaction, and bring-your-own-cloud deployment on AWS for enterprise customers.
- Tracing LLM chains and agents in development and production
- Running evaluations and managing prompts
- Tracking cost, tokens and latency per request, application, user and agent
- Setting spend caps, rate limits and provider fallbacks at runtime
Dotwork
Dotwork is an AI-native enterprise operating platform built on the Enterprise Operating Model: one live, graph-based model that connects strategy, capital, work, technology and AI across the enterprise. For AI cost, it prices AI usage against the provider price in effect and places that spend in the enterprise cost model, connected to the AI assets, owners and initiatives involved.
- Placing AI spend in the same cost model as the rest of technology
- Pricing AI usage against the provider price version in effect
- Connecting AI assets to owners, findings, spend and funded initiatives
- Showing leaders what AI costs the enterprise and what it returns
The core difference
How Dotwork and LangSmith approach the problem.
LangSmith is an engineering platform for building reliable LLM applications. Dotwork is a management model that places AI spend alongside the rest of the enterprise's cost, ownership and strategy.
Context
LangSmith's context is the request and the chain: each step, prompt, model call, cost and latency. Dotwork's context is the AI estate as part of the operating model: which AI assets exist, who owns them, what they cost and which initiatives they serve.
Decisions
LangSmith supports engineering decisions about quality, latency, prompts, routing and per-request cost. Dotwork supports finance and leadership decisions about AI spend, return and accountability.
Time
LangSmith captures each request as it runs, for debugging, evaluation and alerting. Dotwork looks at AI spend by period, priced against the provider price version in effect for that period.
Change
In LangSmith, teams iterate on prompts, evaluations and runtime controls to improve their applications. In Dotwork, AI assets and spend are records that connect to owners, findings and the initiatives that funded them.
Architecture and data model
Side by side.
LangSmith is built to see inside and control each AI request, which is what engineering teams need to ship LLM applications. Dotwork is built to see AI spend in context, connected to the rest of technology cost, the owner of each asset and the initiatives it serves.
| Dimension | LangSmith | Dotwork |
|---|---|---|
| Primary unit | Traces and runs for each LLM request and agent step | AI assets, spend and owners as records in the operating model |
| Cost | Per-request cost and tokens, rolled up by application, user and agent | AI spend by model and provider, placed in the enterprise cost model |
| Pricing | Cost calculated from model prices applied to captured usage | Usage priced against effective-dated provider price versions |
| Quality and prompts | Evaluations, prompt management and automated issue detection on traces | Not an evaluation or prompt management tool |
| Runtime control | Gateway with spend caps, rate limits and provider fallbacks | Budget alerts on AI spend; no provider routing |
| Connection to the business | Engineering observability tied closely to the LangChain framework | AI assets linked to owners, findings, spend and funded initiatives |
The role of AI
Where the AI gets its context.
LangSmith helps teams build and run AI applications well. Dotwork helps the enterprise see what its AI estate costs, who owns it and what it returns. They answer different questions and work side by side.
LangSmith
LangSmith is infrastructure for AI teams. It traces and evaluates LLM applications and agents, and its in-platform agent scans production traces to find issues and propose fixes.
Dotwork
Dotwork's assistant answers questions across the whole operating model, and its agents can create and update Dotwork records with optional human review. An MCP server, enabled per site, gives outside AI tools access to the same graph, including AI spend and ownership.
A fair comparison
Where LangSmith is strong — and where Dotwork is different.
Where LangSmith is strong
- It is the default observability and evaluation platform for teams building on LangChain and LangGraph.
- It offers deep request tracing, with cost, tokens and latency by request, application, user and agent.
- Evaluations, prompt management and automated issue detection help teams improve LLM application quality.
- Its LLM gateway adds spend caps, rate limits, provider fallbacks and PII redaction on the request path.
- Self-hosted and bring-your-own-cloud options keep trace data inside the customer's environment.
Where Dotwork is different
- Places AI spend in the enterprise cost model, next to cloud, labor, vendor and application cost.
- Prices AI usage against the provider price version in effect for each period.
- Connects AI assets to owners, security findings, spend and the initiatives that funded them.
- Sets AI spend beside the value recorded for each initiative, for finance and leadership audiences.
Choosing
Which fits your situation.
Choose LangSmith when
- Your teams build on LangChain or LangGraph
- Engineers need to trace, debug and evaluate LLM applications and agents
- You want spend caps, rate limits and provider fallbacks on the request path
LangSmith fits engineering teams, especially those on LangChain, who need tracing, evaluations, prompt management and runtime controls for LLM applications.
Choose Dotwork when
- Finance needs AI spend in the same cost model as the rest of technology
- Leaders ask what AI costs each initiative and what it returns
- AI assets need clear owners and links to findings and funded work
Dotwork fits finance, technology and AI leaders who need to see what AI costs the enterprise, who owns it and what it returns, in the same model as the rest of technology.
The bottom line
Choose Dotwork when you want to connect the enterprise across systems and silos — with a lower total cost of ownership.
One operating model across strategy, capital, work, technology and AI, instead of another tool to integrate, reconcile and maintain alongside LangSmith.
FAQ
Questions buyers ask.
Does Dotwork replace LangSmith?+
No. Dotwork does not do request tracing, evaluations, prompt management or provider routing at engineering depth. Teams keep LangSmith for those, and Dotwork gives finance and leadership a view of AI spend in the enterprise cost model.
Can Dotwork and LangSmith be used together?+
Yes. LangSmith stays the engineering platform, and AI usage and cost data can be loaded into Dotwork, where it connects to owners, findings, initiatives and the rest of technology cost. The integration is scoped with each customer.
Can Dotwork show what a model price change will affect?+
Partly. Dotwork lists the teams and initiatives using each model, but full impact analysis of a price change is limited today.
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