Container Platform Comparison: Running AI Workloads in Production

AI workloads stress container platforms in specific ways. This guide covers those, drawing on FISTA Solutions' AI enablement infrastructure work.

What should the comparison cover?

Six dimensions, weighted by whether you self-host inference.

Dimension	What to verify	Why it matters
GPU scheduling	Allocation and sharing	Self-hosted inference
Job semantics	Completion, retry, no eviction	Long batch work
Scale-up latency	Image pull and start time	Bursty demand
Autoscaling signals	Queue depth, not just CPU	AI load is not CPU-shaped
Operational burden	Managed or self-run	The dominant cost
Cost model	Idle capacity handling	Utilisation varies

What does GPU scheduling involve?

Allocation, sharing, and awareness of device types.

If you self-host inference, the platform must schedule pods onto machines with suitable accelerators, handle several workloads per device where that is appropriate, and avoid placing work where the device is unavailable.

Support varies, and the gaps are frequently discovered in production. If you do not self-host inference, this dimension does not apply and should not drive the choice. See GPU provider comparison.

Why do long jobs need different semantics?

Because they are work to complete, not services to keep available.

A job embedding a corpus for six hours should run to completion, retry from a checkpoint on failure, and not be evicted by a scheduler rebalancing for service availability.

Check job and cron primitives, eviction behaviour, and whether long-running work can be protected from disruption. See workflow orchestration comparison.

What makes scale-up slow?

Large images and model loading.

AI container images carrying frameworks and dependencies are often several gigabytes. Pulling one before a new instance can serve adds minutes, and loading model weights adds more.

Image caching, pre-pulling, and keeping warm capacity all help. Measure cold start end to end rather than assuming container start time is the whole story.

What should autoscaling react to?

Queue depth and request latency rather than only processor utilisation.

AI workloads waiting on external model calls show low processor use while being fully occupied. Scaling on that signal keeps capacity flat while the queue grows.

Check whether custom metrics can drive scaling, and whether scale-down is conservative enough not to kill long-running work. See AI capacity planning checklist.

What is the operational reality?

Running orchestration well is a specialism.

Upgrades, networking, storage, security policy, and capacity management are ongoing responsibilities requiring people who know the platform. Teams adopting it without that expertise spend disproportionate time on it.

Managed offerings remove much of this at a price. For most teams that trade is correct, and the remaining burden is still non-trivial.

Do you need this at all?

Frequently not.

If you call hosted model APIs rather than self-hosting inference, your AI workload is ordinary application code with unusual latency characteristics. Whatever you already run it on is probably adequate.

Orchestration earns its place with self-hosted inference, substantial batch processing, or an existing platform your team already operates. See serverless platform comparison.

How do you run your own comparison?

Deploy a representative workload — a service and a long batch job — and measure cold start end to end, autoscaling behaviour under a burst, and what happens to the job during a node disruption.

Those three tests cover the AI-specific risks. Conventional platform criteria then decide the rest.

What does switching cost later?

High. Platform migrations touch deployment, networking, storage, and operational practice.

Keeping deployment configuration declarative and avoiding platform-specific features where standard ones exist reduces the cost.

What do people get wrong here?

Adopting orchestration without needing it. GPU scheduling assumed to work. Long jobs treated as services. Autoscaling on processor utilisation. And cold start measured as container start rather than time to serve.

What about self-hosted inference specifically?

It is the case that most justifies a container platform, and it brings the hardest requirements: GPU scheduling, large images, long model load times, and expensive idle capacity.

If you are considering self-hosting, evaluate the platform alongside that decision rather than separately. See open weight vs hosted models for enterprise.

Which should you choose?

Check whether you need orchestration before comparing platforms. If you self-host inference or run substantial batch work, compare on GPU scheduling, job semantics, and cold start, and prefer managed operation unless you have platform engineers.

What should you do first?

Measure your current cold start from scale-up decision to serving traffic. If it is minutes, that is the constraint to address whatever platform you use.

How FISTA Solutions helps

FISTA Solutions builds and operates production AI systems through AI agents, AI enablement, and forward deployed engineering: platform choice matched to whether inference is self-hosted, with cold start measured end to end and long jobs protected from eviction, decisions documented with their reasoning, and handover that leaves your team able to maintain what was delivered. The record is 150+ projects for 50+ companies across 12+ countries.

To run this comparison against your own workload, message FISTA on WhatsApp, or read serverless platform comparison.