Cloud & AI Infrastructure
Compute, storage, managed Kubernetes, GPU acceleration, and disaster recovery delivered as integrated services — so your engineers build on a platform engineered for hybrid deployment rather than maintaining the infrastructure that runs under it.
Overview
Clevertek Cloud & AI Infrastructure provides the compute, storage, container orchestration, and AI acceleration layer that applications and models run on top of — delivered as services rather than hardware procurement cycles. Virtual machines, block/object/file storage, and managed Kubernetes clusters are provisioned through a unified control plane with IaC templates and GitOps pipelines from day one. GPU accelerators are available on demand for training and inference workloads, eliminating the capital commitment and utilisation risk of self-owned hardware. The AI Cloud Platform adds notebook workspaces with pre-built ML environments, managed inference endpoints with autoscaling, and a data platform that connects to existing sources via ETL/ELT pipelines — unifying the experiment and serving layers in one domain. The entire stack is hybrid by design: on-premise infrastructure, public cloud resources, and edge sites operate under the same management plane, policy framework, and cost-governance model rather than as three separate estates with three invoices. Automated backups with cross-region replication and tested disaster recovery runbooks ensure that availability is validated through exercise, not assumed in documentation. FinOps controls per team and per workload provide cost observability that prevents bill shock at month end, because surprise cloud spend is the failure mode that undermines the entire migration business case.
Capabilities
What's included as part of this solution.
Compute & Storage
Virtual machines with right-sized performance tiers, container orchestration via managed Kubernetes, and block, object, and file storage with automated lifecycle policies that tier hot data on NVMe flash and cold data on HDD or archive object storage. Encryption at rest (AES-256) and in transit (TLS 1.3) applies across every tier by default.
- Right-sized compute tiers with burst and reserved capacity options
- Automated storage lifecycle rules from NVMe to cold archive
- AES-256 encryption at rest and TLS 1.3 in transit across all tiers
Managed Kubernetes
Production-grade Kubernetes clusters with a fully managed control plane — automated patching, certificate rotation, and etcd backup — plus node autoscaling (Cluster Autoscaler, HPA, VPA) and GitOps-ready pipelines (Flux/ArgoCD). A merge-to-main workflow triggers deployments without shell-access drift or manual upgrade procedures.
- Managed, auto-patched control plane with etcd backup automation
- Node autoscaling via Cluster Autoscaler with HPA/VPA policies
- GitOps pipeline integration (Flux/ArgoCD) for declarative deployments
GPU as a Service
On-demand GPU accelerators for training and inference — provisioned per-hour for experimentation, or reserved for production pipelines — with multi-GPU node configurations (NVLink/NVSwitch) for distributed training. You consume capacity during the workload window and release it on completion, eliminating the depreciation line of self-owned accelerators that sit idle between experiments.
- Per-hour on-demand or reserved-capacity GPU provisioning
- Multi-GPU nodes with NVLink interconnects for distributed training
- Consumption-based billing with no hardware procurement or retirement
AI Cloud Platform
An integrated AI/ML platform spanning development through production: Jupyter-based notebook workspaces with pre-installed frameworks (PyTorch, TensorFlow, CUDA toolkit), managed inference endpoints with request-based autoscaling, and a governed data platform that connects to existing databases, data lakes, and streaming sources. The experimental lab and production serving layer share the same infrastructure pool and access controls.
- Notebook workspaces with pre-configured ML frameworks and GPU scheduling
- Managed inference endpoints with request queuing and autoscaling
- Governed data platform with lineage, catalog, and quality controls
Sovereign AI & GPU Cloud
India-located sovereign AI and GPU infrastructure for workloads with data residency or sovereignty requirements. Compute and storage run in isolated zones with customer-managed encryption keys (CMEK), private network segments, and no cross-border data flow by default — turning a complex regulatory compliance question into a short architectural answer.
- In-India data residency with geographic zone isolation
- Customer-managed encryption keys (CMEK) with HSM-backed key storage
- Private, network-isolated compute and storage zones
Backup & DR
Automated, schedule-driven backups with cross-region replicable copies and documented, regularly tested failover runbooks. Recovery RTO/RPO targets are validated through exercise rather than assumed from architecture diagrams, so a production outage becomes a procedural restore operation rather than a reconstruction effort.
- Scheduled, policy-driven automated snapshots with retention management
- Documented failover runbooks with periodic recovery exercise validation
- Cross-region replicated copies for geographic redundancy
Where it's used
Real-world scenarios where this solution delivers measurable outcomes.
Lift, then modernise
Migrate existing VM workloads to the platform on day one with zero re-architecture, then incrementally refactor application by application into containers and managed services on the same Kubernetes infrastructure. Modernisation proceeds at your business-determined pace rather than through a high-risk big-bang migration that threatens operational continuity.
A training run without a hardware purchase
Provision a multi-GPU training node for a fine-tuning sprint, execute the job, and release the capacity upon completion. You pay for the compute hours the training consumed rather than a depreciation line for GPU hardware that sits idle between experiments.
Keep regulated data in the country
For workloads with data residency or sovereignty obligations, deploy in India-located zones with customer-managed encryption keys. Data and the models trained on it remain within the jurisdiction for which you are accountable, satisfying regulatory requirements without architectural compromise.
Frequently asked questions
Are we locked into one public cloud?
No. Our platform is hybrid by design — your on-premise estate, any public cloud, and edge sites connect into a unified management operating model. You retain workload portability and are not held hostage to a single provider's pricing changes or platform roadmap.
Is Kubernetes included, and who runs it?
Yes. We provide managed Kubernetes clusters with auto-patched control planes, node autoscaling, and GitOps-ready deployment pipelines. We operate the platform so your engineering team deploys applications rather than maintaining cluster infrastructure.
Do I need to buy GPUs?
No. GPU accelerators are available on demand — per-hour for experimentation or reserved for production — and are released upon workload completion. You pay only for the compute you consume.
Can regulated workloads stay in India?
Yes. Our sovereign AI and GPU cloud runs in India-located zones with data residency enforcement, customer-managed encryption keys, and private isolated network segments for workloads with residency or sovereignty obligations.
How do we keep cloud cost from spiralling?
FinOps controls provide per-team and per-workload cost visibility, budget alerts, and right-sizing recommendations. Spend is a governed decision you make based on data rather than a number you discover at month-end reconciliation.
Why choose Clevertek for Cloud & AI Infrastructure
One accountable partner for end-to-end solutions — here is what buying from us actually gets you.
Hybrid by design
On-premise, public cloud, and edge resources operate as a single management domain with unified policy — not as three separate estates with three toolchains, three cost models, and three invoices.
Zero GPU CapEx
GPU accelerators provisioned on demand and released when the workload completes. You pay for consumed compute, not for shelfware capacity that depreciates while idle.
Predictable spend
FinOps controls deliver cost observability per team and per workload. Surprise bills become deliberate trade-offs you approved in advance rather than financial discoveries at month-end reconciliation.
Disaster recovery built in
Cross-region replication with failover procedures validated through periodic exercise, so the worst production day is a recovery procedure your team has executed before rather than a novel reconstruction effort.
Related solutions
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