Deploy Your Own Private ChatGPT on AWS in 30 Minutes

#ai #openai #cloud #api

What if you could deploy a fully private ChatGPT alternative — on your own AWS infrastructure, with your own data sovereignty rules — in 30 minutes?

No data leaving your account. No vendor lock-in. No per-user subscriptions. Just 3 Terraform commands.

Here's how.

The Stack

Component	Role
Open WebUI	ChatGPT-like interface (100,000+ ⭐ on GitHub)
stdapi.ai	OpenAI-compatible API gateway for AWS
AWS Bedrock	Access to 80+ foundation models

stdapi.ai sits between Open WebUI and AWS Bedrock, translating OpenAI API calls into native AWS requests. Any tool that speaks the OpenAI protocol — Open WebUI, n8n, VS Code AI assistants, custom apps — works immediately. No plugins, no custom integrations.

User → Open WebUI → stdapi.ai → AWS Bedrock → Claude Opus 4.6, DeepSeek, Kimi, Mistral…
                                             → AWS Polly (text-to-speech)
                                             → AWS Transcribe (speech-to-text)

What You Get

80+ AI models — Claude Opus 4.6, DeepSeek, Kimi, Mistral, Cohere, Stability AI, and more
Full multi-modal support — Chat, voice input/output, image generation/editing, document RAG
Multi-region access — Configure multiple AWS regions for the widest model selection and availability
Pay-per-use — No ChatGPT subscriptions, no per-seat fees. You pay only for actual AWS Bedrock usage
Production-ready infrastructure — ECS Fargate with auto-scaling, Aurora PostgreSQL + pgvector for RAG, ElastiCache Valkey, dedicated VPC, HTTPS with ALB

Data Sovereignty & Compliance

This is where it gets interesting for regulated industries:

Region restrictions — Lock inference to specific AWS regions matching your compliance requirements (GDPR, HIPAA, data residency laws, industry regulations)
No data shared with model providers — AWS Bedrock does not share your inference data with model providers
No training on your data — Your prompts and responses are never used for model training
Everything stays in your AWS account — No external data transmission beyond AWS services
Dedicated VPC — Isolated network for your AI workloads

Whether you need to keep data in the EU, in specific US regions, or within national boundaries for government requirements — you configure the allowed regions and stdapi.ai enforces it.

Deploy in 30 Minutes

git clone https://github.com/stdapi-ai/samples.git
cd samples/getting_started_openwebui/terraform

# ⚙️ Customize your settings (regions, models, scaling…)
# → Check the full documentation in the repo to tailor the deployment to your needs

terraform init && terraform apply

That's it. 3 commands.

What Terraform deploys for you:

Open WebUI on ECS Fargate with auto-scaling
stdapi.ai as the OpenAI-compatible AI gateway
Aurora PostgreSQL with pgvector extension for RAG
ElastiCache Valkey for caching
Dedicated, isolated VPC with HTTPS via ALB
All environment variables pre-configured and ready to go

How stdapi.ai Works Under the Hood

stdapi.ai is more than a simple proxy. It's an AI gateway purpose-built for AWS that:

Translates the OpenAI API — Chat completions, embeddings, images (generation/editing/variations), audio (speech/transcription/translation), and model listing
Handles multi-region routing — Automatically selects the best region and inference profile for each model
Exposes advanced Bedrock features — Prompt caching, reasoning modes (extended thinking), guardrails, service tiers, and model-specific parameters
Integrates native AWS AI services — Amazon Polly for TTS, Amazon Transcribe for STT with speaker diarization, Amazon Translate

Your existing OpenAI-powered tools work without modification. Change the base URL, and you're on AWS.

Who Is This For?

Teams that want a private ChatGPT with full data control
Regulated industries (finance, healthcare, government) that need data residency guarantees
Companies tired of paying per-seat ChatGPT subscriptions when usage varies wildly
Developers who want to use the OpenAI ecosystem on AWS infrastructure
Ops engineers who want production-grade AI infrastructure as code