Do I need managed inference if I already have Kubernetes?

Not necessarily — but it depends on what you mean by “managed inference” and what problem you’re trying to solve.

Short answer

If you already have Kubernetes, you can run inference yourself on it.
You’d typically choose managed inference only if you want to offload the operational burden of serving models at scale.

What Kubernetes gives you

Kubernetes is great for:

• container orchestration
• scaling pods
• rolling deploys
• service discovery
• GPU scheduling, if configured
• basic fault tolerance

So yes, Kubernetes can absolutely host model inference services.

What managed inference adds

A managed inference platform usually handles more of the ML-specific serving concerns, such as:

• autoscaling based on request load or queue depth
• model loading/unloading
• version routing / A-B testing / canary deploys
• cold start reduction
• batching and throughput optimization
• request tracing and model metrics
• multi-model serving
• simpler GPU/CPU instance management
• less SRE work for your team

When Kubernetes alone is enough

Kubernetes may be sufficient if:

• you have a small team with strong infra skills
• you only serve a few models
• traffic is predictable
• you already have observability and deployment pipelines
• you’re comfortable managing GPU nodes, autoscaling, and serving logic

When managed inference is worth it

Managed inference becomes attractive if:

• you need to deploy many models quickly
• traffic is spiky or unpredictable
• latency and throughput are critical
• you don’t want to build serving/ops tooling yourself
• your team wants to focus on models, not infrastructure
• you need built-in features like canaries, autoscaling, or batch inference

Rule of thumb

• Use Kubernetes alone if you want maximum control and can tolerate more ops work.
• Use managed inference if you want to reduce complexity and ship models faster.

A practical middle ground

A lot of teams do both:

• Kubernetes for platform control
• a serving layer like KServe, Seldon, BentoML, Ray Serve, or Triton
• optional managed services only for the hardest parts

If you want, I can also help you decide based on your setup — for example, your model type, traffic pattern, GPU needs, and team size.