Dotwork vs Langfuse
An open-source, self-hostable LLM engineering platform for traces, evaluations, prompt management and monitoring.
Langfuse gives engineering teams deep observability into LLM applications, from request traces and per-request cost to evaluations and prompt management. Dotwork gives finance and leadership a view of what AI costs the enterprise and what it returns, placing AI spend in the same model as the rest of technology cost.
At a glance
LLM observability
Langfuse
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.
Langfuse
Langfuse is an open-source (MIT-licensed) LLM engineering platform, available as a cloud service or self-hosted. It captures traces of LLM calls with per-request cost and tokens, and supports evaluations, prompt management and monitors. It is OpenTelemetry-native and widely integrated.
- Tracing LLM requests and agent steps in production
- Tracking cost and tokens per request, application, user and agent
- Managing prompts and running evaluations
- Self-hosting observability under the team's own control
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, next 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, controls, findings and funded initiatives
- Showing leaders what AI costs the enterprise and what it returns
The core difference
How Dotwork and Langfuse approach the problem.
Langfuse is an engineering tool for building and improving LLM applications. Dotwork is a management model that places AI spend alongside the rest of the enterprise's cost, ownership and strategy.
Context
Langfuse's context is the request: the trace, prompt, model, tokens and cost of each call. Dotwork's context is the AI estate as part of the operating model: which assets exist, who owns them, what they cost and which initiatives they serve.
Decisions
Langfuse supports engineering decisions about quality, latency, prompts and per-request cost. Dotwork supports finance and leadership decisions about AI spend, return and accountability.
Time
Langfuse captures each request as it happens, for debugging and monitoring. Dotwork looks at AI spend by period, priced against the provider price version in effect for that period.
Change
In Langfuse, teams iterate on prompts and evaluations to improve their applications. In Dotwork, AI assets and spend are records that connect to owners, controls and the initiatives that funded them.
Architecture and data model
Side by side.
Langfuse is built to see inside each AI request, which is what engineering teams need to build and improve 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 | Langfuse | Dotwork |
|---|---|---|
| Primary unit | Traces and observations for each LLM request | 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 price definitions applied to captured usage | Usage priced against effective-dated provider price versions |
| Quality and prompts | Evaluations, prompt management and monitors | Not an evaluation or prompt management tool |
| Connection to the business | Engineering observability, with open-source and self-hosted deployment | AI assets linked to owners, controls, findings and funded initiatives |
The role of AI
Where the AI gets its context.
Langfuse helps teams build 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.
Langfuse
Langfuse is infrastructure for AI teams: it traces LLM applications and agents, runs evaluations, manages prompts and monitors quality and cost.
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 Langfuse is strong — and where Dotwork is different.
Where Langfuse is strong
- It is open source under the MIT license, self-hostable, and a common default for teams that want control of their observability data.
- It offers deep request tracing, with per-request cost and tokens by application, user and agent.
- Prompt management, evaluations and monitors help teams improve LLM application quality.
- It is OpenTelemetry-native, with broad framework and SDK integrations.
- It is generally available on both cloud and self-hosted deployments.
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, controls, security findings and funded initiatives.
- Sets AI spend beside the value recorded for each initiative, for finance and leadership audiences.
Choosing
Which fits your situation.
Choose Langfuse when
- Engineers need to trace and debug LLM requests and agents
- You run evaluations and manage prompts as part of development
- You want open-source, self-hosted observability
Langfuse fits engineering teams building LLM applications who need tracing, evaluations, prompt management and the option to self-host.
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, controls and links to 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 Langfuse.
FAQ
Questions buyers ask.
Does Dotwork replace Langfuse?+
No. Dotwork does not do request tracing, evaluations or prompt management at engineering depth. Teams keep Langfuse for those, and Dotwork gives finance and leadership a view of AI spend in the enterprise cost model.
Can Dotwork and Langfuse be used together?+
Yes. Langfuse stays the engineering observability tool, and AI usage and cost data can be loaded into Dotwork, where it connects to owners, controls, 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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