A production web app is never just a frontend and a server. It's a database, a cache, a queue, a gateway, storage, and increasingly AI. ContextOS provisions every layer and connects them automatically.
Assembling a web app backend from individual cloud services works, but it comes with a permanent operational cost that compounds as the app grows. Each service is its own console, its own pricing model, its own credentials to rotate.
Provision the database, the cache, the queue, the gateway. Each with its own console, its own pricing model, its own learning curve.
Custom networking, security groups, IAM policies that accumulate complexity every time you add or change a service.
Development, staging, production. Three versions of every configuration. Any change requires rewriting them all.
Declare the model in the UI. ContextOS provisions the runtime on dedicated GPU hardware. No inference framework to install, no CUDA to configure, no batching to tune. GPU utilisation is a first-class autoscaling signal.
Document ingestion, chunking, embedding, vector storage, and retrieval, assembled and connected automatically. Your app queries the pipeline through one authenticated endpoint. Continual or on-demand ingestion.
TLS termination, certificate rotation, authentication, rate limiting, and load balancing at the edge. Add a web gateway and your inference endpoint is production-grade the moment it goes live.
Every layer below is provisioned from the ContextOS UI. Every connection is established by the Zero Trust Bridge. Every credential is generated and rotated automatically.
Provision each service separately, each with its own console, pricing model, and learning curve.
Add every service from one UI.
Provisioned on bare metal, configured, and running in minutes.
Wire services together with custom networking, security groups, and IAM policies.
The Zero Trust Bridge connects every service automatically.
No network rules, no wiring required.
Manage credentials for every service separately. Rotation, expiry, distribution.
Credentials generated, stored, and rotated automatically.
Developers never handle them.
Add AI infrastructure on top of everything else when the product needs it.
Add AI model serving, RAG pipelines, and vector search from the same UI.
Already connected to your app.
4 architectural pieces work together to make this stack possible.
One resource pool across compute, storage, and networking.
Policy-driven scaling for CPU, GPU, and stateful workloads.
Production-ready services provisioned from one catalog.
Service-to-service auth, credential rotation, mutual TLS.
Join the closed beta. Ship your first AI app this week.