GPU platform design
On-premise, private cloud or hybrid GPU node pools on OpenShift or Kubernetes with scheduling, quotas, utilisation reporting and cost controls per workload.
AI & GPU Infrastructure
Governed GPU capacity, model serving, MLOps, data pipelines and vector infrastructure inside your perimeter, from pilot to regulated production.
04 / Become AI-ready
Overview
AI pilots stall when they meet data residency, GPU scarcity, legacy integration and audit requirements. Cloudlit builds and operates the infrastructure layer that lets banks, government and AI companies run models in production while keeping data, prompts, embeddings and logs under their control, and provides the production pipelines that turn LLM experiments into governed services.
What Cloudlit delivers
Model serving and inference gateways
Internal inference gateways, model serving with autoscaling and rate limits, and governed access to core systems through service mesh and message queues.
MLOps and model registry
Fine-tuning pipelines, model versioning, promotion gates and reproducible environments so models move to production with clear ownership.
LLM and data pipelines
Ingestion, crawling and enrichment pipelines that connect enterprise and web data to LLM APIs with deduplication, schema normalisation and job tracking.
Vector and data infrastructure
Vector stores, embedding pipelines and secure access to enterprise data sources within the classification boundary.
AI security and auditability
Immutable prompt, access and model event logging exported to the SIEM, segmentation, client-held keys and policy enforcement.
Resilience
DR site, tested failover and backup for models, registries and vector data with defined RPO and RTO.
Operations
GPU health monitoring, capacity reviews, patching and 24×7 support for the platform under SLA.
Outcomes
- A production AI platform that satisfies residency and audit requirements with no external model APIs where policy forbids them
- Governed GPU capacity shared safely across teams with cost per inference visible
- Models that move from pilot to production with clear ownership and rollback
- A defensible security posture for AI workloads, evidenced for regulators
Why Cloudlit for this
Inside the perimeter
Production AI platforms delivered where data, prompts, embeddings, models and logs never leave the client-controlled environment.
Platform, not just GPUs
Serving, registries, pipelines, security and observability are delivered as one operated layer, not a pile of components.
Partner ecosystem
Premium AI delivery partner to wAI Industries and certified Red Hat partner for OpenShift AI, with vendor escalation when it matters.
Operated at scale
20+ production clusters and 50+ GPU nodes provisioned for AI platforms in banking, with monthly service reporting.
How an engagement runs
- AI readiness assessment covering data, capacity, integration and audit
- Platform architecture and controls design with your CISO and architecture board
- PoC on governed infrastructure with production controls from the start
- Production build, handover and managed operations
Frequently asked questions
Can AI workloads run fully inside a bank's perimeter?
Yes. Cloudlit has delivered production AI platforms where data, prompts, embeddings, models and logs never leave the client-controlled environment, using dedicated GPU node pools and an internal gateway.
Does Cloudlit build the models?
Cloudlit builds and operates the infrastructure layer: GPU capacity, serving, registries, pipelines, security and observability. Data science teams and AI product companies own the models.
Can Cloudlit connect existing systems to LLM APIs safely?
Yes. We build governed pipelines and gateways that authenticate every call, log every event, normalise and deduplicate data, and keep API keys, transit and access under IAM control.
Bring us the AI Infrastructure problem that matters most.
Cloudlit brings architecture, platform engineering, security and operations together so enterprise teams can move from complexity to a controlled production environment.