Generative AI can be a powerful engine of innovation — until your CFO sees the invoice. If you’re tired of being caught off guard by runaway LLM costs, it’s time to discover Dataiku Cost Guard, part of our LLM Guard Services.
Rather than a one-size-fits-all budget, Cost Guard lets you predefine specific limits at the project, user group, or LLM provider level. That means you can fine-tune exactly who or what gets priority, when to trigger alerts, and whether to block queries automatically once you hit your spending threshold. The result? Real transparency, no surprises.
Tired of Surprise LLM Invoices?
LLM providers like OpenAI or Azure may let you set an organization-wide cap, but that alone doesn’t show which project is burning through your budget. A single research and development (R&D) project can inadvertently blow the entire monthly limit, thwarting more critical, revenue-driving applications in the process.
Cost Guard changes the game. Instead of waiting until an invoice lands, an administrator can see the organization’s real-time LLM usage, divide it into meaningful quotas, and decide whether to simply warn stakeholders or cut off access entirely when budgets max out.
Scenario #1: Sandbox Projects Gone Wild
Your data science team has spun up an experimental environment to test a new LLM. They’re making all sorts of queries — some beneficial, others purely exploratory. Without controls, these “just messing around” queries can unexpectedly run up large usage fees.
Here’s where Cost Guard steps in. You create a dedicated sandbox quota, capping it at, for example, $500 a month. If the team approaches that amount, Cost Guard fires off an email alert to the project’s lead, prompting them to investigate or request more budget. If the team actually hits the limit, Cost Guard can automatically block additional LLM calls, preventing further spending.
When that project’s quota is exhausted, Cost Guard blocks further LLM queries for that specific project only. Other teams or applications using the same LLM connection remain unaffected. This way, the sandbox team can experiment freely without threatening more critical initiatives.
Scenario #2: Protecting Your "Crown Jewel" App
On the flip side, you might have a “crown jewel” application that drives business value, such as a live chatbot that answers customer queries. In that case, you definitely don’t want it to shut down just because someone else in the organization depleted a shared budget.
Cost Guard lets you give this flagship project a higher threshold (or no threshold) while still providing crucial visibility. For example, you might set a $10,000 monthly quota and an 80% usage alert. Once usage nears that level, the relevant stakeholders — such as the finance team or the project owner — get a nudge to investigate or increase the limit. Unlike a strict global cap, Cost Guard won’t blindly shut down your biggest revenue source the moment the entire organization’s usage skyrockets elsewhere.
And if your application relies on multiple LLM connections (like OpenAI, Azure, or others), Cost Guard can unify all those queries under a single quota rule. That way, you don’t have to juggle separate budgets or alerts for each provider — the entire project is covered in one place.
Scenario #3: Taming the Provider vs. Project Tug of War
Maybe your biggest challenge isn’t a single project but rather, many teams using the same provider. One group might have a small R&D pilot, while another group runs a massive, business-critical GenAI tool. Without a way to differentiate usage, you’ll never know who’s draining the budget fastest.
Cost Guard solves this by letting you combine quotas with provider-level scopes. You could create quotas based on your main LLM connection (e.g., an OpenAI or Azure connection), slice it again by project or user group, and even define separate alert rules for each. From a single dashboard, you’ll see exactly who’s trending high on usage, which budgets are nearly tapped out, and whether any single project is putting your broader spending in jeopardy.
The Bigger Picture: Dataiku LLM Guard Services
While Cost Guard is all about controlling spending, it’s part of a broader set of Dataiku LLM Guard Services. These include Safe Guard for screening private or sensitive data before it leaves your environment and Quality Guard for ensuring your LLM outputs remain accurate, consistent, and unbiased over time.
Together, these features form a holistic safety net for enterprise GenAI. You decide how to distribute budgets, protect data, and maintain quality — without stifling innovation.
Quick How-To: Setting Up Cost Guard
- Create a Quota: Specify your scope (project, user group, LLM provider) and set a cost limit.
- Choose Alert vs. Block: At what threshold do you simply send an email warning, and when do you entirely block queries?
- Define Reset Cycles: Roll over budgets monthly, quarterly, or however best fits your accounting.
- Stay Informed: Send alerts to the inbox of your choice at the moment usage crosses key thresholds so you can adapt on the fly.
Keep GenAI on Track (While Keeping Your CFO Happy)
Cost Guard is your ally in ensuring GenAI growth doesn’t become a budgetary black hole. Whether you need to rein in a sandbox environment, shield a business-critical application from disruptions, or juggle multiple teams sharing the same LLM provider, Dataiku has you covered.
For teams that already have well-defined GenAI budgeting guidelines, Cost Guard simplifies enforcement by letting you implement those existing rules without building custom scripts or having to triage an avalanche of IT tickets. You can configure quotas, alerts, and blocking rules to mirror your current policies — no need to manually add tags or build custom alerts for each platform. This consolidated approach not only saves time for IT, but also ensures every stakeholder sees consistent, up-to-date information on GenAI spend. The result is a faster path to alignment and clarity around how (and where) your LLM budget gets invested.