The most cost-effective approach is usually:

1) Use a single, always-on small endpoint for low-latency traffic

If your app needs online predictions, the cheapest reliable setup is often:

• one model endpoint
• a small CPU instance to start
• autoscaling only when needed

This avoids paying for oversized GPU capacity when traffic is light.

2) Scale on requests, not on speculation

For a growing app, cost efficiency comes from:

• autoscaling based on QPS / concurrency / queue depth
• keeping minimum replicas low or at zero if your platform supports it
• using request batching if latency allows it

This reduces idle compute.

3) Prefer CPU inference unless the model truly needs GPU

GPU can be much more expensive. CPU is usually the best value when:

• the model is small/medium
• latency requirements are moderate
• throughput is not extreme

Use GPU only if:

• the model is large
• per-request latency is too high on CPU
• you have enough traffic to keep the GPU busy

4) Optimize the model before scaling infrastructure

Cheaper infra often comes from cheaper inference:

• quantization
• pruning
• distillation
• smaller model variants
• ONNX / TensorRT / optimized runtimes
• caching repeated predictions

A 2× faster model is often cheaper than a 2× bigger server fleet.

5) Batch where possible

If your API can tolerate slight added latency:

• batch multiple prediction requests together
• especially useful for embeddings, ranking, and classification

This increases throughput per machine and lowers cost.

6) Use a managed serving platform if team time matters

Managed APIs can be most cost-effective overall when you factor in engineering overhead:

• easier autoscaling
• monitoring
• deployment
• rollback
• traffic splitting

Examples include managed online prediction services from major cloud providers or model serving platforms.

7) Add a cache for repeated or near-duplicate inputs

If many requests repeat:

• cache prediction results
• cache embeddings
• use approximate nearest-neighbor lookup where applicable

This can cut inference spend substantially.

Practical recommendation

For a growing app, the usual best path is:

1. Start with a managed online endpoint
2. Use the smallest CPU instance that meets latency
3. Turn on autoscaling
4. Add batching and caching
5. Reassess only when CPU utilization is consistently high
6. Move to GPU only if CPU can’t meet latency or throughput economically

Rule of thumb

• Low traffic / early growth: small CPU + autoscaling
• Moderate traffic: CPU + batching + caching
• High traffic or large models: consider GPU, but only with high utilization

If you want, I can also give you:

• a cost comparison: CPU vs GPU vs serverless
• a reference architecture for an online prediction API
• or help choose the best option based on your model size, latency target, and request rate