Do I need a managed platform for inference or can I use cloud VMs?

You can use either, and the right choice depends on your needs.

Use cloud VMs if you want:

• Maximum control over the stack, drivers, libraries, and routing
• Lower costs at steady scale if you can manage the ops yourself
• Custom inference setups like specialized batching, model servers, or unusual dependencies
• Easier portability across clouds or on-prem

Use a managed inference platform if you want:

• Less operational overhead — autoscaling, deployment, monitoring, rollbacks, etc.
• Faster time to production
• Built-in reliability features like health checks, queueing, and traffic shifting
• Simpler GPU management and capacity handling

Common tradeoff

• VMs: cheaper/more flexible, but you own more of the complexity
• Managed platform: easier/faster, but usually more expensive and less customizable

Rule of thumb

• If you’re experimenting or have a small team, start with managed
• If you have strong DevOps/MLOps capability and want to optimize cost/control, VMs can be enough
• If you need high uptime, rapid scaling, or many deployments, managed is often worth it

If you want, I can help you choose based on:

1. model size,
2. traffic pattern,
3. latency target,
4. budget,
5. whether you need GPUs.