AWS Development Services

Serverless is usually the right starting point for APIs with variable traffic patterns, background processing jobs, and event-driven workflows — the scaling is automatic and the operational overhead is low. Containers make more sense when you have long-running processes that exceed Lambda's 15-minute timeout, workloads with predictably high baseline traffic where Lambda's per-request pricing exceeds reserved compute costs, or applications with complex runtime dependencies that are painful to package as Lambda deployment artifacts. We make this decision explicitly during scoping and explain the tradeoffs in writing so you can revisit it later if your workload changes.

DynamoDB's single-table design patterns are powerful but counterintuitive — getting the access patterns right upfront is critical because you can't add indexes after the fact without table recreation for some scenarios. We start every DynamoDB engagement by documenting all required query patterns, then design the partition key, sort key, and GSI structure to serve them within DynamoDB's consistency and throughput model. For applications with highly relational data or ad-hoc query requirements that DynamoDB doesn't serve well, we'll tell you and recommend Aurora or RDS instead.

A focused AWS application build — a microservice, a data pipeline, or a serverless API — typically runs 8–16 weeks from design to production depending on scope, with teams of 2–4 engineers. Monthly rates for KodersCode's LatAm-based AWS engineers run meaningfully below equivalent U.S. market rates while maintaining U.S.-aligned working hours for real-time collaboration. We scope and price per engagement after a discovery session where we review your requirements, so you get a fixed-scope estimate rather than an open-ended time-and-materials arrangement.

In development, we use cost allocation tags from day one and set up billing alerts so cost surprises don't accumulate unnoticed. We avoid running production-scale resources in dev/staging environments unless testing requires it, and we use AWS cost estimation tools during architecture design to flag expensive choices before they're implemented. In production, we configure Cost Explorer dashboards, set anomaly detection alerts, and include cost-per-transaction metrics in the application's observability stack so cost spikes are traceable to specific features or traffic changes.

Yes — integration work is a significant part of most AWS development engagements. Common patterns include ingesting data from Salesforce or HubSpot via EventBridge Pipes, connecting AWS Lambda to existing PostgreSQL or MySQL databases over VPC peering, streaming data into Redshift from operational databases via DMS, and building webhook receivers that fan out to internal services. We also have experience with AppSync for GraphQL APIs that federate data across AWS services and external sources. The integration architecture is explicitly scoped upfront, including authentication, error handling, and data transformation requirements.