High and unpredictable GPU costs
GPU instances run idle or are oversized for workloads.
05 / Technology & Engineering
Cloud AI Infrastructure
AI workloads place unusual demands on infrastructure: expensive GPUs, large datasets, bursty training jobs, latency-sensitive inference and models that must sometimes stay within private networks. We design and build infrastructure that meets those demands without runaway costs or security gaps.
At a glance
- Service
- Cloud AI Infrastructure
- Pillar
- Technology & Engineering
- Typical starting point
- Workload and infrastructure assessment
- Typical first phase
- 2 to 4 weeks for design; platform in 6 to 12 weeks
- Key technologies
- Cloud AI, MLOps, Large Language Models
Durations depend on scope, data readiness and integration needs.
What is AI Infrastructure?
Short answer
Cloud AI infrastructure is the compute, storage, networking, orchestration and security foundation used to train, fine-tune and serve AI models, including GPU and accelerator resources, container platforms, model serving systems, data storage and the cost, access and monitoring controls around them, whether in public cloud, private cloud, on-premises or hybrid environments.
Infrastructure decisions for AI have significant cost and performance consequences. Choosing between managed model APIs, serverless GPU services, dedicated cloud GPUs or on-premises hardware depends on workload patterns, data sensitivity, latency and utilisation.
We start from workloads: what will be trained, fine-tuned or served, at what volume and with what constraints. Then we design infrastructure with appropriate GPU selection, autoscaling, inference optimisation, networking, identity and cost governance. Where organisations need private hosting of open-weight models, for data control or regulatory reasons, we design and operate those environments securely.
Business problems
What problems does AI Infrastructure solve?
These are the situations where organisations most often find this service worthwhile.
Data cannot leave controlled environments
Regulatory or contractual requirements prevent use of public AI APIs.
Slow inference
Models respond too slowly for user-facing applications.
Ad hoc infrastructure
AI environments are built by hand, inconsistently and without security standards.
GPU capacity shortages
Teams cannot obtain accelerators when needed.
No visibility of AI spend
Costs cannot be attributed to teams, projects or features.
Use cases
Where can AI Infrastructure be used?
Representative applications. The right use case depends on your processes, data and constraints.
| Area | Example application |
|---|---|
| Private LLM hosting | Serving open-weight models within your cloud account or data centre. |
| Training and fine-tuning clusters | Scalable GPU environments for model training jobs. |
| Inference platforms | Autoscaling model serving with optimised runtimes and batching. |
| Hybrid AI | On-premises GPUs for sensitive workloads combined with cloud capacity for bursts. |
| Edge-to-cloud | Infrastructure supporting models deployed on edge devices with central management. |
| Research environments | Shared, governed GPU workspaces for data science teams. |
Who should consider it
- Organisations hosting or fine-tuning their own models
- Regulated industries with strict data control needs
- Teams facing high or unpredictable AI infrastructure costs
- Platform teams supporting multiple AI projects
How AARHIT approaches it
How we deliver AI Infrastructure
Each stage ends with something you can review, so decisions are made on evidence rather than on promises.
Stage 1 of 6
Workload assessment
We characterise training, fine-tuning and inference workloads, data sensitivity, latency and growth.
Output: workload profile and requirements.
Stage 2 of 6
Hosting strategy
Options such as managed APIs, serverless GPUs, dedicated cloud, on-premises or hybrid are compared on cost, control and performance.
Output: hosting strategy with cost models.
Stage 3 of 6
Architecture design
Compute, storage, networking, container orchestration, serving and security are designed.
Output: infrastructure architecture.
Stage 4 of 6
Build with infrastructure as code
Environments are provisioned through code with policies, identity and monitoring.
Output: reproducible infrastructure.
Stage 5 of 6
Optimisation
Inference runtimes, batching, quantisation, autoscaling and scheduling are tuned for cost and latency.
Output: performance and cost benchmarks.
Stage 6 of 6
Operations
Monitoring, capacity planning, cost reporting and incident procedures are established.
Output: operated platform with runbooks.
Technology
What technologies are involved?
We choose tools on fit, cost, security and your existing estate. These are typical options, not fixed commitments.
Learn more about the underlying technologies
| Layer | Typical options |
|---|---|
| Compute | NVIDIA GPUs across cloud providers, AWS Inferentia and Trainium, Google TPUs |
| Orchestration | Kubernetes, Ray, Slurm, managed ML compute |
| Serving | vLLM, Text Generation Inference, NVIDIA Triton, TensorRT-LLM |
| Storage | Object storage, high-throughput file systems, vector databases |
| Networking and security | Private endpoints, VPC design, zero-trust access, encryption |
| Cost and observability | FinOps tooling, GPU utilisation monitoring, Prometheus, Grafana |
Outcomes
Potential business outcomes
Targets are agreed with you during scoping and measured against a baseline. We do not quote improvement figures before we have seen your process and data.
- Right-sized AI infrastructure with controlled costs
- Private model hosting that meets data control requirements
- Lower latency for AI-powered applications
- Reproducible, secure environments
- Clear attribution of AI infrastructure spend
How long does implementation generally take?
Typical timeline
Workload assessment and hosting strategy usually take 2 to 4 weeks. Building a production AI infrastructure platform typically takes 6 to 12 weeks, with optimisation continuing as workloads evolve.
What determines complexity?
- Model sizes and workload volumes
- Private or hybrid hosting requirements
- Latency and availability targets
- Security and compliance standards
- Number of teams and projects supported
Implementation
Implementation considerations and risks
Most difficulties in this kind of work are predictable. Planning for them early keeps delivery on track.
Utilisation drives cost
Idle GPUs are expensive. Scheduling, autoscaling and sharing matter as much as instance prices.
Managed APIs versus self-hosting
Self-hosting gives control but adds operational responsibility. The break-even depends on volume and requirements.
Check regional GPU availability
Accelerator availability varies by region and can affect data residency plans.
| Risk to consider | How we manage it |
|---|---|
| Cost overruns | Budgets, quotas, autoscaling, utilisation monitoring and regular reviews. |
| Security misconfigurations | Infrastructure as code with policy checks, private networking and least privilege. |
| Capacity constraints | Reservations, multi-region options and workload prioritisation. |
| Operational burden of self-hosting | Managed components where possible, automation and clear operating procedures. |
FAQ
Questions about AI Infrastructure
Should we host our own LLM or use an API?
APIs are simpler and cost-effective for moderate or variable use. Self-hosting open-weight models suits strict data control needs, very high steady volumes or customisation requirements. We model cost and risk for both.
What GPUs do we need for AI?
It depends on model size, whether you are training or serving, latency needs and volume. Many inference workloads run well on mid-range GPUs with optimised runtimes, while training large models needs high-memory accelerators.
How can we reduce AI infrastructure costs?
Common measures include right-sizing instances, autoscaling to zero when idle, batching requests, quantising models, using smaller models where adequate, spot or reserved capacity and monitoring utilisation.
Can AI infrastructure be hosted in India?
Yes. Major cloud providers offer GPU capacity in Indian regions, and on-premises or colocation options are available where required. Availability of specific accelerators varies.
What is vLLM?
vLLM is an open-source inference engine for large language models that improves throughput and memory efficiency, making self-hosted model serving faster and more cost-effective.
Next step
Considering AI Infrastructure for your organisation?
Share the process, product or decision you have in mind. We will ask the right questions, outline options and suggest a sensible first step.