Category Deep Dive · Lifecycle Cloud vs Hyperscalers

Software Lifecycle Cloud vs Hyperscalers (AWS, GCP, Azure)

Hyperscalers evolved from virtual machine primitives and expose 200+ loosely coupled services. KERNSTACK is one control plane for apps, data, AI, and the people and agents who build them.

Functions (WASM)Container Workers NoSQL Tables Object Buckets Full-Text Search Model Inference Edge Gateway

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From 14 loosely coupled AWS services to one cohesive fabric

To ship a standard AI-enabled web product on AWS, an engineering team must configure and secure API Gateway, Lambda, Fargate/ECS, DynamoDB, OpenSearch, Bedrock/SageMaker, S3, CloudFront, Step Functions, EventBridge, IAM, Secrets Manager, CloudWatch, and Route 53. Each service has its own quota model, IAM policy syntax, SDK client, and pricing trap (such as NAT Gateway fees or idle OpenSearch clusters).

KERNSTACK replaces that entire surface with a single control plane engineered from scratch. Compute, tables, buckets, full-text and vector search, inference, workflows, and analytics are native citizens of the same runtime.

Replace 2,000+ lines of Terraform/CloudFormation with a 40-line stack.json
No VPC subnetting, NAT gateway surcharges, or idle OpenSearch cluster minimums
Full European digital sovereignty on hardware operated by goto coding.berlin GmbH

Why hyperscaler IAM is incompatible with autonomous coding agents

AWS IAM was designed for static service accounts and human administrators, not coding agents that dynamically add a table or a search index on a feature branch. On KERNSTACK, agents operate inside an isolated project sandbox with an explicit credit ceiling and branch-scoped preview environment — never holding root infrastructure credentials.

Transparent three-primitive metering

Instead of deciphering 80-page hyperscaler invoices with per-service request surcharges, KERNSTACK maps all duration-metered work to compute (0.01 credits/GiB-s), storage (0.04 credits/GB-hour), and network egress (9.00 credits/GiB) on one shared balance.

Request & Data Flow on the KERNSTACK Fabric

AWS Collage14 Services + IAM + VPCThousands of lines of Terraform

KERNSTACK1 Control Plane · 1 stack.jsonScoped identity automatic

SovereigntyEU Bare MetalNo US CLOUD Act exposure

stack.json — declarative application & infrastructure graph plan · deploy

{
  "stack": { "target": "core-service.prod" },
  "gateway": {
    "routes": [
      { "name": "api", "type": "http", "target": { "kind": "function", "name": "handler" } }
    ]
  },
  "resources": {
    "functions": [{ "name": "handler" }],
    "tables": [{ "name": "events", "key_name": "id", "key_type": "string" }],
    "buckets": [{ "name": "archive" }],
    "search_indexes": [{ "name": "events-search", "sources": [{ "type": "tables", "name": "events", "fields": ["payload"] }] }]
  }
}

FAQ

Frequently asked questions

Does KERNSTACK run on AWS or Google Cloud underneath?

No. KERNSTACK runs on dedicated bare-metal servers in Europe operated directly by us. We do not wrap Lambda, S3, or DynamoDB.

How do AWS services map to KERNSTACK resources?

API Gateway + CloudFront map to Gateway + CDN; Lambda maps to WebAssembly Functions; Fargate maps to Workers; DynamoDB maps to Tables; S3 maps to Buckets; OpenSearch maps to Search; Step Functions map to Workflows; Bedrock/SageMaker map to Inference — catalogue models and custom endpoints, with training run as workers beside the data.

Can we run hybrid or on-premise workloads?

Yes. On-premise User Nodes are on the roadmap so regulated teams can join their own hardware into the same KERNSTACK control plane.

Connected in the Project Graph

Related capabilities, architectures, and comparisons

Ship your stack on the Software Lifecycle Cloud.

Closed preview on dedicated European infrastructure. Bring a repository or start from a blueprint.

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