Unpredictable demand
Inference and agent workloads can move from idle to saturated quickly, making static capacity expensive or unreliable.
AI platform engineering / Kubernetes implementation
Deploy AI that scales without letting costs scale with it.
CodeCrux designs and implements production Kubernetes platforms for AI systems, agents, model APIs, and inference workloads that need reliability, performance, and cost control.
Kubernetes implementation for AI products / platform foundations / performance and cost optimization
AI PLATFORM / PRODUCTION HEALTHY
kubernetescluster / ai-prod-01
model servingGPU / 68%
agent runtimeCPU / 42%
retrieval APIp95 / 184ms
capacity policyoptimized
MONTHLY CAPACITY SIGNALRIGHT-SIZED
01Production-ready AI platforms
02Performance you can measure
03Cost decisions grounded in data
The AI infrastructure problem
A model demo is not an AI product platform.
AI systems combine spiky inference traffic, expensive compute, stateful data, background jobs, model dependencies, and agents that need to call tools reliably.
Kubernetes gives you a platform. The right implementation makes that platform understandable, resilient, observable, and economical for the workload you actually run.
Expensive compute
GPU and high-memory nodes need workload-aware scheduling, utilization tracking, and clear capacity decisions.
More than one service
Production AI includes APIs, workers, retrieval, model serving, tools, queues, data, and operational controls.
Kubernetes implementation for AI products
Build the platform around the workload.
Start with the product and its operating requirements, then implement the Kubernetes foundation that helps the team ship and run it with confidence.
Design the AI cluster.
Cluster topology, namespaces, node pools, security boundaries, networking, storage, secrets, and environment promotion.
Run inference predictably.
Deploy model APIs and inference services with health checks, rollout controls, autoscaling signals, and capacity-aware scheduling.
Operate agents in production.
Run agent APIs, workers, tool services, queues, scheduled jobs, and retrieval dependencies as observable workloads.
Make releases repeatable.
Infrastructure as code, CI/CD, configuration promotion, monitoring, incident signals, backup, recovery, and upgrade practices.
Performance and cost optimization
Make every unit of compute do more.
Optimization is not one setting. It is a feedback loop across workloads, scheduling, autoscaling, serving behavior, observability, and capacity planning.
Right-size workloads
Set realistic requests and limits from measured behavior so workloads are schedulable without reserving unnecessary capacity.
Improve GPU utilization
Match GPU profiles, node pools, batching, concurrency, and scheduling to the model and traffic pattern.
Scale on useful signals
Use queue depth, request rate, latency, tokens, and utilization instead of CPU alone when scaling AI services.
Optimize inference
Measure model latency, throughput, cold starts, batching, caching, and serving overhead across real requests.
Observe the full path
Connect service health, traces, logs, GPU behavior, cost signals, and user-facing SLOs to the same operating view.
Control capacity cost
Choose node pools, commitments, burst capacity, scale-down policies, and workload placement from evidence.
A production path
One platform for the complete AI runtime.
Kubernetes is the execution layer. The platform should make it easy to see how requests, models, agents, tools, data, and infrastructure behave together.
EXPERIENCE
AI productcopilotagent workflow
AI RUNTIME
model APIsagentsretrievaltools
PLATFORM
KubernetesGPU poolsobservability
OPERATE WITH SIGNALperformance / reliability / cost
How we work
From workload discovery to measurable operations.
The goal is not to add Kubernetes complexity. It is to give the team a reliable, cost-aware operating path for the AI product they need to ship.
01
Assess
Map services, data, compute, traffic, reliability, and cost requirements.
02
Design
Choose cluster boundaries, workload patterns, security controls, and operating signals.
03
Implement
Build the platform, delivery path, observability, and production safeguards.
04
Optimize
Use measured performance and spend to tune capacity and scale what works.
Who this is for
For teams ready to make AI operational.
AI STARTUPSMove from prototype to product
Build an infrastructure foundation that can support the first real customers without premature complexity.
ENTERPRISE AI TEAMSStandardize the runtime
Give multiple AI systems, agents, and teams a repeatable deployment and operations model.
PLATFORM TEAMSReduce operational drag
Make workload behavior, reliability, performance, and cost visible enough to improve continuously.
Before you start
Questions about running AI on Kubernetes.
Why use Kubernetes for AI products?
Kubernetes gives AI teams a repeatable platform for model services, agents, APIs, background workers, data services, and observability with consistent release and scaling controls.
Can you optimize GPU and inference costs?
Yes. The work can cover workload sizing, GPU utilization, node pools, autoscaling, batching, model serving, observability, and capacity decisions.
Can you improve our existing cloud Kubernetes cluster?
Yes. CodeCrux can assess an existing managed Kubernetes environment and improve its platform, delivery, reliability, performance, or cost controls.
Can you deploy AI agents as well as model APIs?
Yes. The platform can support agent runtimes, tool services, APIs, scheduled workers, model endpoints, retrieval services, and their production dependencies.
Start with the workload that matters
Ready to deploy AI with more control over performance and cost?
Book a discovery conversation with CodeCrux. Bring one AI system, one agent workflow, or one infrastructure bottleneck.
Schedule an AI infrastructure conversation