Should I use Replicate or run my own GPU stack?

It depends on what you’re optimizing for:

Use Replicate if you want:

• Fastest time to launch — no GPU ops, drivers, scaling, or orchestration
• Simple inference workflows — prototypes, demos, internal tools, light production
• Managed reliability — easier deployments, autoscaling, retries, basic ops handled
• No ML infra team — especially if you don’t want to maintain CUDA/PyTorch/container quirks

Run your own GPU stack if you want:

• Lower cost at scale — once utilization is high enough, self-hosting can be cheaper
• More control — model runtime, batching, scheduling, custom kernels, networking, observability
• Data/compliance requirements — stricter isolation, VPCs, on-prem, residency, audit needs
• Advanced performance tuning — latency, throughput, quantization, custom serving stacks

A good rule of thumb

• Prototype / early product / uncertain demand: Replicate
• Predictable high volume / strong infra team / tight margins: Own stack

Cost vs effort

Self-hosting usually becomes attractive when you can keep GPUs busy enough that:

• utilization is consistently high,
• workloads are steady,
• and the engineering time to operate the stack is justified.

If traffic is spiky or hard to predict, managed infrastructure often wins because idle GPU time is expensive.

Middle path

A lot of teams do:

1. Start on Replicate
2. Measure actual traffic, latency, and cost
3. Move hot paths to self-hosted GPUs if needed

If you want, I can help you decide based on your situation—e.g. expected traffic, model size, latency target, budget, and whether this is training or inference.