Runpod

Total monthly cost: Rarely exceeds $10 even with heavy use.

How It Works

All tools follow the same pattern:

Local CLI → Upload input to cloud storage → RunPod API → Poll for result → Download output

1. File transfer: Tools use Cloudflare R2 when configured (R2_ACCOUNT_ID, R2_ACCESS_KEY_ID, R2_SECRET_ACCESS_KEY, R2_BUCKET_NAME), falling back to free upload services
2. RunPod API: Tools call the /run endpoint, then poll /status/{job_id} until complete
3. Cold vs warm start: First request after idle spins up a worker (~30-90s). Subsequent requests are fast (~5-15s)

Endpoint Management

Workers

workersMin: 0    — Scale to zero when idle (no cost)
workersMax: 1    — Max concurrent jobs (increase for throughput)
idleTimeout: 5   — Seconds before worker scales down

Across all endpoints, you share a total worker pool based on your RunPod plan. If you hit limits, reduce workersMax on endpoints you're not actively using.

Checking Endpoint Status

Each tool stores its endpoint ID in .env:

Disabling an Endpoint

To free worker slots without deleting the endpoint, set workersMax=0 via the RunPod dashboard or GraphQL API.

RunPod API Reference

Use these to query and manage endpoints programmatically. RunPod disables GraphQL introspection, so these field names are verified and must be exact.

Authentication

All API calls require Authorization: Bearer $RUNPOD_API_KEY.

• GraphQL: POST https://api.runpod.io/graphql
• REST (Serverless): https://api.runpod.ai/v2/{endpoint_id}/...

GraphQL Queries

List all endpoints:

query { myself { endpoints { id name gpuIds templateId workersMax workersMin } } }

Current spend rate:

query { myself { currentSpendPerHr spendDetails { localStoragePerHour networkStoragePerHour gpuComputePerHour } } }

List pods:

query { myself { pods { id name runtime { uptimeInSeconds } machine { gpuDisplayName } desiredStatus } } }

Common mistakes: Field names are camelCase with full words — localStoragePerHour not localStoragePerHr. Endpoints are endpoints not serverlessWorkers. spending is not a field — use currentSpendPerHr and spendDetails.

GraphQL Mutations

Update endpoint GPU or config:

mutation { saveEndpoint(input: {
  id: "endpoint_id",
  name: "endpoint-name",
  templateId: "template_id",
  gpuIds: "AMPERE_24",
  workersMin: 0,
  workersMax: 1
}) { id gpuIds } }

saveEndpoint requires name and templateId even for updates — query first to get current values.

REST API (Serverless)

Health response includes job counts and worker state:

{
  "jobs": { "completed": 16, "failed": 1, "inProgress": 0, "inQueue": 2, "retried": 0 },
  "workers": { "idle": 0, "initializing": 1, "ready": 0, "running": 0, "throttled": 0 }
}

Note: /requests only returns pending/queued jobs. Completed job history is not available via the API — check the RunPod web console for logs.

GPU Type IDs

Availability note: ADA_24 (4090) is frequently throttled/unavailable on RunPod. Always configure endpoints with multiple fallback GPU types (comma-separated) to avoid jobs getting stuck in queue indefinitely:

gpuIds: "AMPERE_24,ADA_24"   # Try 3090 first, fall back to 4090

All toolkit tools also enforce a 5-minute queue timeout — if no GPU is available within 300 seconds, the job is automatically cancelled to prevent runaway billing from failed initialization cycles.

Cloudflare R2 via AWS CLI

R2 uses the S3-compatible API but requires --region auto:

AWS_ACCESS_KEY_ID="$R2_ACCESS_KEY_ID" \
AWS_SECRET_ACCESS_KEY="$R2_SECRET_ACCESS_KEY" \
aws s3api list-objects-v2 \
  --bucket "$R2_BUCKET_NAME" \
  --endpoint-url "https://${R2_ACCOUNT_ID}.r2.cloudflarestorage.com" \
  --region auto

Common mistake: Omitting --region auto causes InvalidRegionName error. R2 valid regions: wnam, enam, weur, eeur, apac, oc, auto.

Troubleshooting

Force Image Pull

When you push a new Docker image version, RunPod may still use the cached old one. To force a pull:

1. Update the template's imageName to use @sha256:DIGEST notation
2. Wait for the worker to restart
3. Revert to :latest tag after confirming

Cold Start Too Slow

• qwen3-tts: ~70s cold start, ~7s warm
• sadtalker: ~60s cold start, ~10s warm
• image_edit: ~90s cold start, ~15s warm

If cold starts are a problem, set workersMin: 1 (costs money when idle).

Job Fails with OOM

The model needs more VRAM than the GPU provides. Options:

• Use a larger GPU tier
• For dewatermark: reduce --resize-ratio (default 0.5 for safety)
• For image_edit: reduce --steps

"No workers available"

You've hit your plan's concurrent worker limit. Either:

• Wait for a running job to finish
• Set workersMax=0 on endpoints you're not using
• Upgrade your RunPod plan

Docker Images

All Dockerfiles live in docker/runpod-*/. Images use runpod/pytorch as the base to share layers across tools.

Building for RunPod (from Apple Silicon Mac):

docker buildx build --platform linux/amd64 -t ghcr.io/conalmullan/video-toolkit-<name>:latest docker/runpod-<name>/
docker push ghcr.io/conalmullan/video-toolkit-<name>:latest

GHCR packages default to private — you must manually make them public for RunPod to pull them. Go to GitHub > Packages > Package Settings > Change Visibility.

Cost Optimization

• Keep workersMin: 0 on all endpoints (scale to zero)
• Only deploy endpoints you actively need
• Use workersMax=0 to disable idle endpoints without deleting them
• Qwen3-TTS is significantly cheaper than ElevenLabs for voiceovers
• Check the RunPod dashboard for usage and billing