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AI GOVERNANCE WITH CAKE

Bring cost, performance, and risk under control from day one

Most governance tools focus on compliance. Cake starts with cost. Get full visibility into what your AI systems are spending, where that spend is coming from, and how to prevent it from spiraling. At the same time, monitor model quality, data lineage, and risk exposure across your stack.

 

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Overview

AI governance isn’t something to add later. It’s a core part of building production-grade systems. Without it, teams risk runaway costs, unpredictable model behavior, and compliance gaps that are hard to fix after the fact.

Cake gives you the tools to govern your AI systems from the start. That includes deep visibility into infrastructure costs, built-in performance benchmarks, red teaming support, and full transparency into how your models are trained and used. Whether you’re deploying a single model or managing dozens of AI-powered apps, Cake makes it easy to align legal, engineering, and business teams around shared guardrails and goals.

By making cost, performance, and risk fully observable, Cake helps your organization scale AI responsibly while continuing to move fast.

Key Cake benefits

  • Gain full visibility into AI infrastructure costs: Track token usage, model calls, compute resources, and downstream costs at the application level in a unified view.

  • Benchmark model performance at every stage: Use tools like PromptFoo, Deepchecks, and Langfuse to evaluate outputs, monitor drift, and red team your agents.

  • Map lineage and egress risks: Trace fine-tuning datasets, identify where sensitive data is used, and ensure safe model outputs across your stack.

  • Streamline cross-team governance: Give legal, data, engineering, and product teams shared visibility into model versions, datasets, risks, and controls.

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Increase in
MLOps productivity

 

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Annual savings per
LLM project

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Faster model deployment
to production

 

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COST VISIBILITY & CONTROL

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Track and manage spend across your entire AI stack

  • Monitor usage at the project level: Break down costs by application, model, component, and user to understand exactly what each AI workload is consuming.
  • Predict future costs and scale scenarios: See how costs will evolve as you add users, increase traffic, or switch out components, helping you plan ahead with confidence.
  • Set proactive controls to prevent overages: Avoid surprise bills by defining spending thresholds, alerts, and usage-based limits across your AI infrastructure.

MODEL MANAGEMENT & EVALUATION

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Benchmark and monitor performance across models and pipelines

  • Track key metrics out of the box: Cake collects and exposes model metrics automatically using Prometheus and Grafana for real-time visibility into performance.
  • Evaluate quality with curated tools: Use integrated tools like PromptFoo, Deepchecks, Langfuse, and Ragas to test against benchmarks, detect drift, and red team responses.
  • Compare and troubleshoot over time: Track prompt versions, input-output pairs, and model changes to understand how updates affect behavior across use cases.
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LINEAGE & DATA SECURITY

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Ensure transparency around training data, fine-tuning, and egress

  • Trace fine-tuning datasets and model versions: Track which datasets were used in training or fine-tuning, and link them to the resulting models and applications.
  • Monitor data egress and sensitive exposure: Protect your intellectual property.
  • Avoid vendor lock-in and opaque third-party APIs: Use open standards and transparent tooling for long-term flexibility.

CROSS-TEAM GOVERNANCE

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Give legal, data, and engineering teams a unified view of your AI systems

  • Centralize model metadata and status: Keep track of model versions, update history, approval status, benchmarks, and known risks in one place.
  • Streamline reviews and compliance checks: Support internal audits and regulatory reporting with ready access to model lineage, risk logs, and performance records.
  • Support collaboration across disciplines: Bridge the gap between technical and non-technical teams with shared dashboards and tooling to align on objectives and risks.
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THE CAKE DIFFERENCE

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From fragmented oversight to
governance that controls AI costs

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Traditional governance approaches

Fragmented, reactive, and hard to scale: Governance is often bolted on after deployment and spread across teams and tools.

  • No unified view of project-level or per-model costs
  • Model monitoring is manual, inconsistent, or missing entirely
  • No central audit trail for model versions, lineage, or data exposure
  • Requires legal, ops, and engineering teams to manually piece things together

Result:

Hidden risks, runaway costs, and little confidence in AI systems

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AI governance with Cake

Centralized oversight for cost, performance, and risk: Cake gives you the tools to track, evaluate, and govern every component of your AI stack.

  • Track token usage, infra spend, and system-wide costs per project or application
  • Benchmark models for accuracy, toxicity, and drift using tools like Promptfoo, Deepchecks, and Langfuse
  • Monitor egress and data lineage across all components in your stack
  • Unified observability layer with Grafana dashboards and OpenCost integration

Result:

Lower risk, better decisions, and predictable AI spend at scale

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“

"Our partnership with Cake has been a clear strategic choice – we're achieving the impact of two to three technical hires with the equivalent investment of half an FTE."

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Scott Stafford
Chief Enterprise Architect at Ping

Read The Case Study
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“

"With Cake we are conservatively saving at least half a million dollars purely on headcount."

CEO
InsureTech Company

Read the case study
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"Cake powers our complex, highly scaled AI infrastructure. Their platform accelerates our model development and deployment both on-prem and in the cloud"

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Felix Baldauf-Lenschen
CEO and Founder

COMPONENTS

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Tools that power Cake's governance stack

 

Frequently asked questions

What is AI governance and why does it matter?

AI governance refers to the systems and processes that ensure AI models are used responsibly, safely, and efficiently. It covers cost management, performance monitoring, data transparency, and compliance. Without strong governance, organizations risk uncontrolled spend, model drift, data leaks, and regulatory exposure.

How does Cake help control AI infrastructure costs?

Can Cake help us monitor model performance and detect drift?

What kind of data lineage and egress controls does Cake provide?

Who in the organization benefits from AI governance with Cake?

Learn more about Cake

 

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