This is the fifth in a series of blog posts about How to build with GenAI- From strategy to implementation. In this series, we will explore the following questions:
- Is GenAI the right strategy for your product roadmap?
- Should you build or buy your GenAI model?
- How do you navigate the complexity of data to deliver clear results?
- Should you take a Human-in-the-loop approach?
- How do you manage costs while developing with GenAI?
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In our last post, we explored how human-AI collaboration creates smarter and more trustworthy systems. But as you scale those systems, another challenge emerges: balancing innovation with the practical realities of cost management and compliance.
As Jim Meyer, our VP of Engineering, puts it: “It’s easy to get excited about what AI can do—but without careful planning, you can end up with a surprise bill or compliance headaches you weren’t ready for.”
At Yotascale, we’ve faced these challenges head-on. Here’s what we’ve learned about scaling Gen AI responsibly while keeping costs under control and compliance at the forefront.
Why Cost Management Matters in AI
When you’re experimenting with Gen AI, costs can feel like an afterthought. But as you move into production, even seemingly small factors—like token usage or API call volume—can add up quickly.
Jeff Harris, our Director of Strategy and Operations, uses an iceberg analogy to explain it: “The prompt you write—the question you ask the AI—is just the tip of the iceberg. Below the surface, there’s a lot more happening. System messages, chat history, context injections, and API calls—all of these contribute to token usage, and they can multiply your costs without you even realizing it.”
At Yotascale, we’ve found that monitoring costs early is key to scaling responsibly. Simple steps like optimizing prompts, choosing cost-efficient models, and tracking token usage in real time can make a big difference.
Pro Tip: Tools like Tiktokenizer can help you break down token usage and identify opportunities for efficiency.
Building Compliance into Your AI Architecture
Cost isn’t the only consideration when scaling Gen AI. For enterprise-grade systems, compliance with data privacy and security standards is critical—not just for legal reasons but to build trust with your users.
Jeff explains Yotascale’s approach: “We designed our AI so that customer data stays within our system. The large language model (LLM) acts only as an interface, creating structured API calls based on user inputs. This ensures that sensitive cloud cost data never leaves our secure environment.”
This architecture eliminates many common compliance risks while maintaining the flexibility to leverage Gen AI’s capabilities. It’s an approach that prioritizes security by design, making compliance an integral part of the system rather than an afterthought.
Key Lessons for Responsible AI Scaling
Here are three lessons we’ve learned from scaling Gen AI responsibly:
- Understand Cost Drivers: Costs can balloon quickly if left unchecked. Monitor token usage, optimize your prompts, and choose the right models for your use case.
- Build Compliance into the Foundation: Ensure your architecture keeps sensitive data secure and prevents unintended data sharing.
- Iterate Responsibly: Balancing innovation with practical constraints is essential for scaling sustainably. Experiment, learn, and refine as you grow.
As Jim puts it: “Scaling AI isn’t just about adding features—it’s about doing it in a way that makes sense for your users, your business, and your bottom line.”
Looking Ahead
Scaling AI responsibly requires balancing costs, compliance, and user trust. In our next post, we’ll explore how to optimize AI for your business needs while keeping the human element at the forefront.
