DEV Community: J.Goutin

Deploy Your Own Private ChatGPT on AWS in 30 Minutes

J.Goutin — Thu, 19 Feb 2026 21:20:44 +0000

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

Get Started

📦 Deployment repo: github.com/stdapi-ai/samples

📖 Documentation: stdapi.ai

📩 Need help? We can help you deploy and customize this solution for your needs. Reach out to us.

3 commands. 30 minutes. Your private ChatGPT is in production. 🎯

Deploy Your Own Private ChatGPT on AWS in 30 Minutes

J.Goutin — Thu, 19 Feb 2026 21:15:15 +0000

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

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

Get Started

📦 Deployment repo: github.com/stdapi-ai/samples

📖 Documentation: stdapi.ai

📩 Need help? We can help you deploy and customize this solution for your needs. Reach out to us.

3 commands. 30 minutes. Your private ChatGPT is in production. 🎯

Use AWS Bedrock & AI Services (Claude, Nova, Polly, Transcribe) with Your Existing OpenAI Code

J.Goutin — Fri, 19 Dec 2025 14:58:08 +0000

TL;DR

Use your existing OpenAI-compatible tools (OpenWebUI, n8n, Cline, Cursor) with AWS Bedrock's 80+ models and AWS AI services (Polly, Transcribe). Just change the endpoint - no code changes needed. Deploy to your AWS account in 15 minutes with Terraform. Data stays in your AWS environment for HIPAA/GDPR compliance.

The Solution in 30 Seconds

from openai import OpenAI

# Just change this line ↓
client = OpenAI(base_url="https://your-aws-api/v1", api_key="your-key")

# Everything else stays the same ↓
response = client.chat.completions.create(
    model="anthropic.claude-sonnet-4-5-20250929-v1:0",  # Claude 4.5 on Bedrock
    messages=[{"role": "user", "content": "Hello from AWS!"}]
)

What this enables:

Use OpenWebUI, n8n, Cline, Cursor with AWS Bedrock and AWS AI services
Access Claude 4.5, Nova, Qwen, DeepSeek, Mistral, Cohere, and 75+ more models
Text-to-speech (Polly), speech-to-text (Transcribe), image generation, embeddings
Keep all data in your AWS account (HIPAA, GDPR, FedRAMP ready)
Multi-region model access from a single endpoint

The Problem We Solved

Most modern AI tools are built for OpenAI's API, but AWS Bedrock uses a completely different SDK. This forces you to choose between using familiar tools or switching to AWS for compliance/cost reasons and rewriting everything.

We ran into this while working with a customer who needed:

Compliance: All data in their AWS account (GDPR requirements)
Choice: Multiple models (Claude, Nova, Qwen, Mistral, DeepSeek, Stability AI, Cohere)
Multi-region access: Different Bedrock models available in different AWS regions
Standard tooling: OpenWebUI, n8n, IDE coding agents (Cline, Cursor)
Full AWS AI stack: Bedrock models plus Polly (TTS) and Transcribe (STT)

Our Approach: An OpenAI-Compatible Translation Layer

stdapi.ai is an API gateway that sits in your AWS account and translates OpenAI API calls to AWS services.

Key Features

OpenAI API Compatibility

Your existing code works without modifications:

from openai import OpenAI

client = OpenAI(
    base_url="https://your-deployment.example.com/v1",
    api_key="your-api-key"
)

# Chat completions
response = client.chat.completions.create(
    model="amazon.nova-pro-v1:0",
    messages=[{"role": "user", "content": "Explain AWS Lambda"}]
)

# Text-to-speech with Polly
response = client.audio.speech.create(
    model="amazon.polly-neural",
    input="Welcome to the future of voice technology!"
)

# Speech-to-text with Transcribe
with open("meeting-recording.mp3", "rb") as audio_file:
    transcription = client.audio.transcriptions.create(
        model="amazon.transcribe",
        file=audio_file,
        response_format="json"
    )

# Image generation with Bedrock
response = client.images.generate(
    model="stability.stable-image-ultra-v1:0",
    prompt="A serene mountain landscape at sunset"
)

# Embeddings with Bedrock
response = client.embeddings.create(
    model="cohere.embed-v4",
    input="Semantic search transforms how we find information"
)

Multi-Region Access

Access models across multiple AWS Bedrock regions from a single endpoint. Different models are available in different regions. You can configure which regions to include based on your requirements. AWS Bedrock inference profiles handle automatic routing when needed.

Multi-Modal Capabilities

Process text, images, videos, and documents together in a single request. Supports HTTP URLs, S3 URLs (for direct access to your data), and base64 data URIs. Perfect for vision tasks, document analysis, and RAG applications.

Deployment

Deploy to your AWS account in 5-15 minutes using Terraform.

Sample configurations set up:

Application Load Balancer with ECS/Fargate
IAM roles with least-privilege access
CloudWatch logging
S3 storage
Optional: Custom domain with ACM certificate

Get started: Sample Terraform configurations

Use Cases

Chat interfaces: OpenWebUI or LibreChat for private ChatGPT alternatives
Workflow automation: n8n connecting AI to 400+ services
Developer tools: IDE coding agents (Cline, Cursor, Continue)
Knowledge management: AI-powered note-taking and semantic search
Internal AI tools: Custom chatbots for Slack, Discord, Teams

Technical Details

How It Works:

Converts OpenAI API format to Bedrock's format
Maps model IDs to appropriate AWS services and regions
Provides unified access to models across configured AWS regions
Converts Bedrock responses back to OpenAI format
Supports streaming via Server-Sent Events (SSE)

Security:

All traffic stays within your AWS account
IAM role-based access control
Optional API key authentication
VPC deployment supported
Integrates with AWS Bedrock guardrails

Performance:

Low latency translation layer
Streaming response support
Scales with ECS/Fargate auto-scaling

Open Source & Commercial Options

stdapi.ai is available as open source (AGPL-3.0) for experimentation and internal use.

For production deployments, we recommend the AWS Marketplace version, which includes:

Hardened container images
Regular security updates
Production support
14-day free trial

The commercial license also removes AGPL obligations for proprietary applications.

Get Started

🏢 Recommended: AWS Marketplace (14-day free trial, production-ready)

🚀 Open source: GitHub (AGPL-3.0 for experimentation)

📦 Sample deployments: Terraform configurations

📚 Documentation: Full guides and API reference

Conclusion

This translation layer approach solves the AWS Bedrock compatibility gap while keeping everything AWS-native.

Key benefits:

Zero code changes: Your OpenAI SDK code works as-is
AWS-native: Fully integrated with Bedrock, Polly, Transcribe
Multi-region: Access models across AWS regions from one endpoint
Production-ready: Terraform deployment, CloudWatch integration

We'd love to hear your feedback! Have you solved this problem differently? What challenges have you faced integrating Bedrock into your workflow?

What's Next

We're planning detailed tutorials on:

Deploying OpenWebUI on AWS: Private ChatGPT alternative with Bedrock
Building n8n workflows: AWS AI services in automation pipelines
IDE coding agents setup: Configuring Cline and Cursor with Bedrock
RAG applications: Document search with Bedrock embeddings

Want a specific tutorial? Drop a comment or open an issue on GitHub!

Use AWS Bedrock & AI Services (Claude, Nova, Polly, Transcribe) with Your Existing OpenAI Code

J.Goutin — Fri, 19 Dec 2025 14:32:38 +0000

TL;DR

The Solution in 30 Seconds

from openai import OpenAI

# Just change this line ↓
client = OpenAI(base_url="https://your-aws-api/v1", api_key="your-key")

# Everything else stays the same ↓
response = client.chat.completions.create(
    model="anthropic.claude-sonnet-4-5-20250929-v1:0",  # Claude 4.5 on Bedrock
    messages=[{"role": "user", "content": "Hello from AWS!"}]
)

What this enables:

Use OpenWebUI, n8n, Cline, Cursor with AWS Bedrock and AWS AI services
Access Claude 4.5, Nova, Qwen, DeepSeek, Mistral, Cohere, and 75+ more models
Text-to-speech (Polly), speech-to-text (Transcribe), image generation, embeddings
Keep all data in your AWS account (HIPAA, GDPR, FedRAMP ready)
Multi-region model access from a single endpoint

The Problem We Solved

We ran into this while working with a customer who needed:

Compliance: All data in their AWS account (GDPR requirements)
Choice: Multiple models (Claude, Nova, Qwen, Mistral, DeepSeek, Stability AI, Cohere)
Multi-region access: Different Bedrock models available in different AWS regions
Standard tooling: OpenWebUI, n8n, IDE coding agents (Cline, Cursor)
Full AWS AI stack: Bedrock models plus Polly (TTS) and Transcribe (STT)

Our Approach: An OpenAI-Compatible Translation Layer

stdapi.ai is an API gateway that sits in your AWS account and translates OpenAI API calls to AWS services.

Key Features

OpenAI API Compatibility

Your existing code works without modifications:

from openai import OpenAI

client = OpenAI(
    base_url="https://your-deployment.example.com/v1",
    api_key="your-api-key"
)

# Chat completions
response = client.chat.completions.create(
    model="amazon.nova-pro-v1:0",
    messages=[{"role": "user", "content": "Explain AWS Lambda"}]
)

# Text-to-speech with Polly
response = client.audio.speech.create(
    model="amazon.polly-neural",
    input="Welcome to the future of voice technology!"
)

# Speech-to-text with Transcribe
with open("meeting-recording.mp3", "rb") as audio_file:
    transcription = client.audio.transcriptions.create(
        model="amazon.transcribe",
        file=audio_file,
        response_format="json"
    )

# Image generation with Bedrock
response = client.images.generate(
    model="stability.stable-image-ultra-v1:0",
    prompt="A serene mountain landscape at sunset"
)

# Embeddings with Bedrock
response = client.embeddings.create(
    model="cohere.embed-v4",
    input="Semantic search transforms how we find information"
)

Multi-Region Access

Multi-Modal Capabilities

Deployment

Deploy to your AWS account in 5-15 minutes using Terraform.

Sample configurations set up:

Application Load Balancer with ECS/Fargate
IAM roles with least-privilege access
CloudWatch logging
S3 storage
Optional: Custom domain with ACM certificate

Get started: Sample Terraform configurations

Use Cases

Chat interfaces: OpenWebUI or LibreChat for private ChatGPT alternatives
Workflow automation: n8n connecting AI to 400+ services
Developer tools: IDE coding agents (Cline, Cursor, Continue)
Knowledge management: AI-powered note-taking and semantic search
Internal AI tools: Custom chatbots for Slack, Discord, Teams

Technical Details

How It Works:

Converts OpenAI API format to Bedrock's format
Maps model IDs to appropriate AWS services and regions
Provides unified access to models across configured AWS regions
Converts Bedrock responses back to OpenAI format
Supports streaming via Server-Sent Events (SSE)

Security:

All traffic stays within your AWS account
IAM role-based access control
Optional API key authentication
VPC deployment supported
Integrates with AWS Bedrock guardrails

Performance:

Low latency translation layer
Streaming response support
Scales with ECS/Fargate auto-scaling

Open Source & Commercial Options

stdapi.ai is available as open source (AGPL-3.0) for experimentation and internal use.

For production deployments, we recommend the AWS Marketplace version, which includes:

Hardened container images
Regular security updates
Production support
14-day free trial

The commercial license also removes AGPL obligations for proprietary applications.

Get Started

🏢 Recommended: AWS Marketplace (14-day free trial, production-ready)

🚀 Open source: GitHub (AGPL-3.0 for experimentation)

📦 Sample deployments: Terraform configurations

📚 Documentation: Full guides and API reference

Conclusion

This translation layer approach solves the AWS Bedrock compatibility gap while keeping everything AWS-native.

Key benefits:

Zero code changes: Your OpenAI SDK code works as-is
AWS-native: Fully integrated with Bedrock, Polly, Transcribe
Multi-region: Access models across AWS regions from one endpoint
Production-ready: Terraform deployment, CloudWatch integration

We'd love to hear your feedback! Have you solved this problem differently? What challenges have you faced integrating Bedrock into your workflow?

What's Next

We're planning detailed tutorials on:

Deploying OpenWebUI on AWS: Private ChatGPT alternative with Bedrock
Building n8n workflows: AWS AI services in automation pipelines
IDE coding agents setup: Configuring Cline and Cursor with Bedrock
RAG applications: Document search with Bedrock embeddings

Want a specific tutorial? Drop a comment or open an issue on GitHub!