What should I use for cheap model hosting at low traffic?

For cheap model hosting at low traffic, the best option usually depends on whether you need:

1. Always-on API
2. Occasional/infrequent usage
3. CPU-only vs GPU
4. A ready-made hosted model vs your own container

Best cheap options

1. Serverless GPU endpoints

Good if traffic is low and bursty.

• Modal — very popular for low-traffic ML apps; scales to zero, easy Python deployment.
• RunPod Serverless — often cost-effective for sporadic inference.
• Replicate — simple to use, but can get pricey at scale.
• Beam / Baseten — convenient, usually more polished, sometimes pricier.

Why: you pay mostly when requests happen, not for an idle GPU.

---

2. Cheap CPU hosting

If your model is small enough to run on CPU, this is usually the cheapest.

• Fly.io
• Render
• Railway
• Hetzner / DigitalOcean / Vultr small VM
• Oracle Cloud Free Tier (if you can handle the setup and limits)

Why: low traffic + CPU inference can be extremely cheap, especially for smaller models or quantized models.

---

3. One small VM with a quantized model

Best when:

• traffic is low but you want predictable latency
• model fits in RAM/CPU
• you can tolerate always-on cost

Options:

• a small $5–$20/month VM
• run llama.cpp, vLLM only if resource needs fit, or other lightweight serving stack

This is often the cheapest “simple” setup if your model is small.

---

4. Managed model APIs

If you don’t need to host your own model at all:

• OpenAI
• Anthropic
• Mistral API
• Groq
• Together
• Fireworks

This can be cheapest operationally for low traffic because there’s no infra to manage.

---

My practical recommendation

If you want the lowest effort:

• Use a managed API if the model can be external.

If you want to host your own model and traffic is low:

• Small CPU VM if the model is small/quantized
• Modal or RunPod Serverless if you need GPU but traffic is sporadic

If you want the cheapest possible and can tolerate setup:

• Oracle Cloud Free Tier or a small Hetzner VM

---

Rule of thumb

• Tiny/quantized model → CPU VM
• Occasional GPU inference → serverless GPU
• Steady traffic → reserved GPU/VM may be cheaper than serverless
• Don’t want ops → managed API

If you tell me:

• model size/type,
• expected requests/day,
• whether you need GPU,
• and whether latency matters,

I can suggest the cheapest concrete setup.