Cloud & Infrastructure

AWS Development Services

We build and run products on AWS, from a single Lambda function to a multi-account setup with dozens of services.

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How we work

AWS remains the broadest cloud platform available, and that breadth is both its advantage and its risk. Our aws development services cover the primitives most teams actually use in production: compute on EC2, ECS, Fargate and Lambda, data on RDS, Aurora and DynamoDB, and the networking, identity and messaging layers that hold a system together.

We treat AWS as a set of tools to pick from and use the few services a system actually needs. A new product might run entirely on Lambda and DynamoDB behind API Gateway, while a data-heavy platform might need RDS Postgres with read replicas and an EKS cluster for background processing. The right combination depends on traffic shape, team size and how much operational work the client wants to own.

We write infrastructure as code from the start, usually Terraform or CDK, so environments are reproducible and a account migration or disaster recovery plan is not a manual exercise. Cost visibility is part of the initial architecture, not an afterthought bolted on after the first surprising invoice.

Fit

When AWS is the right choice, and when it is not

Good fit

You need the widest catalog of managed services in one account, from managed Kubernetes on EKS to a vector-capable relational database on RDS Postgres with pgvector.
Your compliance program needs broad regional coverage and mature audit tooling across several jurisdictions at once.
You are adding AI features and want managed access to multiple foundation models through Bedrock alongside data that already lives in RDS or DynamoDB.
Your engineering org already runs production workloads on AWS, so hiring and onboarding are faster than a switch to another cloud would justify.
You need IAM policies fine-grained enough to separate duties across several teams sharing one organization.

Consider something else

A two- or three-service application run by a small team is often cheaper and simpler to operate on a single managed platform such as Render or Fly.io than across a dozen AWS primitives.
If the organization already runs on Microsoft 365 and Entra ID, Azure usually wires up single sign-on and directory sync with less custom glue.
Teams whose main workload is analytics and reporting sometimes get results faster from BigQuery than from assembling Redshift, Glue and QuickSight.
Without someone accountable for cost governance, an AWS bill grows through unused NAT gateways, idle load balancers and forgotten snapshots faster than it would on a flatter platform.

What we build

What we build with AWS

Compute
EC2 for full control over the instance, ECS or Fargate for containers without managing servers, Lambda for event-driven functions, and EKS when a team has already standardized on Kubernetes.
Data and storage
RDS and Aurora for relational workloads, DynamoDB for high-throughput key-value access, S3 for object storage, and pgvector on RDS Postgres for embeddings when a project needs retrieval without standing up a separate vector database.
Networking and delivery
VPC design with private subnets and NAT gateways, CloudFront for content delivery, Route 53 for DNS, and API Gateway in front of Lambda or container services.
Messaging and events
SQS for durable queues, SNS for fan-out notifications, EventBridge for routing events between services, and Step Functions for orchestrating multi-step workflows with retries.
Identity and security
IAM roles scoped to least privilege, Cognito for customer-facing authentication, Secrets Manager for credential rotation, and Security Hub for centralized findings across accounts.
AI and data pipelines
Bedrock for access to several foundation models without managing GPU infrastructure, SageMaker for custom model training, and Kinesis or MSK for streaming data pipelines.

Ecosystem

Libraries and tools we pair it with

Terraform
AWS CDK
CloudFormation
GitHub Actions
Datadog
PgBouncer
Docker
Amazon EKS
pgvector
CloudWatch and X-Ray

Choosing Between Lambda, ECS and EKS

Lambda fits workloads that are short-lived, bursty or event-triggered: image processing after an S3 upload, a webhook handler, a scheduled job. Execution time limits and cold starts make it a poor fit for long-running connections or workloads that need consistent low latency.

ECS on Fargate is our default for standard web services and APIs. It runs containers without a cluster to patch, scales predictably, and works well for teams who want Docker without Kubernetes's operational surface. EKS earns its complexity when a team already runs Kubernetes elsewhere and wants one operating model across clouds, or when a platform needs custom schedulers, service mesh, or fine-grained control over networking that ECS does not expose.

Lambda: event-driven, short execution, pay per invocation
ECS/Fargate: standing services, simpler operations, no cluster to manage
EKS: multi-cluster consistency, custom controllers, existing Kubernetes expertise

Keeping an AWS Bill Predictable

Cost surprises on AWS rarely come from the workload itself; they come from resources nobody is watching. We tag every resource by project and environment from day one so a bill can be split apart without guesswork, set budget alerts before a project ships, and review idle NAT gateways, unattached EBS volumes and over-provisioned RDS instances on a schedule rather than after a finance team asks questions.

For steady-state workloads we evaluate Savings Plans or Reserved Instances once traffic is predictable, and use Spot Instances for batch and non-critical background jobs where interruption is acceptable. None of this requires a dedicated cloud cost team; it requires deciding on the conventions before the first environment goes live.

Services

Services that use AWS

Industries

Where we use it

FAQ

AWS FAQs

How do we decide between Lambda and ECS for a new AWS service?

Look at execution duration and traffic pattern first. Lambda suits short, bursty, event-triggered work such as processing an uploaded file or handling a webhook; anything approaching its execution time limit or needing a persistent connection belongs on ECS or Fargate instead. We also weigh existing team familiarity: a team already comfortable with Docker images often ships faster on ECS than learning Lambda's packaging and cold-start behavior.

Can AWS Bedrock replace a direct integration with a model provider like OpenAI?

Sometimes. Bedrock gives you a single API surface across several foundation model providers and keeps inference inside your AWS account and IAM boundary, which matters for some compliance programs. The trade-off is that newer model features and API capabilities usually appear on a provider's native API before they reach Bedrock, so teams chasing the latest tool-calling or structured-output features may still integrate directly.

Does moving to AWS mean we have to adopt Kubernetes?

No. Most teams we work with run ECS on Fargate or Lambda and never touch EKS. Kubernetes on AWS makes sense when a team already operates it elsewhere or needs capabilities ECS does not offer, not as a default landing zone for a new AWS account.

Is RDS with pgvector enough for a retrieval-augmented generation feature, or do we need a dedicated vector database?

For most applications with tens of thousands to a few million vectors, pgvector on an existing RDS Postgres instance performs well and avoids adding another system to operate. Once query volume or vector count grows large enough that index build time and recall tuning become a bottleneck, a dedicated vector database becomes worth the added operational cost.

What does migrating from on-premises servers to AWS actually involve?

It starts with mapping dependencies between applications, databases and network services that have often accumulated undocumented over years. We typically run a phased migration: non-critical services first, hybrid connectivity through Site-to-Site VPN or Direct Connect during the transition, then a planned cutover for anything with strict downtime requirements, with a rollback path defined before we start.

How much AWS experience does our internal team need after launch?

Enough to read CloudWatch alarms and deploy through your CI pipeline, not enough to design the architecture from scratch. We hand over infrastructure as code, runbooks for common operational tasks, and IAM access scoped to what your team actually needs day to day, rather than leaving you dependent on undocumented console changes.

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