Technology
AWS Lambda Development Services — Serverless That Earns Its Place
Lambda function design, optimization, and operations — cold-start mitigation, IAM scoping, observability, and the architectures where serverless wins.
What we build with AWS Lambda
- Lambda functions in Node, Python, Go, Rust, and Java with proper cold-start tuning
- API Gateway (REST + HTTP API), Function URLs, ALB, and AppSync integration patterns
- Step Functions for long-running, stateful, and human-in-the-loop workflows
- EventBridge, SQS, SNS, Kinesis, DynamoDB Streams, and S3-event-driven processing
- Lambda layers, container images (up to 10GB), and ARM64 / Graviton optimization
- Provisioned concurrency and SnapStart for latency-sensitive endpoints
- Lambda extensions for observability, secrets, and custom runtime patches
- Powertools for AWS Lambda (Python, TypeScript, Java, .NET) — logging, tracing, metrics
- Observability with CloudWatch, X-Ray, OpenTelemetry, Datadog, and Honeycomb
- IaC with SAM, CDK, Terraform, or Serverless Framework
- VPC integration with proper ENI management, cold-start mitigation, and DNS
- Lambda authorizers, custom domain mappings, and proper API Gateway throttling
- DLQ wiring, idempotency tokens, and replay tooling for failed invocations
- Cost engineering: memory sizing sweep, ARM migration, and right-sized concurrency
Why DiveScale
Built by engineers who ship AWS Lambda in production
Lambda is the right answer for event-driven, bursty, and unpredictable workloads — not a default for every service. DiveScale designs Lambda architectures that earn the serverless tax: low cold-start, properly scoped IAM, and observability you can actually act on.
We choose runtime per workload: Node and Python for general work; Go and Rust when cold-start and memory matter. ARM64/Graviton for ~20% cost cut on most runtimes. Container images when the dependency footprint is too large for the zip layout.
Operationally we wire Lambda the way you would wire any production service: structured logs, X-Ray traces, alarms on real failure modes, and IAM roles scoped to the minimum the function actually needs.
AWS Lambda use cases we deliver
How we deliver
Our AWS Lambda delivery process
- 01
Workload fit
We pressure-test whether Lambda actually beats a container for the workload — cost, latency, and ops together.
- 02
IaC with SAM or CDK
Functions, permissions, and triggers in code from day one. No console clicks on production accounts.
- 03
Tune & observe
Memory sweep, ARM64 where possible, provisioned concurrency where the cold-start tail justifies it, X-Ray on everything.
- 04
Operate & evolve
Quarterly cost reviews, IAM tightening, and migration to containers when the workload outgrows Lambda.
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Learn moreAWS Lambda — Frequently Asked Questions
Event-driven workloads, infrequent or spiky traffic, glue code between AWS services, and low-volume APIs. Lambda struggles when traffic is high and steady — at that point a container often wins on cost.
