Practical GenAI Governance: Access and Cost control at scale
In this article, we’ll delve into two critical aspects I rarely see sufficiently considered during the development of GenAI applications and broader GenAI Platforms: Access Control and Cost Monitoring. Recently, my LinkedIn and Reddit feeds have shown me many people lamenting unexpected bills due to missing or ineffective LLM quota limits, problems with data exposure via their agents, and a lack of visibility over who is using what model and why.
First, which use case are you running?
- Non-Business-Critical / Privacy-First Use Cases: Do yourself a favor, consider using something like Open Router. Buy some credits, create an API Key, and use that. You’ll only spend the amount you pre-paid. I heavily use this approach for experimenting, comparing models, and prototyping. If you are interested in controlling the datasource your agent can access and want to add a governance to your solution then you are in the right place, otherwise you should already be fine with Open Router.
- Enterprise use case: You need something more structured, secure, and scalable. This article is for you!
Let’s explore how an architecture like the one illustrated below, can bring order to the chaos.
Access LLMs through a Gateway
At enterprise scale all interactions with the various LLMs (Claude, Gemini, OpenAI, Hugging Face, etc.) should funnel through a central LLM Gateway. Why?
- Single Point of Integration: Instead of each GenAI application integrating directly (and differently) with each LLM provider, they all talk to the gateway. This simplifies development and maintenance significantly. Adding a new LLM or updating an existing one happens in one place.
- Centralized Monitoring & Logging: Every request, every response, every token flows through the gateway. This allows for comprehensive logging and monitoring. You gain immediate visibility into which applications are calling which models, the frequency, latency, and potential errors. Moreover, this will be helpful later when we will talk about FinOps
- Caching: Many requests to LLMs are repetitive. An intelligent gateway can cache responses to identical prompts, drastically reducing redundant calls to the underlying LLMs and saving significant costs.
- Guardrails & Prompt Evaluation: Before a prompt even reaches an LLM, the gateway can enforce guardrails. This includes checking for PII, toxic language, prompt injection attempts, or ensuring prompts align with company policy. It acts as a crucial security layer, and you don’t have to worry about inconsistencies among the GenAI applications, you will define guardrails once.
- Quota Management: The gateway is the ideal place to enforce usage quotas, budget and rate limits before hitting the LLM provider’s limits (or your budget limits). This prevents runaway costs from a misconfigured application or unexpected usage spike. When a budget is nearing exhaustion, alerts can be triggered, or access can be automatically throttled or blocked, preventing bill shock.
- Standardized KPIs: By centralizing requests, you can easily define and track Key Performance Indicators (KPIs) like cost-per-request, tokens-per-session, or model usage distribution across the organization.
Apply Access Control List Policies to your GenAI Applications
Simply having a gateway isn’t enough. You need granular control over which applications can use it and how. Moreover, if you have MCP servers over the place, and you implemented an MCP Mesh as proposed in my article (Kudos to you), this is where Access Control Lists (ACLs) come in.
Not every application needs access to every LLM and every datasource. A customer service bot might only need access to a fine-tuned internal model and internal manuals, while a research tool might require access to powerful frontier models and some sensitive documents. ACL Policies, ensure that each application can only access models and data explicitly permitted for its function. This enhances security and prevents accidental usage of expensive models or exposition of sensitive data. Moreover, use ACLs to differentiate between development, staging, and production environments. Dev environments might have access to cheaper models or stricter quotas, while production applications have the necessary resources allocated.
Embrace FinOps
Visibility and control are great, but how do you translate that raw data into actionable financial insights? This is where FinOps principles and standardized data models like FOCUS (FinOps Open Cost and Usage Specification) become essential. FOCUS provides a common language for cloud and SaaS costs, enabling consistent analysis across different services, why not adding also GenAI costs? If you treat LLM costs just like any other cloud resource, you can enable mature financial management and optimization across all the GenAI Application pool.
The LLM Gateway is perfectly positioned to generate detailed, standardized usage data for every request. This data should include identifiers for the application, user (if applicable), model used, tokens consumed (input and output), timestamps, and calculated cost. Transforming this raw data into the FOCUS format allows it to be easily ingested by standard FinOps platforms and tools.
Once in a FinOps tool, you can:
- Allocate Costs: Accurately charge back GenAI costs to the specific departments or projects using them.
- Identify Optimization Opportunities: Spot expensive or inefficient usage patterns. Is one application using a costly model when a cheaper one would suffice? Is caching underutilized?
- Forecast Spending: Predict future GenAI costs based on current trends.
- Track ROI: Correlate GenAI spending with business outcomes to understand the value generated.
Conclusion
Working with GenAI brings incredible opportunities, but uncontrolled adoption can lead to spiraling costs and security risks. By architecting your GenAI platform with a central LLM Gateway, enforcing granular ACLs and budgets per application, and integrating usage data into a robust FinOps practice using the FOCUS standard, you can innovate responsibly and scale effectively. You gain the control, visibility, and financial accountability needed to harness the power of GenAI without breaking the budget.
What are your thoughts? Are you implementing similar controls? I’d love to hear about your experiences and challenges. Feel free to reach out!
