Discover how platform engineering teams are leveraging Kubernetes to deploy and scale Gen AI models efficiently while maintaining production-grade reliability and performance.
Kubernetes: The Gift That Keeps on Giving
When we started building Ingenimax, our mission was clear: empower data science and AI teams to ship, scale, and maintain models in their own cloud environments in minutes - not months.
Why Kubernetes? Why Now?
Control
Full ownership of your infrastructure without vendor lock-in
Composability
Mix and match components to build your ideal AI stack
Clarity
Complete visibility into what's running, where, and why
Kubernetes gives you more than orchestration - it gives you control, composability, and clarity. And when paired with the right tooling, it unlocks exactly what enterprise AI needs: flexibility without vendor lock-in, visibility without compromise, and runtime environments you can actually trust.
KServe: The Quiet Hero
- •Multi-framework support
Think: PyTorch, TensorFlow, XGBoost, and beyond
- •Custom serving runtimes
Bring your own container, GPU support included
- •Autoscaling on demand
With Knative under the hood, along with other tools to facilitate model monitoring
- •Traffic splitting
And canary rollouts, production-grade
One of the quiet heroes in our architecture is KServe, the open-source standard for serverless model inference on Kubernetes. It powers our model serving layer and gives our customers all the building blocks to provide a production-first platform for AI teams without reinventing the wheel - or betting on the wrong one.
The Managed AI Problem
Over the past quarter, we've been hearing and seeing the same thing from startups and enterprises alike: performance and reliability on major managed AI platforms is getting shaky - just when revenue depends on it.
Creeping Inference Latencies
Response times on managed platforms are steadily increasing, affecting user experience and application performance.
Costly Cold Starts
Cold starts aren't just annoying - they're costing real dollars in both direct expenses and lost customer opportunities.
Endpoint Downtime
Entire endpoints going down for minutes at a time is unacceptable when running live production workloads that demand reliability.
Black-Box Queuing
Paying top dollar for "managed inference" but getting opaque throttling and zero accountability - a risk we weren't willing to take.
Our Bet: AI You Can Actually Own
Your cloud
AWS, GCP, Azure, or on-prem
Faster performance
2-5x faster inference latencies
Full transparency
See what's running and why
No vendor lock-in
Freedom to adapt and evolve
With Kubernetes and KServe, we help teams deploy and manage model inference across their own infrastructure—whether that's on AWS, GCP, Azure, or on-prem.
And here's the thing: we're seeing inference latencies 2–5x faster than comparable managed AI endpoints, with full transparency into what's running, where, and why. No hidden limits. No surprise costs. No vendor handcuffs.
In 2025, owning your AI infrastructure isn't a burden - it's an edge.
The Kubernetes Advantage
Dependable Ally
Kubernetes may not be the shiny new toy anymore, but it's still the most dependable ally for teams building real, revenue-critical AI systems.
Production-Grade
Thanks to projects like KServe, it's never been easier to run a performant, production-grade model serving solution - on your terms.
Cloud-Native Principles
At Ingenimax, we're betting on composability, control, and cloud-native principles - not hype.
Because when uptime, latency, and flexibility matter… Kubernetes really is the gift that keeps on giving.
Need a little help with KServe? That's exactly why StarOps exists.