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Cloud Engineering · Sub-service
Kubernetes & Containers
Container platforms for scalable APIs, workers, and inference sidecars.
Summary
Container platforms for scalable APIs, workers, and inference sidecars.
Problem
What teams are stuck with
Pets-as-servers cannot scale AI workers or absorb traffic spikes.
Clusters without policy become expensive chaos.
Offerings
What we deliver
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01
Containerization and Helm/Kustomize
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02
Cluster design and hardening
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03
Autoscaling policies
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04
Service mesh where warranted
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05
GPU/inference workload patterns
Approach
How we work it
Right-size clusters; separate critical and batch workloads.
GitOps for desired state; network policies by default.
Autoscale on product and queue metrics — not guesses.
Outcomes
What good looks like
- Elastic capacity for APIs and AI workers
- Repeatable cluster configuration
- Better utilization and cost control
Related
Keep exploring
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