Removing product as a bottleneck:
The conversation around PaaS is urgent again, and AI is why. Code generation can speed up your development cycles, building and pushing features faster, but production delays will persist if you’re still deploying at the same speed as before. To avoid eroding the benefits of code generation, you need to deploy applications nearly as fast as they can be coded with AI
Better, sustainable security:
Security works the same way. When your team makes the decision to pull from open-source components and stitch them together, you now own every security gap between them – data at rest, data in flight, and its running state. This seems like a worthwhile investment at first, but as recent research suggests, AI-assisted attacks are on the rise, and platform teams won’t be able to keep up with surging security vulnerabilities in the build-it-yourself model.
Getting fast access to new technologies you can use to build your competitive advantage:
The pace of AI innovation itself compounds the problem. Whether it’s shadow AI use, MCP servers, agentic harnesses, this week’s new foundation models, or whatever emerges next, the landscape is evolving fast enough that what’s bleeding-edge today may be table stakes in six months. When you build your own platform, you’re on the hook to evaluate each layer of these new technologies, determine how they fit into your stack, and then integrate them yourself, on top of everything else your platform team is already managing to keep the business going.
A proven, pre-integrated platform stack looks real nice when you need to deliver instead of tinker around with the same thing all your competitors have. Why not TryTanzu.ai?
Original 🔗: The DIY platform trap that’s burning out engineering teams