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Deploy stdapi.ai on AWS

Get a production-grade AI gateway running on AWS with two Terraform commands, speaking the OpenAI, Anthropic, Cohere and Ollama APIs.

Quick Start

Deploy

git clone https://github.com/stdapi-ai/samples.git
cd samples/getting_started_production/terraform
terraform init
terraform apply -var alb_domain_name=api.example.com

If the hosted zone is not the immediate parent of that name — say api.eu.example.com served from the example.com zone — name the zone as well with -var alb_route53_zone_name=example.com. The first apply waits for ACM to validate the certificate through DNS, which usually takes a few minutes.

No git? Download the ZIP 
curl -L https://github.com/stdapi-ai/samples/archive/refs/heads/main.zip -o samples.zip
unzip samples.zip
cd samples-main/getting_started_production/terraform
terraform init
terraform apply -var alb_domain_name=api.example.com

Confirm your AWS identity and region before deploying

The AWS provider uses the region and profile from your environment — not a Terraform variable. Check both before running terraform apply: 

aws sts get-caller-identity
aws configure get region

That's it. Two Terraform commands, and you have:

  • Production-grade ECS Fargate deployment with HTTPS
  • Regional S3 buckets
  • Auto-scaling and API key authentication
  • Interactive API documentation at /docs
  • IP-restricted access (your IP only)
  • Optional WAF and optional CloudWatch monitoring, one variable away
%%{init: {'flowchart': {'htmlLabels': true}} }%%
flowchart LR
  openai["<img src='../styles/logo_openai.svg' style='height:64px;width:auto;vertical-align:middle;' /> OpenAI SDK"] -->|HTTPS| alb["<img src='../styles/logo_amazon_load_balancing.svg' style='height:64px;width:auto;vertical-align:middle;' /> ALB"]
  anthropic["<img src='../styles/logo_anthropic.svg' style='height:64px;width:auto;vertical-align:middle;' /> Anthropic SDK"] -->|HTTPS| alb
  alb --> ecs["<img src='../styles/logo.svg' style='height:64px;width:auto;vertical-align:middle;' /> stdapi.ai<br/>ECS Fargate"]
  ecs --> bedrock["<img src='../styles/logo_amazon_bedrock.svg' style='height:64px;width:auto;vertical-align:middle;' /> Amazon Bedrock"]
  ecs --> polly["<img src='../styles/logo_amazon_polly.svg' style='height:64px;width:auto;vertical-align:middle;' /> Amazon Polly"]
  ecs --> transcribe["<img src='../styles/logo_amazon_transcribe.svg' style='height:64px;width:auto;vertical-align:middle;' /> Amazon Transcribe"]
  ecs --> s3["<img src='../styles/logo_amazon_s3.svg' style='height:64px;width:auto;vertical-align:middle;' /> Amazon S3"]
  ecs --> cloudwatch["<img src='../styles/logo_amazon_cloudwatch.svg' style='height:64px;width:auto;vertical-align:middle;' /> CloudWatch"]

Prerequisites

Before you start

  1. Subscribe on AWS Marketplace — this is the action that starts your 14-day free trial:

    Subscribe on AWS Marketplace — starts your 14-day free trial

  2. Install Terraform or OpenTofu >= 1.9.

  3. Configure AWS credentials (aws configure or aws sso login).
  4. A domain in a public Route 53 hosted zone in this account — the deployment serves the API from a name under it, such as api.example.com. It is required: an ALB reached through its generated *.elb.amazonaws.com name can never hold a trusted certificate, since ACM does not issue for a name you do not control, so a deployment without a domain would publish the API and every API key in cleartext over HTTP.

Requires AWS administrator permissions

The Terraform module provisions IAM roles and policies, KMS keys, ECS/Fargate, ALB, and networking. A restricted developer profile will fail during terraform apply.

Strongly recommended: deploy into a sandbox / non-production AWS account first to evaluate the stack, then replicate into your target account with scoped-down principals once you've validated it.

14-Day Free Trial

The AWS Marketplace subscription includes a 14-day free trial of the stdapi.ai license. AWS charges for the infrastructure it deploys (ALB, Fargate, KMS, NAT) and for Bedrock usage apply from the first minute — see Deployment Cost.

Get Your Credentials

terraform output -raw api_key
terraform output api_endpoint
terraform output docs_url

Ready-to-use Terraform examples on GitHub

See Data Sovereignty & Compliance for what each variant pins.

Optional: expose the API as MCP tools

The MCP server is off by default. Set enable_mcp_streamable_http = true on the Terraform module and every endpoint becomes a named MCP tool at <api_endpoint>/mcp, callable directly by Claude Code, LangGraph, or any MCP client.

Every exposed tool adds its schema to each MCP client's context window, so expose only the tools your agents actually use — for example mcp_include_tools = "openai_chat_completion,openai_embedding,search_models". See the MCP configuration reference.

Make Your First API Call

Want to explore the API without writing code? Use the docs_url from terraform output docs_url to open the interactive Swagger documentation in your browser — you can browse all available endpoints and make live API calls directly from the page, no code required.

If the docs page returns 503

This is normal on a fresh deployment: the ECS service takes 2–3 minutes to pass health checks. If it persists, check that the AWS Marketplace subscription was accepted — without it the ECS tasks cannot pull the licensed image and never become healthy. See Troubleshooting.

TLS needs no attention here: the sample serves the API from your own domain under an ACM certificate, so the browser trusts it. Only a configuration with neither alb_domain_name nor alb_certificate_arn behaves differently — the module then creates no HTTPS listener at all and the endpoint is plain http://, which must never carry an API key.

stdapi.ai is compatible with both OpenAI and Anthropic SDKs. If you've used either before, you already know how to use it — the base URL changes, along with the API key, and the model name only where it differs, since Anthropic's and OpenAI's own names for the models Bedrock serves resolve here as they stand. Here are the raw HTTP calls with curl so you can verify the endpoint from any shell:

API_ENDPOINT=$(terraform output -raw api_endpoint)
API_KEY=$(terraform output -raw api_key)

curl "$API_ENDPOINT/v1/chat/completions" \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "amazon.nova-micro-v1:0",
    "messages": [{"role": "user", "content": "Hello! Tell me a joke."}]
  }'

API_ENDPOINT=$(terraform output -raw api_endpoint)
API_KEY=$(terraform output -raw api_key)

curl "$API_ENDPOINT/anthropic/v1/messages" \
  -H "x-api-key: $API_KEY" \
  -H "anthropic-version: 2023-06-01" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "amazon.nova-micro-v1:0",
    "max_tokens": 1000,
    "messages": [{"role": "user", "content": "Hello! Tell me a joke."}]
  }'

Using the official SDKs? Point the base_url (Python) / baseURL (Node.js) option at $API_ENDPOINT/v1 (OpenAI SDK) or $API_ENDPOINT/anthropic (Anthropic SDK), and set the model field to a model from the catalog below. The rest of your existing code is unchanged. The API Overview has SDK snippets for Python, Node.js, and more.

Discover the full model catalog

Once your first call succeeds, switch the model field to any other Bedrock model — anthropic.claude-fable-5, anthropic.claude-sonnet-5, qwen.qwen3-coder-next, and more.

  • Browse before you switch: the Models page lists everything with prices and scores
  • Browse active models (recommended): GET /search_models — returns every discovered non-legacy model with full details (provider, modalities, supported routes, regions, streaming/legacy status). Add legacy=true to look up a deprecated model instead. Or open the interactive Swagger docs.
  • Find a model by capability: the same endpoint filters by modality, route, region, streaming, Batch API support, or legacy status — e.g. GET /search_models?input_modalities=IMAGE&route=/v1/chat/completions returns only vision-capable chat models. This is also the recommended way for AI agents to discover the right model ID before calling another endpoint. See the Search Models API reference.
  • OpenAI SDK compatibility: GET /v1/models is also available with the standard OpenAI listing format (lighter payload, no capability metadata) for tools that require the exact OpenAI schema.

Using your own Terraform config instead of the sample? The sample above enables authentication for you (api_key_create = true, retrieved above). Writing your own module config from scratch? stdapi.ai runs without authentication unless you set api_key_create = true — see Authentication & Security for all options.

Verify the deployment is healthy:

curl $API_ENDPOINT/health
# → {"status": "ok"}

The /health endpoint requires no authentication and is used by the ALB health check.

Troubleshooting

The 503 hiccup on first deployment is already covered above — see Make Your First API Call.

Full troubleshooting guide: Troubleshooting — 401 auth errors, 404 model not found, ThrottlingException, S3 errors, VPC connectivity, Terraform IAM failures, and more.

Need help?

For questions, issue reports, or assistance, see the Contact page.

Prefer a hands-off setup?

A managed deployment service is available if you'd rather not manage Terraform yourself. Choose between guided assistance (step-by-step support while you retain full control) or fully managed setup (handled on your behalf, inside your AWS account). Response time is 1 business day during the engagement.

Deployment Cost

AWS infrastructure cost is driven by configuration, not a fixed default: task count (one per Availability Zone unless overridden), Fargate Spot vs. on-demand, scheduled service hours, and whether an ALB is provisioned at all. A minimal deployment — one scheduled Fargate Spot task, no ALB, reached via Service Discovery — and a full multi-AZ production stack — ALB + WAF, one task per AZ, running 24/7 — sit at opposite ends of a wide range.

stdapi.ai's own license is billed separately at $0.10/container-hour ($0.09 via private offer); Bedrock and other AI service usage is billed by AWS at cost, with no stdapi.ai markup.

See Cost-Optimized Deployment for the Spot/scheduling configuration, and Cost Management → Gateway Cost for the full tier-by-tier breakdown.

Cleaning Up

When you're done testing, tear down the stack to stop incurring AWS and license charges:

terraform destroy

Running from the ZIP download instead of git clone? Run the same command from the samples-main/getting_started_production/terraform directory.

Next Steps