# BLUEPRINT-AWS-PRIVATE-AI
Private LLM platform on AWS
Run private AI workflows on dedicated GPU capacity in your own AWS account — open-weight models, zero standby cost, multi-tenant by design.
# THE-PROBLEM
why this pattern exists.
Hosted AI APIs are easy to start with — but the moment you have sensitive prompts, regulated data, or material inference volume, the trade-offs flip. You're sending business-critical context to a third party, paying per-token at someone else's pricing curve, and locked to whichever models that vendor chooses to expose.
Self-hosting fixes all three — but only if it doesn't burn a 24/7 GPU bill, only if it's actually reliable for real work, and only if a small team can stand it up and own it. What you actually want is private, dedicated capacity that scales to zero when nobody's using it, picks up sub-second when someone does, and lets you swap models or add use cases without rebuilding from scratch.
# ARCHITECTURE
how it's built.
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Private AI infrastructure
Your models, your data, your VPC, your AWS account. Prompts and outputs never leave your perimeter — no third-party data-processing terms to negotiate, no data-residency exceptions, no shared tenancy with other vendors' customers.
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Zero standby cost
GPUs are provisioned on demand and released when work drains. You pay for inference minutes, not for parked capacity. An idle team costs nothing; a busy team scales up automatically and back down when they're done.
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Open-weight model freedom
Run any open-weight model your workloads need — Gemma, Qwen, Llama, others — and swap models without re-architecting the platform. Right-size each use case to the model that fits, not to whatever the vendor picked for you.
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Multi-tenant by design
Per-team isolation, per-user authentication (including SSO via SAML), per-application credentials for service-to-service callers. Different teams share GPU capacity safely without sharing prompts, history, or quotas.
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Production-grade reliability
Long-running generations are protected from termination, queued work survives capacity transitions, and the API stays sub-second even when the GPU fleet is cold or scaling. The platform is designed to sit at zero overnight and resume the next morning without lost work.
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Deployable anywhere, fully codified
The entire platform is infrastructure as code. Stand it up in any AWS account or region in hours; extend it with new endpoints or use cases by attaching another stack — no rebuilds, no environment-specific code paths.
# KEY-PRIMITIVE
the load-bearing idea.
Zero standby cost without dropping work
The decision that makes this blueprint commercially viable is solving cost and reliability together: GPUs only run when there's work for them, and termination never kills an in-flight generation. The fleet returns to zero only when work is genuinely complete; when scale-down does fire, instances signal they're still busy and stay up until they're truly idle. The result is a platform that can sit at zero spend overnight and resume sub-second the next morning, with no lost work in between — the cost profile of pay-per-token, the privacy and control of self-hosted.
# TECH-STACK
what runs it.
# PRODUCTION-EVIDENCE
what we've measured.
Private AI on dedicated AWS infrastructure. Years in production.
This is a working platform — not a POC, not a demo. Companies run their own LLM workflows on dedicated GPU capacity inside their own AWS account, with prompts and outputs that never leave their perimeter. Open-weight models give them model choice without vendor lock-in; the serverless control plane keeps idle cost at zero; the IaC foundation means new use cases ship as additional endpoints, not as another platform to operate. It's the cost profile of pay-per-token with the privacy and control of self-hosted.
- Idle GPU cost
- Zero
- Your AWS account
- Private
- API latency
- Sub-sec
- In production
- Years
want one of these in production?
30-min discovery call. we adapt the blueprint, we don't resell it.
book-call// or write: hello@saloid.com · gräfelfing · de