How fast can the pod start?

Within 24 hours of greenlight after a 3-day free trial. The trial runs against a real scoped task, so you see the engineering depth before you sign anything. Replacement is free within 14 days if the fit is wrong.

Devlyn AI · AWS

AWS pods, owned by us. Embedded with you.

Senior AWS engineers under one retainer, with AI-augmented workflows that compress 100 hours of typical work to 25. Deployed in 24 hours.

Where $AWS fits

AWS pods ship cloud-native infrastructure spanning serverless architectures with Lambda and API Gateway, container orchestration with ECS/Fargate for predictable workloads and EKS for Kubernetes-native deployments, data-layer design with DynamoDB for key-value and document access patterns, RDS and Aurora for relational workloads with read replicas, S3 for object storage with lifecycle policies, event-driven architectures using EventBridge and SQS for decoupled service communication, and Step Functions for workflow orchestration. Devlyn engineers ship AWS with CDK (TypeScript or Python) or Terraform for infrastructure-as-code with modular construct patterns, OpenTelemetry for distributed tracing across serverless and container services, and cost-aware architecture choices including reserved-capacity planning, spot-instance strategies, and right-sizing recommendations — with production-grade IAM least-privilege policies and GuardDuty threat detection.

AI-augmented AWS workflows lean on Cursor and Claude Code for CDK construct scaffolding with proper resource configuration, Terraform module generation with variable and output definitions, Lambda handler patterns with proper error handling and cold-start optimisation, EventBridge rule and target configuration, and IAM policy generation with least-privilege scoping — all under senior validation that owns architecture decisions, cost-budget review and optimisation (reserved instances, savings plans, spot strategies), IAM security posture with service-control policies and permission boundaries, and AWS-specific pitfalls like Lambda cold-start mitigation, DynamoDB partition-key design for even distribution, and cross-region replication configuration. Compression shows up strongest in IaC module scaffolding, Lambda handler boilerplate, and IAM policy generation.

AWS engagements at Devlyn typically run as one senior DevOps or platform engineer plus shared backend for $5,500–$10,000/month, covering infrastructure architecture, CI/CD pipeline design, and cost-optimisation strategy. This scales to a two- or three-engineer pod when the roadmap splits into parallel lanes across platform infrastructure (networking, compute, storage), data-pipeline and analytics (Kinesis, Glue, Athena), and security and compliance (GuardDuty, Config, CloudTrail, SCPs). Pods share a single retainer with flexible allocation.

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Where AWS pods land today

Six combinations that show up most often in the last few quarters of AWS discovery calls — vertical, geography, and the named-risk pattern each engagement designed around.

AWS · B2B SaaS · Seattle

AWS for B2B SaaS in Seattle

The most common 2026 B2B SaaS engineering trap is integration-first roadmaps that fragment the codebase into per-customer hacks and one-off webhook handlers, creating a maintenance debt spiral that slows all future feature work. AWS pods compress the work — aws pods ship cloud-native infrastructure spanning serverless architectures with lambda and api gateway, container orchestration with ecs/fargate for predictable workloads and eks for kubernetes-native deployments, data-layer design with dynamodb for key-value and document access patterns, rds and aurora for relational workloads with read replicas, s3 for object storage with lifecycle policies, event-driven architectures using eventbridge and sqs for decoupled service communication, and step functions for workflow orchestration. On the Pacific (PT) calendar, seattle fte pipelines compete with faang-tier salaries that startup budgets cannot match.

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AWS · Fintech · London

AWS for Fintech in London

The most common 2026 fintech engineering trap is shipping a feature that depends on a partner-bank integration that has not been contractually signed or technically certified, creating a rollback scenario that wastes months of engineering effort. AWS pods compress the work — aws pods ship cloud-native infrastructure spanning serverless architectures with lambda and api gateway, container orchestration with ecs/fargate for predictable workloads and eks for kubernetes-native deployments, data-layer design with dynamodb for key-value and document access patterns, rds and aurora for relational workloads with read replicas, s3 for object storage with lifecycle policies, event-driven architectures using eventbridge and sqs for decoupled service communication, and step functions for workflow orchestration. On the GMT / BST calendar, london fte hiring runs 3–5 months for senior fintech and ai roles, with offers regularly contested by us tech giants opening uk offices.

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AWS · AI Startup · San Francisco

AWS for AI Startup in San Francisco

The most common 2026 AI-startup engineering trap is shipping LLM-powered features without deterministic-test wrapping of stochastic systems, creating quality regressions that are invisible until users report hallucinations or incorrect outputs at scale. AWS pods compress the work — aws pods ship cloud-native infrastructure spanning serverless architectures with lambda and api gateway, container orchestration with ecs/fargate for predictable workloads and eks for kubernetes-native deployments, data-layer design with dynamodb for key-value and document access patterns, rds and aurora for relational workloads with read replicas, s3 for object storage with lifecycle policies, event-driven architectures using eventbridge and sqs for decoupled service communication, and step functions for workflow orchestration. On the Pacific (PT) calendar, fte hiring in sf has slowed structurally since 2024 layoffs but compensation expectations have not.

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AWS · Healthtech · Boston

AWS for Healthtech in Boston

The most common 2026 healthtech engineering trap is shipping a clinical feature that has not been reviewed against HIPAA BAA requirements or FDA SaMD classification boundaries, creating regulatory exposure that can halt the entire product. AWS pods compress the work — aws pods ship cloud-native infrastructure spanning serverless architectures with lambda and api gateway, container orchestration with ecs/fargate for predictable workloads and eks for kubernetes-native deployments, data-layer design with dynamodb for key-value and document access patterns, rds and aurora for relational workloads with read replicas, s3 for object storage with lifecycle policies, event-driven architectures using eventbridge and sqs for decoupled service communication, and step functions for workflow orchestration. On the Eastern (ET) calendar, boston fte pipelines run 4–6 months for senior backend roles.

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AWS · Govtech · Washington DC

AWS for Govtech in Washington DC

The most common 2026 govtech engineering trap is shipping a feature that fails Section 508 accessibility testing or FISMA audit-trail requirements late in the procurement evaluation cycle, disqualifying the product from the award after months of engineering investment. AWS pods compress the work — aws pods ship cloud-native infrastructure spanning serverless architectures with lambda and api gateway, container orchestration with ecs/fargate for predictable workloads and eks for kubernetes-native deployments, data-layer design with dynamodb for key-value and document access patterns, rds and aurora for relational workloads with read replicas, s3 for object storage with lifecycle policies, event-driven architectures using eventbridge and sqs for decoupled service communication, and step functions for workflow orchestration. On the Eastern (ET) calendar, dc fte pipelines for cleared roles run 6–9 months.

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AWS · Ecommerce · Berlin

AWS for Ecommerce in Berlin

The most common 2026 e-commerce engineering trap is checkout optimisation that breaks tax-jurisdiction compliance or fraud-rule integrations, creating either tax liability exposure or legitimate-order rejection spikes. AWS pods compress the work — aws pods ship cloud-native infrastructure spanning serverless architectures with lambda and api gateway, container orchestration with ecs/fargate for predictable workloads and eks for kubernetes-native deployments, data-layer design with dynamodb for key-value and document access patterns, rds and aurora for relational workloads with read replicas, s3 for object storage with lifecycle policies, event-driven architectures using eventbridge and sqs for decoupled service communication, and step functions for workflow orchestration. On the CET / CEST calendar, berlin fte pipelines run 2–4 months for senior backend roles.

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What AWS depth at Devlyn looks like

Common use cases

AI-augmented angle

Engagement shape & pricing

Ecosystem fluency

AWS ecosystem depth covers the full modern surface: CDK for TypeScript and Python infrastructure-as-code, Terraform for multi-cloud IaC with state management, Lambda for serverless compute with Powertools for structured logging and tracing, ECS/Fargate for container orchestration, EKS for managed Kubernetes, DynamoDB for key-value and document NoSQL, RDS and Aurora for managed relational databases, S3 for object storage with intelligent tiering, EventBridge for event-driven architecture, SQS and SNS for messaging, Step Functions for workflow orchestration, CloudFront for CDN and edge compute, IAM for access management, GuardDuty for threat detection, and CloudWatch with X-Ray for monitoring and tracing. Devlyn engineers operate fluently across this entire surface with cost-aware, security-first production patterns.

Real outcomes

Calenso · Switzerland

4× productivity

5,000+ integrations on the platform after AI-augmented engineering replaced manual workflows.

Creator.ai

6 weeks → 1 week

6× faster delivery, 2× output per engineer, 50% leaner team.

Klaviss · USA

$4,800/mo pod

Two engineers + PM + shared DevOps. Real-estate platform overhaul shipped in 8 weeks.

Haxi.ai · Middle East

AI engagement at scale

Real-time, context-aware AI conversations across platforms — spec to production by one pod.

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Verticals where AWS ships well

AWS pods most often run engagements in the verticals below. Each links through to a vertical-level hub with named risks, compliance posture, and key metrics.

Metros where AWS pods deploy

Hand-picked cities where AWS engagements show up most. Each city has its own time-zone alignment and hiring-climate notes on the metro hub.

Common questions about AWS engagements

What does a AWS pod actually own end-to-end?

Architecture, security review, and the AWS-specific patterns that production-grade work requires. AWS pods ship cloud-native infrastructure spanning serverless architectures with Lambda and API Gateway, container orchestration with ECS/Fargate for predictable workloads and EKS for Kubernetes-native deployments, data-layer design with DynamoDB for key-value and document access patterns, RDS and Aurora for relational workloads with read replicas, S3 for object storage with lifecycle policies, event-driven architectures using EventBridge and SQS for decoupled service communication, and Step Functions for workflow orchestration. Devlyn engineers ship AWS with CDK (TypeScript or Python) or Terraform for infrastructure-as-code with modular construct patterns, OpenTelemetry for distributed tracing across serverless and container services, and cost-aware architecture choices including reserved-capacity planning, spot-instance strategies, and right-sizing recommendations — with production-grade IAM least-privilege policies and GuardDuty threat detection.
How does AI-augmented AWS differ from a single contractor using AI tools?

AI-augmented AWS workflows lean on Cursor and Claude Code for CDK construct scaffolding with proper resource configuration, Terraform module generation with variable and output definitions, Lambda handler patterns with proper error handling and cold-start optimisation, EventBridge rule and target configuration, and IAM policy generation with least-privilege scoping — all under senior validation that owns architecture decisions, cost-budget review and optimisation (reserved instances, savings plans, spot strategies), IAM security posture with service-control policies and permission boundaries, and AWS-specific pitfalls like Lambda cold-start mitigation, DynamoDB partition-key design for even distribution, and cross-region replication configuration. Compression shows up strongest in IaC module scaffolding, Lambda handler boilerplate, and IAM policy generation. The 4× compression comes from pod-level workflow design, not from individual tool adoption.
What does a AWS engagement typically cost?

AWS engagements at Devlyn typically run as one senior DevOps or platform engineer plus shared backend for $5,500–$10,000/month, covering infrastructure architecture, CI/CD pipeline design, and cost-optimisation strategy. This scales to a two- or three-engineer pod when the roadmap splits into parallel lanes across platform infrastructure (networking, compute, storage), data-pipeline and analytics (Kinesis, Glue, Athena), and security and compliance (GuardDuty, Config, CloudTrail, SCPs). Pods share a single retainer with flexible allocation.
Which AWS ecosystem libraries does Devlyn cover?

AWS ecosystem depth covers the full modern surface: CDK for TypeScript and Python infrastructure-as-code, Terraform for multi-cloud IaC with state management, Lambda for serverless compute with Powertools for structured logging and tracing, ECS/Fargate for container orchestration, EKS for managed Kubernetes, DynamoDB for key-value and document NoSQL, RDS and Aurora for managed relational databases, S3 for object storage with intelligent tiering, EventBridge for event-driven architecture, SQS and SNS for messaging, Step Functions for workflow orchestration, CloudFront for CDN and edge compute, IAM for access management, GuardDuty for threat detection, and CloudWatch with X-Ray for monitoring and tracing. Devlyn engineers operate fluently across this entire surface with cost-aware, security-first production patterns.
How fast can the pod start?

Within 24 hours of greenlight after a 3-day free trial. The trial runs against a real scoped task, so you see the engineering depth before you sign anything. Replacement is free within 14 days if the fit is wrong.

When the next move is a conversation

Book a 30-minute discovery call. We will scope a AWS pod against your roadmap and timeline. No contracts. No commitment. Or run the Pod ROI Calculator against your current vendor's burn first.