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AI GOVERNANCE WITH CAKE

Enforce budgets. Manage access. Stay in control.

AI costs span models, apps, and infrastructure, and they can scale quickly. Governance isn’t just about risk or compliance; it’s about staying in control of spend and access from the start. Cake provides visibility into every level of your AI stack so you can set the guardrails to control spend and scale responsibly.

 

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Overview

As AI systems expand, costs can spiral and access can become difficult to manage. Traditional governance tools focus on risk and compliance after the fact, but Cake helps you stay in control from the start. You can define who has access to specific projects, apps, and environments, and apply clear, enforceable budgets to each one.

Budgets can be set at the project level, monitored in real time, and automatically enforced to avoid overruns. Teams only get access to what they need, and every application has guardrails in place to prevent runaway spend.

At the same time, Cake gives you the flexibility to scale with confidence. Whether you are launching a new GenAI app or managing dozens of internal AI tools, you get the visibility and control to grow responsibly without introducing risk or losing agility.

Key Cake benefits

  • Set and enforce project-level budgets: Apply cost limits to individual projects to prevent overages and align spend with business goals.

  • Control access by team, project, and application: Define who can deploy models, access data, or make infrastructure changes at each layer of your stack.

  • Track spend across all AI workloads: Get real-time visibility into which teams, apps, or users are driving costs and where optimizations are needed.

  • Manage quotas and usage across environments: Apply rate limits or resource caps to specific apps or environments to prevent uncontrolled usage.

  • Extend governance across performance, risk, and compliance: Monitor model outputs, track lineage, and maintain a shared source of truth for legal, engineering, and product teams.

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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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BUDGET ENFORCEMENT AT EVERY LAYER

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Govern spend across teams, projects, and applications

  • Project-level budgets: Set and enforce spending caps that apply across all apps and workloads within a given project.
  • App-specific cost controls: Define usage limits for individual apps to prevent cost overruns in production.
  • Real-time tracking and alerts: Monitor spend as it happens and receive alerts when teams approach defined thresholds.

ROLE-BASED ACCESS FOR SAFER SCALING

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Manage permissions with precision and clarity

  • Scoped access by project or app: Control who can view, edit, or deploy workloads at every level of your AI stack.
  • Separation of duties: Limit infrastructure changes to specific roles and prevent unauthorized cost-driving actions.
  • Environment-specific permissions: Apply stricter access rules in production while keeping sandbox access flexible.
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MORE THAN JUST COST AND ACCESS

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Extend governance to performance and compliance

  • Integrated evaluation tools: Use Promptfoo, Langfuse, Deepchecks, and Ragas to benchmark model quality and detect drift.
  • Model and infrastructure monitoring: Track usage, latency, and system health through Prometheus and Grafana.
  • Lineage and audit readiness: Trace datasets, fine-tuning events, and model versions for faster audits and internal reviews.

THE CAKE DIFFERENCE

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From scattered oversight to clear
budget and access control

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Traditional governance

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

  • Budgets are managed manually or not enforced
  • Access is broad, static, or hard to audit
  • Model performance is hard to assess, with no shared benchmarks across teams
  • AI costs are difficult to attribute or forecast
  • Compliance reviews are time-consuming and siloed
  • Teams lack a shared source of truth across model governance

Result:

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

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AI governance with Cake

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

  • Enforce real-time budgets across teams, projects, and apps
  • Scope and audit access across environments
  • Evaluate quality using Promptfoo, Langfuse, Deepchecks, and Ragas with benchmarks and version control
  • See usage, spend, and forecasted growth per app
  • Review audits quickly with shared dashboards
  • Unify legal, data, and engineering in one view

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

 

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Deepchecks

Model Evaluation Tools

Automate data validation and monitor ML pipeline quality with Deepchecks, fully managed and integrated by Cake.
ragas

Ragas

Model Evaluation Tools

Rapidly evaluate RAG pipelines and optimize LLM performance with Cake’s streamlined Ragas orchestration.
Grafana

Grafana

Cloud Compute & Storage

Grafana is an open-source analytics and monitoring platform used for observability and dashboarding. Cake integrates Grafana into AI pipelines to visualize model performance, infrastructure metrics, and system health.
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OpenCost

Observability & Monitoring

OpenCost is an open-source cost monitoring tool for Kubernetes environments. Cake integrates OpenCost to track, optimize, and govern AI infrastructure spending across cloud-native deployments.
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Prometheus

Observability & Monitoring

Prometheus is an open-source systems monitoring and alerting toolkit. Cake integrates Prometheus into AI pipelines for real-time monitoring, metric collection, and governance of AI infrastructure.
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Promptfoo

LLM Observability

LLM Optimization

Promptfoo is an open-source testing and evaluation framework for prompts and LLM apps, helping teams benchmark, compare, and improve outputs.
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Langfuse

LLM Observability

Langfuse is an open-source observability and analytics platform for LLM apps, capturing traces, user feedback, and performance metrics.
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LiteLLM

Observability & Monitoring

Inference Servers

Data Catalogs & Lineage

LiteLLM provides a lightweight wrapper for calling OpenAI, Anthropic, Mistral, and other LLMs through a common interface. It supports token metering, caching, and governance tools.

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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 governance and Cake

 

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