In the software industry, entrepreneurs have to go through 4 phases of innovation to build a product:

Idea: What change are they trying to make?

Design: How will their users interface with their product and services?

App: Build the core software using LLMs, and other building blocks

Product: Build the distribution engine to distribute their App to rest of the world

For the last mile to go from (Agentic) App to Product, organizations have to build multiple distribution engines. The Control Plane is nothing but an Operating system for those distribution models.

Now in Part 3, we turn to the solution: What if you didn’t have to abandon your existing stack, but could augment it with a purpose-built control plane, one that handled tenant lifecycle, deployment orchestration, and multi-cloud complexity out of the box?

Imagine a data analytics SaaS startup, “DataCo”, that wants to build Redis SaaS as an example. They use Argo CD to deploy their analytics application (a set of microservices + some Spark jobs) onto the EKS cluster.

Modern platform engineering teams often assemble “DIY” control planes for their Products using open-source building blocks: Kubernetes for the runtime, Terraform for infrastructure provisioning, and Argo CD for continuous deployment. This stack promises cloud-agnostic flexibility and full control. But as many CTOs and platform VPs have learned, stitching these tools together into a robust control plane is hard.

🚀 Exciting New Features in Omnistrate

🚀 Exciting New Features in Omnistrate

Over the past few weeks, I've been leading an initiative to integrate the Serverless Framework with our Omnistrate platform. Today, I'm excited to share the technical details of this integration and how it can help developers maintain their serverless applications while gaining enterprise SaaS capabilities.

Many B2B startups start their cloud journey with AWS ECS or GCP Cloud Run. It's understandable—they're familiar, quick to set up, and tightly integrated with the hyperscalers' ecosystems. Throw in some Terraform, and you have a hello-world deployment workflow that seems "good enough" to get your SaaS up and running.

But "good enough" quickly breaks down as you scale.

🚀 Exciting New Features in Omnistrate

In previous posts, we explored BYOA deployments, AI job scheduling, and cost control and insights. In this blog, we focus on the performance accelerators and scalability layers Omnistrate offers out of the box—purpose-built for AI workloads that demand fast startup, efficient resource usage, and intelligent scaling.

Let’s dive into four features that can supercharge your AI deployment strategy.