Should I switch from AWS to a dedicated GPU provider?

Maybe — but it depends on why you’re using AWS and what your GPU workload looks like.

Switch to a dedicated GPU provider if:

• GPU cost is your main pain point and you’re running GPUs consistently.
• You want simpler, more predictable pricing than AWS.
• You don’t need lots of adjacent AWS services (VPC complexity, IAM-heavy setups, etc.).
• You’re training/inferencing on single-purpose workloads where raw GPU price/performance matters most.
• You can tolerate a bit less ecosystem convenience for lower compute cost.

Stay on AWS if:

• You rely on tight integration with S3, EKS, IAM, CloudWatch, RDS, Lambda, etc.
• Your workload is bursty or intermittent, so managed cloud elasticity matters more than lowest unit cost.
• You need enterprise compliance, networking, security, or governance features.
• Your team already knows AWS well and switching would add operational overhead.
• You use a mix of services beyond GPU compute, not just ML workloads.

A practical rule of thumb

• Mostly GPU compute, especially at steady utilization? Dedicated GPU provider is often cheaper.
• Lots of surrounding infrastructure and enterprise requirements? AWS is often worth the premium.

What to compare before deciding

Look at:

• All-in hourly cost: GPU + CPU/RAM + storage + bandwidth
• Availability/reliability
• Startup time / provisioning speed
• Driver/CUDA support
• Storage and data transfer costs
• How much engineering time migration will take

Common hybrid approach

A lot of teams do this:

• Keep AWS for storage, orchestration, and app backend
• Use a dedicated GPU provider for training or batch inference
• Route workloads based on cost/performance

My short recommendation

If your workload is GPU-heavy and steady, it’s worth benchmarking a dedicated GPU provider.
If AWS is only “expensive” because of GPU instances but you depend on the ecosystem, a hybrid setup is often the best move.

If you want, I can help you make the decision with a simple side-by-side comparison based on your workload.