How Automat-it scales AI startups on AWS

The rapid acceleration of AI-driven startups has transformed cloud infrastructure from a simple scaling exercise into a complex balancing act. Companies now juggle GPU-intensive workloads, rapidly shifting AI models, strict compliance mandates, and ballooning operational costs. For many founders, the real challenge isn’t just launching a product. It is maintaining sustainable cloud operations while scaling fast enough to stay competitive.

Meanwhile, AWS has evolved beyond a basic hosting service. For startups building AI-native products, it now functions as an orchestration hub for everything from deployment pipelines to generative AI governance. According to Ziv Kashtan, CEO of Automat-it, the startups that scale most effectively are those that treat cloud architecture as a strategic asset, not an afterthought.

The Hidden Cost Of Scaling Too Fast

“Early on, we saw that rapidly growing startups often let their cloud spend outpace their revenue,” Kashtan notes. This insight drove Automat-it to embed continuous FinOps optimization into its AWS managed services approach. The company, an AWS Premier Partner specializing in startups, has helped thousands of businesses transition from MVP to production. What began as a DevOps-focused firm has become an AI services company guiding startups through increasingly complex AI workflows on AWS.

Kashtan says one of the biggest misconceptions founders hold is that migrating to AWS alone guarantees efficiency. “Lift and shift is good enough,” he says, describing a common startup mindset. “AWS is like Lego. You can build anything on it. But you can also miss out on all the good stuff easily.”

Another misconception is that managed services are inherently more expensive than building everything in-house. Kashtan argues startups often overlook the hidden costs of maintenance, patching, downtime, and inefficient resource management.

The pain typically surfaces when startups shift from early execution to true scaling. Suddenly, AI inference costs spike, deployments become fragile, and engineering teams spend more time fighting outages than building features. “In practice, this can look like spiraling AI and GPU costs, where startups struggle to maintain sustainable unit economics,” Kashtan explains.

Why DevOps Maturity Matters

One of the most consistent architectural mistakes Automat-it observes is startups delaying operational discipline until later growth stages. Teams often skip multi-account AWS landing zones, rely on manual provisioning through the AWS console, or deploy monolithic systems that are difficult to scale. “We often see teams manually provisioning resources via the AWS Web UI rather than relying on Infrastructure as Code,” Kashtan says.

For high-growth startups, DevOps maturity is directly tied to speed and resilience. Mature CI/CD pipelines, automated testing, and Infrastructure as Code enable faster deployments with less operational friction. Kashtan argues the most effective startups embrace “outcome over output,” outsourcing undifferentiated infrastructure management so internal teams can focus on proprietary innovation. “When DevOps is mature, engineering teams are freed up to focus entirely on their core product,” he says.

That maturity also increasingly applies to AI workloads. Many startups rush to production with impressive AI demos, only to discover that production-grade observability, governance, and cost control are far harder problems to solve.

What A Well-Optimized AWS Environment Looks Like

According to Kashtan, well-optimized startup environments on AWS share common traits. They prioritize Infrastructure as Code from day one using tools like Terraform or AWS CDK. They implement multi-account strategies for security isolation and compliance readiness. They embrace elastic compute environments such as Amazon EKS Auto Mode or Amazon ECS on Fargate to reduce operational burden and optimize costs.

In AI environments specifically, Automat-it advocates for multi-tiered model strategies using Amazon Bedrock, where simpler tasks are routed to lower-cost models while premium models handle advanced reasoning. “Teams make the mistake of using a single, premium LLM for everything,” Kashtan says. “A multi-tiered model strategy dramatically improves efficiency.”

Automation also plays a growing role in reducing operational overhead. Kashtan points to cloud cost management, CI/CD pipelines, compliance evidence collection, and agent orchestration as areas where AWS-native automation can significantly lighten the engineering load.

A Twelvefold Reduction In AI Infrastructure Costs

One example Automat-it highlights is its work with mokSa.ai, a video intelligence startup facing unsustainable infrastructure costs. The company’s original architecture relied on one AI model per dedicated GPU instance, resulting in costs of $353 per camera per month. Automat-it re-architected the platform using Amazon EKS and implemented NVIDIA GPU time-slicing to allow multiple AI models to share virtual GPU resources simultaneously.

“The result was an incredible twelvefold cost reduction, down to just $27 per camera monthly, while keeping inference times well under their required 500ms threshold,” Kashtan says.

The AWS Landscape In 2026

Looking ahead, Kashtan believes AWS will continue evolving into a managed orchestration layer for Agentic AI systems, abstracting away much of the infrastructure complexity startups currently struggle with. “With stringent regulations like the EU AI Act taking effect in August 2026, AWS’s built-in governance and traceability tools will become vital survival mechanisms for high-risk startups,” he says.

For founders building on AWS today, his advice is straightforward: “Focus on your core product and partner for the rest.”