Demand almost never arrives at the rate you planned for.
A launch, a marketing push, a customer ten times bigger than the last one. On a fixed server, a surge is the moment things slow down or fall over, and you discover that your capacity was a guess made months ago by someone hoping for the best.
The quiet times are the other half of it. A server sized for the busy hour costs exactly the same at three in the morning when nobody is using it. You end up paying for a peak you hit occasionally and idle capacity the rest of the time.
When you are building the product
In SaaS platform development the application has to hold up as customers arrive, without you re-architecting every time you sign a bigger one. Serverless scales with each request, so capacity follows real demand instead of a forecast you made before you had any users.
When it is one heavy job inside a bigger system
Not everything needs to be serverless. Often the win is moving a single spiky job, like document generation, a nightly import or a webhook that fires in bursts, onto serverless so it stops dragging the rest of your application down every time it runs.
01Architecture and design
We start by mapping how work moves through the system, then decide what belongs in a serverless function and what does not. A long-running batch job or a steady, predictable load is often better served another way, and we would rather say so early than bill you for the lesson.
02Building on AWS Lambda
Node.js or PHP functions on Lambda, fronted by API Gateway and triggered by the events that matter to your application: HTTP requests, queue messages, uploads and scheduled jobs. Each function does one thing, which keeps them cheap to reason about and safe to change.
03Data and storage
DynamoDB where you need fast key lookups at any scale, S3 for durable and cheap object storage, Aurora Serverless where the data is genuinely relational. We choose the store to fit the access pattern rather than forcing the workload to fit a database you already happen to run.
04Infrastructure as code
The whole environment is defined in code, using the Serverless Application Model, CloudFormation, the Serverless Framework or Architect. It is version-controlled, it deploys the same way every time, and a new environment is a command rather than a day of careful clicking.
05Cost and scaling
You pay for the milliseconds your code runs and nothing for the time it sits idle, which suits applications with irregular or bursty traffic well. We keep an eye on it so it stays that way, because a serverless bill can still climb if a function is written carelessly.
We designed these serverless systems, and we still run them.
The stack is usually Lambda and API Gateway, with DynamoDB, S3 and SNS behind it and Aurora Serverless where the data is relational. We are an AWS Select Tier Services Partner and run a Well-Architected review on every engagement, and serverless has been one of our founding principles rather than a stack we picked up recently.
Serverless API integration
Secure24
A serverless API bridging on-site gym hardware to an AI video platform, scaling from zero across many sites and costing almost nothing when the gyms are quiet.
Serverless data platform
Diagnostic Associates
A fully serverless telematics pipeline on Lambda, DynamoDB, S3 and SNS, processing thousands of data items a day with self-service, real-time access.
ERM carbon footprint tool
A secure, scalable web application that took a global consultancy's carbon models off spreadsheets and kept the calculations behind an API.
Thank you for this superb piece of exemplary cloud work and project commitment.
We work out where serverless fits before we build with it.
- 01
Model the workload
We look at how work actually arrives: steady, spiky, scheduled, event-driven. Serverless is the right answer for some of that and the wrong one for the rest, and it is far cheaper to know which is which before anything is built than to unpick it afterwards.
- 02
Build event-driven
Functions on AWS Lambda, triggered by API requests, queue messages, file uploads or timed events, with the whole thing defined as code so it deploys the same way every time. We reach for DynamoDB, S3, SNS and API Gateway, and for Aurora Serverless where relational data fits the job better.
- 03
Run and watch
Serverless removes the servers, not the responsibility. We watch the error rates, the cold starts and the bill, keep the runtimes current, and stay on the engagement long after launch, which is when most of the interesting scaling questions actually turn up.
The things buyers ask us first.
Will we be locked in?
No. Everything runs in your own AWS account and you hold a copy of all of it, including the infrastructure definitions and the deployment pipeline. You can take it elsewhere whenever you want, and an engagement that only survives because leaving is painful is not one we want.
Can you make our existing application serverless?
Often, in parts. The usual move is to lift out the jobs that spike, like exports or heavy processing, and make those serverless first, rather than a six-month rewrite of the whole thing. You limit the risk and the spend at each step, and keep shipping the rest of the time.
Does it have to be on AWS?
No, though for serverless it usually is. We know Lambda and the services around it deeply enough to make them cheap and predictable, and that is worth more to you than a platform we would be learning at your expense.
Who will actually build it?
The two founders, and the trusted collaborators we bring in around them. There is no account manager between you and the people writing the code, and no junior quietly learning on your project.