cheap inference hosting for small traffic

For small-traffic inference hosting, the cheapest options are usually:

Best low-cost options

1. Serverless / pay-per-request

Good if traffic is intermittent or low.

• Modal
• Replicate
• Runpod Serverless
• Cloudflare Workers AI for supported models
• Hugging Face Inference Endpoints can be simple, but often not the cheapest

Pros: no always-on server cost
Cons: cold starts, less control, can get pricey with sustained traffic

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2. Cheap GPU VPS / dedicated small GPU

Good if you need more control and steady small usage.

• RunPod GPU pods
• Vast.ai
• Lambda Cloud (usually reliable, not always cheapest)
• Paperspace (varies)
• DigitalOcean / AWS / GCP GPU instances are usually more expensive than marketplace options

For very small traffic, a single low-end GPU or even CPU model can be enough.

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3. CPU hosting for smaller models

If your model is small enough or optimized:

• Fly.io
• Railway
• Render
• Hetzner VPS
• DigitalOcean basic droplet
• Lightsail

This is often the cheapest if:

• model is small
• latency requirements are modest
• you can use quantized models

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Cheapest practical stack

If you want the lowest cost for small traffic:

1. Try CPU first

   • quantize model
   • use ONNX / GGUF / TensorRT if applicable
   • host on Hetzner, Fly.io, or a small VPS

2. If CPU is too slow:

   • use Runpod serverless or Vast.ai for GPU
   • autoscale or run only when needed

3. If traffic is bursty:

   • use serverless inference
   • accept cold start tradeoff

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Rule of thumb

• Tiny traffic + simple model → CPU VPS
• Burst traffic → serverless inference
• Need GPU but still cheap → RunPod / Vast.ai
• Want easiest setup → Replicate / Modal

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My short recommendation

If you just want the cheapest option for small traffic, I’d check:

1. Hetzner VPS if CPU inference is possible
2. RunPod Serverless if you need a GPU
3. Vast.ai if you’re okay with more setup for lower GPU cost

If you want, I can give you a ranked list by monthly cost for:

• CPU-only
• GPU
• serverless
• or based on your model size / framework