Should I use serverless inference for a SaaS product?

Maybe — it depends on your traffic pattern, latency needs, and cost tolerance.

Serverless inference is a good fit if:

• Traffic is spiky or unpredictable
• You’re early-stage and want to avoid managing GPU/infra
• You can tolerate cold starts or occasional startup latency
• Your workloads are short-lived and stateless
• You want to scale to zero when idle

It’s usually a bad fit if:

• You need consistent low latency or real-time responses
• You have steady, high volume traffic
• Your model is large and expensive to load repeatedly
• You need tight control over hardware, batching, or routing
• Your unit economics are sensitive and you want lower cost at scale

Common SaaS pattern

A lot of SaaS products start with serverless inference to:

• launch faster,
• validate demand,
• minimize ops.

Then they move to:

• dedicated endpoints,
• autoscaled GPU workers,
• or a hybrid setup once traffic becomes predictable.

Practical recommendation

If you’re building a SaaS product, use serverless inference when:

1. MVP / early customers: yes, to move fast.
2. Bursty usage: yes, often ideal.
3. Latency-sensitive core feature: be cautious.
4. At scale: compare costs carefully; dedicated infrastructure may win.

Quick rule of thumb

• < moderate steady usage, variable demand → serverless
• high, steady traffic → dedicated deployment
• unknown demand → start serverless, measure, then optimize

If you want, I can help you decide based on your specific:

• model size,
• expected requests/minute,
• latency target,
• and budget.