Slow AI delivery on self-managed infrastructure
Teams spend months building platforms before delivering any AI capability.
01 / Advance Technology
Cloud-Based AI Solutions
Cloud platforms now offer managed services for almost every part of an AI solution: models, vector search, document understanding, speech, training and serving. Used well, they shorten delivery considerably. Used carelessly, they create cost, security and lock-in problems. We design cloud AI solutions that get the benefits without the surprises.
At a glance
- Service
- Cloud-Based AI Solutions
- Pillar
- Advance Technology
- Typical starting point
- Cloud AI architecture review
- Typical first phase
- 2 to 4 weeks for architecture; solutions in 6 to 12 weeks
- Key technologies
- Cloud AI, Large Language Models, Generative AI
Durations depend on scope, data readiness and integration needs.
What is Cloud AI Solutions?
Short answer
Cloud-based AI solutions are AI applications and workflows built primarily on managed cloud services, such as hosted foundation models, machine learning platforms, AI search and document services, combined with cloud data, security and integration services, to deliver AI capabilities without building and operating all infrastructure in-house.
The main cloud providers, AWS, Microsoft Azure and Google Cloud, each offer extensive AI services. Selecting among them, and deciding when to use managed services versus open-source components, depends on existing investments, data location, security requirements, skills and cost.
We design solutions that use managed services where they save effort and reduce risk, while keeping important parts portable. Data residency, including requirements to keep data in Indian regions, network isolation, identity integration and cost controls are addressed in the architecture from the beginning.
Business problems
What problems does Cloud AI Solutions solve?
These are the situations where organisations most often find this service worthwhile.
Unclear cloud AI choices
Too many overlapping services make it hard to choose an architecture.
Data residency and compliance concerns
Sensitive data must stay in approved regions and under organisational control.
Unexpected cloud AI bills
Model usage and GPU resources grow without budgets or visibility.
Use cases
Where can Cloud AI Solutions be used?
Representative applications. The right use case depends on your processes, data and constraints.
| Area | Example application |
|---|---|
| Knowledge assistants | Retrieval-augmented assistants using cloud model services and managed search. |
| Document automation | Cloud document AI services combined with workflow and storage. |
| Contact centres | Speech-to-text, summarisation and quality analytics for customer calls. |
| Forecasting | Managed ML platforms for demand and capacity forecasting. |
| Media and content | Image, video and translation services for content operations. |
| Analytics | AI-assisted analytics over cloud data warehouses. |
Who should consider it
- Organisations already using AWS, Azure or Google Cloud
- Teams that want to deliver AI capabilities quickly
- Regulated organisations needing clear data controls
- Companies concerned about cloud AI costs
How AARHIT approaches it
How we deliver Cloud AI Solutions
Each stage ends with something you can review, so decisions are made on evidence rather than on promises.
Stage 1 of 6
Requirements and constraints
We gather use cases, data sensitivity, residency requirements, existing cloud investments and budgets.
Output: requirements and constraints summary.
Stage 2 of 6
Service and architecture selection
Managed and open-source options are compared on capability, cost, security and portability.
Output: reference architecture and service choices.
Stage 3 of 6
Landing zone and security
Accounts or subscriptions, networking, identity, encryption and logging are set up to organisational standards.
Output: a secure cloud foundation for AI workloads.
Stage 4 of 6
Solution build
The AI solution is built with infrastructure as code, evaluation sets and monitoring.
Output: a working solution in a non-production environment.
Stage 5 of 6
Cost and performance tuning
We test at expected load, tune model choices, caching and scaling, and set budgets and alerts.
Output: performance results and cost projections.
Stage 6 of 6
Release and operations
The solution is released with runbooks, dashboards and support arrangements.
Output: production solution and operations handover.
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 |
|---|---|
| AWS | Amazon Bedrock, SageMaker, Textract, Transcribe, OpenSearch |
| Microsoft Azure | Azure OpenAI Service, Azure AI Foundry, AI Search, Document Intelligence |
| Google Cloud | Vertex AI, Gemini models, Document AI, BigQuery ML |
| Open components | Open-weight models, LangChain or LlamaIndex, open vector databases |
| Platform | Kubernetes, serverless functions, API gateways, infrastructure as code |
| Governance | Cloud cost management, policy as code, centralised logging |
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.
- Faster delivery of AI capabilities
- Clear control over where data is stored and processed
- Predictable and monitored AI operating costs
- Solutions aligned with existing cloud and security standards
How long does implementation generally take?
Typical timeline
Architecture and foundation work typically takes 2 to 4 weeks. A first cloud AI solution often takes 6 to 12 weeks to reach production, depending on data integration and security review requirements.
What determines complexity?
- Data sensitivity and residency requirements
- Existing cloud maturity and landing zone
- Integration with on-premises systems
- Expected usage volume and latency needs
- Multi-cloud or hybrid requirements
Implementation
Implementation considerations and risks
Most difficulties in this kind of work are predictable. Planning for them early keeps delivery on track.
Check model availability by region
Not every model or service is available in every region, which matters for data residency.
Design identity and access carefully
AI services should use managed identities and least-privilege access, not shared keys.
Set budgets before launch
Usage-based AI services can scale costs quickly; budgets, quotas and alerts prevent surprises.
| Risk to consider | How we manage it |
|---|---|
| Lock-in to one provider | Abstraction layers for models and portable components for critical logic. |
| Data leaving approved boundaries | Private networking, regional deployment and data handling agreements reviewed. |
| Cost overruns | Cost modelling, quotas, caching, model tiering and monitoring. |
| Service changes by providers | Version pinning, monitoring of provider announcements and regression testing. |
FAQ
Questions about Cloud AI Solutions
Which cloud is best for AI?
There is no single best choice. AWS, Azure and Google Cloud all offer strong AI services. The right choice depends on your existing cloud use, required models, data location, security needs, skills and commercial terms.
Can our data stay in India?
In many cases yes. The major providers operate Indian regions, but specific models and services vary by region. We check availability for your requirements during architecture design.
Do cloud AI providers train models on our data?
Enterprise offerings from major providers generally state that customer data is not used to train their foundation models, but terms vary. We review the relevant terms and configure services accordingly.
Is cloud AI more expensive than running our own infrastructure?
For variable or moderate workloads, managed cloud services are usually more cost-effective. For steady, high-volume inference, dedicated infrastructure can be cheaper. We model both where relevant.
Can you work in a hybrid environment?
Yes. Many solutions keep sensitive data on-premises and use cloud services for specific capabilities, connected through secure networking.
Next step
Considering Cloud AI Solutions 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.