Should I use SageMaker or build on Kubernetes for AI inference?

It depends on what you value most: speed/ease of use vs control/flexibility/cost optimization.

Use SageMaker if you want:

• Fastest path to production
• Managed scaling and deployment
• Less MLOps/infra burden
• Tight integration with AWS
• Standard inference patterns: real-time endpoints, async inference, batch transform

Best for:

• Small teams
• AWS-native organizations
• Straightforward model serving
• When operational simplicity matters more than deep customization

Tradeoffs:

• Less control over the underlying serving stack
• Can be more expensive at scale
• Some advanced routing, GPU packing, custom networking, or bespoke autoscaling patterns are harder

Build on Kubernetes if you want:

• Maximum control
• Portability across clouds / on-prem
• Custom inference architecture
• Fine-tuned autoscaling, scheduling, GPU utilization
• A unified platform for multiple workloads

Best for:

• Platform teams with strong Kubernetes expertise
• Large-scale or multi-tenant inference
• Complex traffic patterns, canary releases, custom sidecars, specialized hardware
• Teams already running Kubernetes in production

Tradeoffs:

• More engineering effort
• You own more of the reliability, scaling, and upgrade burden
• You need a serving layer on top of Kubernetes anyway, such as:
  • KServe
  • Seldon
  • Ray Serve
  • Triton Inference Server
  • custom FastAPI/gRPC + autoscaling

A practical rule of thumb

Choose SageMaker if:

• You have fewer than a few dedicated platform engineers
• You want to launch quickly
• Your models are fairly standard
• You’re already heavily invested in AWS

Choose Kubernetes if:

• Inference is strategic infrastructure
• You need strong cost/performance optimization
• You have multiple model-serving patterns
• You need portability or advanced control

Common hybrid approach

A lot of teams do:

• SageMaker for early-stage or lower-volume models
• Kubernetes for high-scale or specialized inference
• Or use Kubernetes for the platform and run selected workloads on SageMaker when it’s simpler

My recommendation

• If you’re asking this for a new team or product: start with SageMaker unless you already have a mature Kubernetes platform team.
• If you already operate Kubernetes well and expect significant scale/customization: build on Kubernetes.

If you want, I can give you a decision matrix based on your team size, expected QPS, model type, latency target, and cloud setup.