page:recipes:ai chat application:starlette:artifact storage

Host ai chat application with Starlette: stored input and output artifacts

Deploy a ai chat application built with Starlette on Ample using the generated downloads pattern. Compute runs the app in an isolated microVM behind a public HTTPS URL, a managed PostgreSQL 16 database is auto-provisioned and injected as DATABASE_URL, a private S3-compatible bucket holds objects with credentials delivered as encrypted environment variables. Verified on Starlette: an export query rendered to a CSV artifact stored with metadata in a private bucket and returned only to an authorized caller (401 without token, export=ok rows=3). Not separately tested: your export formats, retention policy and entitlement model; treat the ai chat application-specific behavior as your application code.

Representative Queries

• Host ai chat application with Starlette: stored input and output artifacts
• Where can I host AI chat application built with Starlette?
• I need an input/output artifact contract, object metadata and access boundaries.

Resource Requirements

• primitive:compute
• primitive:postgres
• primitive:s3-compatible-object-storage

Infrastructure Requirements

• Compute: Apps run in isolated x86_64 Firecracker microVMs that auto-pause when idle and wake on request; sizes are the priced VM sizes.
• Postgres: Managed PostgreSQL 16 runs in its own microVM and is auto-provisioned when an app needs a database and no DATABASE_URL is supplied.
• S3-compatible object storage: Buckets are S3-compatible with issued credentials; PutObject and GetObject are verified by canary. Other S3 operations are not verified.

Workflow Steps

1. Build and start: pip install into .ample/python from requirements.txt, then uvicorn from run.py reading PORT on the python-3.12 template. The server must bind 0.0.0.0 on PORT.
2. Implement the pattern on PostgreSQL: The fixture's module implements generated downloads: an export query rendered to a CSV artifact stored with metadata in a private bucket and returned only to an authorized caller (401 without token, export=ok rows=3). Copy the approach into your schema; keep migrations idempotent and run them with --release-command.
3. Wire object storage: Create the bucket(s), then pass endpoint, region, bucket and keys as --env values. Use path-style addressing. Keep private data in an unpublished bucket.
4. Deploy: Run the synchronous deploy once and read the result (exit 0 live, 1 failed, 2 blocked). Re-running with no change is a no-op. Command: ample deploy . --name --public --start "python3 run.py" --env S3_ENDPOINT=... --env S3_REGION=... --env S3_BUCKET=... --env S3_ACCESS_KEY_ID=... --env S3_SECRET_ACCESS_KEY=...
5. Verify: Fetch the live URL and the pattern self-test route(s) (/p/generated-downloads) from the example; then run your own checks. On failure read ample logs --kind build then --kind runtime. Command: ample logs --kind build.

Prerequisites

• A Starlette project that builds and starts with the documented commands (pip install into .ample/python from requirements.txt, then uvicorn from run.py reading PORT on the python-3.12 template).
• A PostgreSQL driver reading DATABASE_URL at runtime (auto-provisioned when omitted, or supplied with --env).
• A bucket from ample bucket create with its credentials passed as encrypted S3_* environment variables.
• An Ample account token with servers:write, databases:read, buckets:read.

Tested Configuration

• Template: python-3.12
• Runtime: python
• Size: s-1vcpu-1gb
• Install: python3 -m pip install --target .ample/python -r requirements.txt
• Start: PYTHONPATH=.ample/python:${PYTHONPATH:-} python3 run.py

Examples

• Starlette pattern fixture: Multi-pattern Starlette app whose generated downloads module was checked live; the module is under tests/deploy-canaries/_pattern-modules.

Success Checks

• App responds on its public URL: HTTP GET on / -> expect: ample canary starlette patterns
• Generated-downloads self-test: HTTP GET on /p/generated-downloads -> expect: export=ok rows=3 (with ?token=...; 401 without)

Limitations

• Verified on the python-3.12 template at s-1vcpu-1gb with the example fixture; other sizes, templates and Starlette major versions are not verified.
• The ai chat application itself (your export formats, retention policy and entitlement model) is application code and was not separately tested; the pattern checks are what was verified.
• Region, compliance attestations and request-duration limits are unknown and not claimed.
• Apps auto-pause when idle and wake on the next request; always-on is an operator setting, not a plan feature.
• Managed PostgreSQL 16 only; extensions, connection limits and backup or restore procedures are not verified; apps and their databases are placed together.

Cost Estimate

• Monthly Amount: 10.00 USD
• Components:
  • App server (s-1vcpu-1gb): 5.00 USD
  • Managed PostgreSQL database (s-1vcpu-1gb): 5.00 USD
• Always-on monthly price of the tested sizes; apps and databases auto-pause when idle. Buckets are allocation-priced per quota and not included.