Cloud & Hybrid AI Deployment

The cloud is where most enterprises meet AI — and where cost, security and operational discipline decide whether AI scales or stalls. We design cloud and hybrid platforms that give teams fast, self-service access to AI capacity without surrendering governance, cost control or portability.

From landing zones and GPU capacity strategy to Kubernetes platforms and CI/CD for models, Novera Intelligence builds the deployment layer that makes AI workloads boring to operate and predictable to bill.

The Challenges We Solve

• Uncontrolled AI spend

  Idle GPU instances, over-provisioned endpoints and duplicated experiments inflate bills silently. We implement rightsizing, scheduling, cost attribution and governance that make spend visible and deliberate.

• Fragmented environments

  Teams deploying into different accounts, regions and tooling stacks create shadow platforms. We consolidate on landing zones and shared platform services with golden-path templates.

• Hybrid complexity

  Data gravity, residency and latency requirements pull workloads on-premises while burst capacity lives in the cloud. We design the scheduling, networking and data layer that makes hybrid coherent.

• Security and identity gaps

  AI services introduce new identities, data flows and egress paths. We integrate AI deployments into your IAM, network and secrets architecture rather than bolting it on.

What We Deliver

• Cloud landing zones

  Account structure, networking, identity, guardrails and policy-as-code that give teams safe, fast access to cloud AI resources.

• Public cloud AI platforms

  Deployment architecture across AWS, Microsoft Azure and Google Cloud, with GPU instance selection matched to workload profiles and cost targets.

• Private, hybrid & multi-cloud

  Reference architectures for on-premises, private cloud and multi-cloud estates, with workload placement policy and consistent operations.

• Kubernetes AI platforms

  Cluster design, GPU scheduling (including fractional allocation), autoscaling and multi-tenancy for serving and training on Kubernetes.

• Infrastructure as code

  Terraform-based provisioning with environment parity, review workflows and drift management.

• CI/CD for AI

  Pipelines that move models, configurations and applications from commit to production with automated evaluation gates.

• Cloud networking & security

  Private connectivity, egress control, IAM integration and secrets management for AI services and endpoints.

• Observability

  Metrics, logs and tracing across infrastructure, serving layers and model behavior, wired into your existing observability estate.

• Disaster recovery & resilience

  Recovery objectives, backup strategy and failover design for AI platforms and their data dependencies.

• Cloud cost optimization

  Commitment strategy, rightsizing, scheduling and architectural cost reduction — measured, not guessed.

Architecture in Layers

One deployment layer, every destination

1. Landing zone

   Accounts, networking, identity and guardrails with policy-as-code from day one.

2. Compute

   GPU instance and node-pool strategy across cloud and on-premises, scheduled to demand.

3. Platform services

   Kubernetes, storage, registries, vector and model serving shared across teams.

4. Delivery

   Infrastructure-as-code and CI/CD pipelines moving models and applications with evaluation gates.

5. Governance

   Cost attribution, quota, security policy and audit across every environment.

How We Engage

Delivered through a disciplined lifecycle that carries the initiative from assessment to dependable, optimized operation.

1. 01

   Estate review

   Current cloud footprint, workloads, cost baselines and security posture.

2. 02

   Platform design

   Landing zone, network, identity and Kubernetes architecture with workload placement policy.

3. 03

   Foundation build

   Landing zone and shared platform services deployed as code, validated with a pilot workload.

4. 04

   Workload migration

   AI workloads moved onto the platform with pipelines, monitoring and cost controls enabled.

5. 05

   Operate & optimize

   Cost reviews, rightsizing cycles and platform evolution as demand grows.

Deliverables You Can Expect

• Landing zone and network architecture
• GPU capacity and instance selection strategy
• Kubernetes platform design and build
• Terraform modules and pipeline code
• Cost optimization plan and attribution model
• Security, IAM and DR design documents

Use Cases

• Enterprise AI cloud foundation

  An enterprise standardizing its AI estate on a governed landing zone with shared GPU capacity and golden-path deployment.

• Hybrid burst strategy

  Training on-premises with elastic burst to cloud, orchestrated by one scheduling layer and one cost view.

• Cloud modernization

  Legacy ML estates moved to modern managed services with IaC, observability and cost governance rebuilt properly.