From two-person startups shipping their first AI product to enterprise teams running regulated AI infrastructure at scale.
You're moving fast and don't want to spend weeks stitching together a model hosting service, a database, an edge network, and a secrets manager. BRICQS gives you all of it in one platform. Deploy a GPU runtime, connect your database, secure your API keys — in one afternoon.
Your org needs dedicated infrastructure, audit logs on every action, approval gates before production deploys, secrets isolation between teams, and SSO. BRICQS is built for teams that need these controls, not teams that need to build them.
You need to run large open-weight models, store and query embeddings, and build retrieval pipelines — without becoming a cloud infrastructure expert. BRICQS handles the Kubernetes, the networking, the SSL certs, the GPU allocation. You focus on the model.
Here's what you'd stitch together without BRICQS — and what BRICQS replaces.
Not just convenience — BRICQS is architected differently from shared-pool platforms that bolt on AI features as an afterthought.
Your org gets its own Storage account and secrets vault at signup. No multi-tenant pooling. Data isolation isn't a compliance add-on — it's the default architecture.
BRICQS Identity means zero passwords in environment variables. WAF and bot protection on every domain by default. Audit log on every platform action — create, update, deploy, delete.
CPU%, memory%, GPU utilization, request count, and response time from Azure Monitor on every deployment. No OpenTelemetry configuration, no Grafana setup, no third-party agent to install.
Development, preview, and production run simultaneously on the same platform. Promote from preview to production with zero downtime and a single click — no YAML required.
Five runtimes designed specifically for AI workloads: an LLM gateway with model routing and caching, GPU containers for open-weight model inference, agent loop orchestration with memory and tool use, RAG pipeline runtime with document ingestion, and visual workflow orchestration. These aren't adapter layers over general compute — they're first-class platform primitives built for how AI applications actually work.
Each runtime is purpose-built — not a thin wrapper around generic cloud compute.
BRICQS is opinionated about the happy path. You shouldn't need to read a 40-page AWS docs page to deploy a GPU container. The CLI, dashboard, and API are all designed to minimize time-to-first-deploy.