CASE STUDY Grace AI

• $250,000 in Azure credits secured in under 2 weeks, which helped to extend infrastructure runway
• Deployment of AI agents enabled at a production scale
• Budget freed up for product development and team growth

Grace AI is an enterprise-focused agentic AI platform helping organizations automate complex workflows and deploy intelligent AI agents at scale.

As a fast-growing startup building compute-intensive AI products, cloud infrastructure quickly became one of the company’s largest operating expenses.

Here’s why they reached out to Spendbase:

• Azure costs grew rapidly, driven by AI model training, optimization, and deployment.
• Couldn’t easily access the cloud funding programs and credits available to larger, more established companies.
• Had to balance experimentation and innovation against infrastructure budget constraints.
• Cloud costs were shortening runway that could otherwise fund product development and hiring.
• Needed a more sustainable way to support production-scale AI workloads without increasing operational costs.

“Honestly, the biggest challenge was cost. As an early-stage AI company, the work we do optimizing models and deploying agents is compute-heavy and expensive, and those cloud bills were eating into the runway we’d rather put toward product and team. We also didn’t have easy access to the kind of credits and discounts bigger players get, so every experiment came with a cost tradeoff that slowed us down. That’s exactly what working with you solved.”

— Divine Matthew, CEO at Grace AI

Day 1–3: Discovery call + eligibility assessment

We started with several conversations to better understand Grace AI’s business model, growth plans, AI infrastructure requirements, and Azure environment.

Our team reviewed the company’s current cloud usage, upcoming product roadmap, and eligibility for available Microsoft startup funding programs.

This initial assessment allowed us to determine the strongest application strategy and identify the opportunities most likely to maximize available cloud credits.

1st week: Building and submitting the Azure credits application

Once eligibility was confirmed, we worked closely with the Grace AI team to gather the required information and prepare a strong application.

We helped position the company’s AI platform, growth trajectory, and infrastructure requirements in a way that clearly demonstrated the value Azure credits would bring to the business.

After finalizing the application materials, Spendbase took ownership of the submission process. We coordinated documentation, ensured all requirements were met, and managed communications related to the application.

This allowed Grace AI’s team to remain focused on building and improving their platform while we handled the operational side of the process.

As a result, Grace AI secured $250,000 in Azure credits, significantly extended its infrastructure runway and created more flexibility to invest in product innovation and business growth. 

If your startup is spending heavily on cloud infrastructure, AI workloads, or model development, Spendbase can help you identify available funding programs and secure cloud credits.

Talk to one of our experts to check your eligibility and find a custom solution for your business.