In 2009, Heroku changed everything with a single command. git push heroku main. That was it. Your app was live. No servers to configure. No networking to understand. No ops team required. Developers loved it because it let them do what they actually wanted to do — build products, not manage infrastructure.

For a brief moment, it felt like the future had arrived. Then AWS won.

Amazon offered something Heroku couldn't — real power. You could build anything, scale to any size, and customise every detail. But that power came with a price. VPCs with CIDR allocations. Subnets spanning availability zones. Security groups with ingress and egress rules referencing other security groups. IAM policies with resource-level permissions and trust relationships. Application load balancers with listener rules and target group bindings. Auto-scaling policies triggered by CloudWatch metric thresholds. ECS task definitions with container port mappings and service discovery configs. RDS parameter groups. ElastiCache subnet groups. Terraform state backends with locking mechanisms. The list kept growing.

The industry made a deal with the devil. We traded simplicity for control, and we've been paying for it ever since.

Here's what that deal actually costs.

A three-person startup spends its first two weeks not building its product, but fighting with AWS. They're debugging why their ECS tasks won't register with the load balancer. They're figuring out why their RDS instance isn't reachable from their application subnet. They're learning that their NAT gateway costs $30/day to exist. By the time they deploy, they've lost weeks they didn't have, and they've built infrastructure held together by Stack Overflow answers and Terraform modules they don't fully understand.

A Series A company decides they can't keep doing this. They hire a DevOps engineer. That's $150K / ₹20-40 LPA or more per year — for one person whose job is managing cloud resource lifecycle, writing IaC modules, debugging provider version conflicts, and building deployment pipelines. For a startup trying to find product-market fit, that's not a hire. That's half the runway, gone.

A growth-stage company has accumulated two years of infrastructure decisions made by engineers who are no longer there. Terraform state has drifted from reality. Modules reference deprecated provider APIs. The dependency graph has circular references that only work by accident. No one fully understands how the system works. Everyone's afraid to touch it. Technical debt compounds silently until something breaks in production at 3 am.

This is the reality for almost every software company. 64% of companies say they don't have the infrastructure skills they need. The average cloud misconfiguration costs $4.88 million. These aren't edge cases. This is the default experience of building software in 2025.

And the tooling hasn't changed. Terraform was released in 2014. Eleven years later, engineers are still writing HCL, still managing state locks, still debugging dependency cycles, still copy-pasting modules from public registries and hoping they work. The same workflows. The same failure modes. For over a decade.

Every other part of software development has moved up the abstraction curve. We don't write assembly anymore. We don't manage our own socket connections. We don't hand-roll authentication flows. But infrastructure provisioning? Still operating at the same level of indirection as it was ten years ago. Still requiring specialised knowledge that most developers don't have and don't want to acquire.

We started asking a simple question. What if any developer could provision production infrastructure without becoming an infrastructure expert?

Not by hiding complexity behind a managed platform that owns your resources. Not by sacrificing control for convenience. What if we could preserve full ownership — your cloud account, your resources, your Terraform state — while eliminating the expertise barrier?

This wasn't possible until recently. The reason is technical.

The infrastructure code has zero tolerance for error. When an LLM generates application code and hallucinates a function that doesn't exist, you get a build error. When it hallucinates an infrastructure resource reference, you get a security group that exposes port 5432 to the internet. Or a subnet route that blackholes your traffic. Or a circular dependency that fails silently during Terraform apply. The blast radius is production. The tolerance for probabilistic output is zero.

So we built a different kind of system.

A developer describes what they want in natural language. Our LLM parses that intent into a structured intermediate representation—a directed acyclic graph of resources, dependencies, and constraints. This graph passes through a constraint validation engine before compiling down to infrastructure code.

The AI understands intent. The compiler guarantees correctness.

Here's what it looks like in practice.

A developer opens Insor and types: "I need a Node.js backend with PostgreSQL and Redis, behind a load balancer."