Amazon Web Services
Also known as: AWS
AWS is Amazon's cloud platform — the most service-dense and the operational default for most TantraDev FinTech engagements. The depth (KMS, Aurora, ECS Fargate, EKS, Lambda, SQS, EventBridge, CloudFront) covers nearly every architectural choice without forcing a multi-cloud foray. We default to AWS when the client has no existing cloud preference and the latency budget allows it.
Concepts that travel with this one.
Architecture rarely lives in isolation — these are the terms that come up in the same conversation.
Terraform
Terraform is HashiCorp's declarative infrastructure-as-code tool. You write the desired state (a VPC, an RDS cluster, an IAM role) in HCL and Terraform reconciles cloud reality to match. TantraDev writes Terraform for everything — networks, databases, secrets, monitoring — and hands the state and modules back at the end of every engagement so the runbook works without us.
Kubernetes
Kubernetes is an open-source container orchestration platform that schedules, scales, and self-heals workloads across a fleet of nodes. TantraDev runs Kubernetes (EKS, AKS, GKE) where workload heterogeneity or multi-tenancy justifies the operational tax — and explicitly recommends managed alternatives (ECS Fargate, Cloud Run) where they don't. Picking K8s for a five-service app is usually the wrong call.
Microsoft Azure
Azure is Microsoft's cloud platform, dominant in healthcare and government deployments where existing Microsoft licensing and HIPAA BAA structure tip the calculus. TantraDev uses Azure for HIPAA-residency workloads, AKS-based Kubernetes engagements, and clients with existing Microsoft enterprise agreements. Service parity with AWS is close enough that the choice is almost always organisational, not technical.
Google Cloud Platform
Google Cloud Platform is Google's cloud, strongest where data and ML workloads dominate — BigQuery, Vertex AI, Dataflow, Pub/Sub, and Cloud Run all read like first-class citizens rather than late additions. TantraDev reaches for GCP when ML training cost or analytical query economics dominate the architecture decision, particularly in EdTech and consumer-data products.
Building a system where Amazon Web Services is the load-bearing decision?
30 minutes on the phone, one page in your inbox — what to build, what to skip, what it will cost. You keep the audit even if we are not the right fit.