Event-driven architecture on AWS Lambda, designed so the operational savings are real — and honest advice about the workloads that belong somewhere else.
Lambda removes servers from the picture and replaces them with a different set of decisions: concurrency limits, cold starts, event ordering, and a call graph that gets hard to reason about somewhere around the fortieth function.
We design serverless systems that stay legible — clear event boundaries, sensible function granularity, and enough observability to answer what happened without reading logs line by line.
Function boundaries, queueing, and retry semantics designed so failures are contained and replayable rather than silently dropped.
Memory sizing, concurrency controls, and cold-start mitigation — usually recovering more spend than they cost to implement.
Structured logging, distributed tracing, and alerting that make a serverless call path debuggable at 3 AM by someone who did not build it.
Long-running jobs, sustained high-throughput workloads, and anything with heavy startup costs are usually cheaper and simpler on Fargate or EC2. We will tell you that before the migration rather than after. The point of serverless is removing operational burden, and a Lambda function fighting a fifteen-minute timeout has added burden rather than removed it.
Most Lambda cost problems are sizing problems — memory set once at a default and never revisited, when memory and CPU scale together and a larger function often finishes fast enough to cost less. We measure rather than guess, apply reserved and provisioned concurrency where latency actually requires it, and put limits around the functions that could otherwise scale straight into your account quota.
Containers on ECS and EKS, serverless adoption, CI/CD and delivery automation, Infrastructure as Code, and application refactoring.
Workload discovery, migration planning and sequencing, database and data movement, and cutover with validation.
Infrastructure as code, continuous compliance monitoring, and automated qualification evidence for regulated Life Sciences workloads on AWS.
We'll review your functions, their sizing, and their call paths, and show you what can be recovered without rewriting the application.
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