Why 90% of Agentic RAG Projects Fail (and How Cake Changes That)
TL;DR Most RAG projects stall at 60%: Quick demos are easy to build, but brittle architectures break down before reaching production. The last 40%...
How to Build Scalable GenAI Infrastructure in 48 Hours (Yes, Hours)
Learn how Cake helped a team scale GenAI infrastructure in just 48 hours. Secure, observable, and composable, no glue code or rewrites required....
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How to Build an Enterprise AI Stack (That Doesn’t Break at Scale)
Enterprises are under more pressure than ever to incorporate AI into their workflows. But most are stuck stitching together a stack that was never...
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The 5 Paths to Enterprise AI (And Why Most Lead to Trouble)
Every AI team eventually hits the same familiar wall: their prototype works great, but production is a nightmare. This is the inevitable disconnect...
The High Cost of Sticking with Closed AI
Around the world and across industries, enterprise AI teams routinely face a decision: stick with proprietary platforms or embrace the open source...
Top 10 Vector Databases: Choosing the Right One for Your Project
Key takeaways Vector databases are essential infrastructure for modern AI: They power search, memory, and retrieval across RAG, agentic, and...
Why Cake Beats AWS SageMaker for Enterprise AI
Enterprise AI teams are stuck with a tough choice: spend months or years building everything from scratch, rely on cloud vendor platforms like AWS...
The Future of AI Ops: Exploring the Cake Platform Architecture
Cake is an end-to-end environment for managing the entire AI lifecycle, from data engineering and model training, all the way to inference and...
