Production Cloud for Enterprise AI

The right answer depends on your existing stack, AI use case, and compliance requirements. AWS has the broadest service coverage and competitive compute costs in the ap-south-1 (Mumbai) region — the default starting point for most new deployments. Azure is the right call if your organisation already uses Microsoft Dynamics 365, Office 365, or Azure AD: managed identity integration across services significantly reduces credential management complexity. GCP's Vertex AI and BigQuery are the strongest managed offerings for teams building heavily on Google's AI infrastructure. For data residency: all three providers have India regions (AWS ap-south-1, Azure Central India, GCP asia-south1). For DPDP compliance, the provider is less important than configuration — we've delivered compliant deployments on all three. We help you evaluate during the discovery sprint rather than picking based on vendor marketing.

A cloud migration has four phases. Assessment (2 weeks, fixed price): we inventory your current infrastructure, map dependencies, identify migration risks, and produce a migration runbook with a timeline and cost estimate. Pilot migration (4–6 weeks): we migrate one non-critical workload to validate the runbook, test backup and rollback procedures, and find infrastructure gaps before the bulk migration. Bulk migration (8–20 weeks depending on number of services and whether re-architecture is needed alongside migration). Post-migration stabilisation (4 weeks): monitoring, cost right-sizing, and performance tuning. Total calendar time typically runs 16–28 weeks for a mid-size enterprise migrating 20–50 services. The biggest variable is organisational: how quickly your application teams can validate migrated workloads.

AI agents and ML pipelines have different infrastructure requirements than web applications. Key components: inference compute — GPU instances or managed inference endpoints (AWS SageMaker, Azure AI Foundry, Vertex AI) sized for your model and latency requirements; vector database — for RAG retrieval, options include Pinecone, Weaviate, pgvector on RDS, or managed equivalents on each cloud; high-throughput message queues — for agent event loops and async tool call processing (SQS, Azure Service Bus, Pub/Sub); cost guardrails — inference spend can spike unpredictably at scale, requiring budget alerts and automatic endpoint scaling-to-zero when idle; and networking — agents calling internal systems need private connectivity (VPC endpoints, private link) rather than public internet routes. We architect these components during the discovery sprint, selecting managed services where they reduce operational burden.

Data residency — keeping specific data categories within Indian borders — is required by several regulations: RBI mandates that payment system data be stored in India; SEBI has data localisation requirements for market intermediaries; IRDAI requires insurance data in India. On AWS, Azure, and GCP, data residency is achieved through region selection and service configuration. Cloud services are global by default — some services (CDN, DNS, global load balancers) pass data through international infrastructure even when primary compute is in India. We audit your data flows, identify which services require region-pinning and which cross-region services need alternatives, and implement the controls required for your specific regulatory obligations.

Cloud cost optimisation is the process of identifying and eliminating unnecessary spend without reducing capability. Common sources of overspend: compute instances sized for peak load that run continuously at 15–20% utilisation (typical savings from right-sizing: 25–40%); storage accumulating without lifecycle policies moving aged data to lower-cost tiers; staging and development environments running 24×7 at production scale; AI inference endpoints staying warm when idle; and reserved instances purchased for workloads that have since changed. Organisations that haven't conducted a cost review in the past 12 months typically see 20–35% savings. The review takes 2–3 weeks and produces a prioritised list of changes with projected savings per item — you can implement selectively based on engineering capacity.

Pricing follows the engagement model. Discovery sprints are fixed-price for a fixed timebox. Build engagements are scoped after discovery — fixed-scope, time-and-materials, or outcome-linked. Managed services are billed monthly with defined SLAs. Total cost depends on scope, integration complexity, QA depth, and post-launch support — we publish a transparent breakdown for buyers evaluating their options.

Every engagement starts with a fixed-scope, fixed-price discovery sprint — typically 2 weeks. You leave with a written roadmap, risk register, cost estimate, and timeline before committing to a build. We then offer four engagement models: fixed-scope builds, time-and-materials, outcome-linked retainers, and managed services post-launch. If discovery doesn't justify proceeding, you can stop there — no obligation.