Generative AI applications
Assistants and copilots using hosted foundation models.
Technology / Infrastructure and Edge
Cloud AI
Cloud platforms offer ready-made building blocks for AI, from hosted foundation models to training infrastructure. They can shorten delivery dramatically, provided architecture choices account for data residency, security, cost and portability.
What is Cloud AI?
Short answer
Cloud AI refers to artificial intelligence capabilities delivered through cloud computing platforms, including hosted foundation models, machine learning development and training services, AI APIs for vision, speech, language and documents, vector search, GPU infrastructure and the data, security and operations services that support them.
The major cloud providers offer layered AI services. At the top are ready-to-use APIs for tasks like speech recognition or document extraction. In the middle are platforms for accessing foundation models and building AI applications. At the base are managed machine learning platforms and GPU infrastructure for custom model training and serving.
Cloud AI allows organisations to experiment and scale without buying hardware, but it brings decisions about provider selection, regional availability, data handling terms, identity and network design, cost governance and avoiding excessive dependence on proprietary services.
Related concepts
- Foundation model APIs
- Managed ML platforms
- GPU compute
- Vector search
- MLOps
- Data Engineering
- Edge AI
How it works
How does Cloud AI work?
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Choose the service layer
Decide between ready APIs, model platforms or infrastructure based on customisation needs.
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Set up a secure foundation
Accounts, networks, identity, encryption and logging follow organisational standards.
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Connect data
Data pipelines bring information into cloud storage, warehouses or search indexes.
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Build and deploy
Applications call AI services or deploy custom models with infrastructure as code.
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Operate and optimise
Usage, performance and cost are monitored and tuned.
Applications
Where is Cloud AI useful?
Common applications across industries. Suitability always depends on the specific data, process and constraints.
Document and speech processing
Managed OCR, transcription and translation services.
Custom model training
Scalable training jobs on cloud GPUs.
Analytics with ML
Machine learning inside cloud data warehouses.
Scalable inference
Autoscaling endpoints for applications.
Hybrid AI
Combining on-premises data with cloud AI services.
Limitations
Limitations and when not to use it
Every technology has boundaries. Knowing them early avoids expensive disappointment.
- Specific models and GPUs may not be available in every region.
- Usage-based costs can escalate without controls.
- Proprietary services can create lock-in.
- Latency to cloud regions may not suit real-time edge needs.
- Some regulatory contexts restrict data transfer to public cloud.
Common misconceptions
Cloud AI is always cheaper.
For steady, high-volume inference, dedicated or self-hosted infrastructure can cost less.
Using cloud AI means providers train on your data.
Enterprise terms for major services generally exclude training on customer data, but terms must be checked.
Multi-cloud is always the safest choice.
Multi-cloud adds complexity; portability can often be achieved through architecture instead.
Comparison
Cloud AI hosting options
Options trade control against effort and cost profile.
| Option | Control | Operational effort | Good fit |
|---|---|---|---|
| Ready AI APIs | Low | Very low | Standard tasks such as OCR or transcription |
| Hosted foundation models | Moderate | Low | Generative AI applications |
| Managed ML platforms | High | Moderate | Custom model training and serving |
| Self-managed cloud GPUs | Very high | High | Private model hosting, specialised workloads |
| On-premises or hybrid | Highest | Highest | Strict data control or steady high volumes |
Decision guide
How to evaluate Cloud AI for your organisation
Questions worth answering before investing. They form the basis of our feasibility and proof-of-concept work.
| Question to ask | Why it matters |
|---|---|
| Where must data be stored and processed? | Regional availability and data terms constrain service choices. |
| What existing cloud investments do we have? | Aligning with current platforms reduces cost and complexity. |
| How variable is expected usage? | Variable loads suit managed, usage-based services. |
| How important is portability? | Abstraction layers protect future flexibility. |
| Who manages cost and security governance? | Clear ownership prevents overspend and misconfiguration. |
AARHIT services
Services that apply Cloud AI
How we put this technology to work for organisations.
Advance Technology
Cloud-Based AI Solutions
Build AI workloads on managed cloud services with sensible choices on cost, data residency and scale.
Explore service Technology & EngineeringCloud AI Infrastructure
Design compute, storage, networking and GPU capacity for training and inference with cost and security controls.
Explore service Technology & EngineeringMachine Learning Operations
Version, deploy, monitor and retrain models with repeatable pipelines, so they stay accurate after launch.
Explore service Technology & EngineeringAI Security, Governance and Ethics
Put policies, risk assessments, security controls and accountability around AI systems throughout their life.
Explore serviceRelated technologies
Industries
FAQ
Cloud AI FAQs
What is cloud AI?
Cloud AI is the delivery of AI capabilities, such as foundation models, machine learning platforms, AI APIs and GPU infrastructure, through cloud computing providers.
Which cloud provider is best for AI?
AWS, Microsoft Azure and Google Cloud all offer extensive AI services. The best choice depends on existing investments, required models, regions, skills, security and commercial terms.
Can cloud AI meet Indian data residency needs?
Often yes, using Indian cloud regions, but availability of specific models and services varies and should be verified for each use case.
How can we control cloud AI costs?
Through budgets and alerts, quotas, choosing appropriately sized models, caching, batching, autoscaling and monitoring cost per application or feature.
When should we self-host AI instead of using cloud services?
When strict data control is required, when usage is steady and high enough to justify dedicated infrastructure, or when specific customisation is needed.
Next step
Exploring Cloud AI?
Tell us what you want to achieve. We will help you judge whether this technology is the right fit and how to test it with your own data.