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
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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.
Related technologies
AWS
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Serverless apps defined and shipped with AWS SAM — Lambda, API Gateway, Step Functions, and everything you need to deploy from a single template.
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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.
