page:recipes:ai workflow runner:axum:artifact storage

Host ai workflow runner with Axum: stored input and output artifacts

Summary

Deploy a ai workflow runner built with Axum 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 Axum: 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 workflow runner-specific behavior as your application code.

Representative Queries

Resource Requirements

Infrastructure Requirements

  1. Compute
    Apps run in isolated x86_64 Firecracker microVMs that auto-pause when idle and wake on request; sizes are the priced VM sizes.

  2. 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.

  3. 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
    cargo build --release in the rust builder image (stable toolchain, Cargo.lock committed, pure-Rust dependencies), then the release binary on the ubuntu-24.04 template reading PORT and DATABASE_URL. 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 Keep private data in an unpublished bucket.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.

    ample deploy . --name --public --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.

    ample logs --kind build

Limitations

Cost Estimate

Next Actions