Migration that finishes, architecture that survives a growth spurt, and a bill somebody can explain. We build on AWS with infrastructure as code from the first commit, and we will tell you when a workload is better off staying where it is.
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750+ happy clients, including top Fortune 500 companies
Overview
AWS is the cloud most enterprises already have something running on, usually before anyone drew an architecture for it. We are normally called in at the point where that sprawl starts to cost money: a bill nobody can explain, an EC2 fleet that has outgrown its original sizing, or a migration that stalled somewhere between lift-and-shift and actually working.
Our AWS work covers migration and modernisation, serverless and container builds on Lambda, ECS and EKS, data platforms on S3, Redshift and Glue, and machine learning on SageMaker and Bedrock. We build with infrastructure as code from the first commit, because an environment nobody can rebuild from a repository is one nobody can safely change.
We are equally willing to tell you that a workload should not move. Some systems are cheaper where they are, and some are close enough to end of life that migrating them is paying to relocate a problem. That conversation happens before the statement of work, not after it.
AWS capabilities
EC2 where you need the control, Fargate where you do not want to run the host, and Lambda where the work is bursty enough that paying for idle capacity makes no sense. Most estates end up with all three, and the useful part is knowing which is which.
S3 as the durable base, with lifecycle rules that actually move cold data to cheaper tiers. Redshift, Athena and Glue on top of it, so analysts can query the lake without an engineer standing up a cluster for every question.
SageMaker for models you train yourself and Bedrock for the ones you do not. Both deployed inside your account, so prompts and training data stay in a boundary your security team already signed off.
Terraform or CDK, in your repository, reviewed like application code. Clicking through the console is fine for a spike and indefensible for production, because nobody can tell you six months later what changed or who changed it.
IAM roles scoped to the job instead of to the team, secrets in Secrets Manager, GuardDuty and Security Hub wired to somewhere a person actually looks. Most AWS incidents are permissions, not exploits.
Right-sizing, Savings Plans, Graviton where the workload will take it, and tagging good enough that finance can see which team spent what. We treat the monthly bill as a design output, not an accident.
Services
Six kinds of engagement, and most clients start with one and grow into two. Whether you are moving onto AWS, building something new on it, or trying to work out where the bill is going, the first conversation is the same one.
Assessment first, then a wave plan. Some workloads lift and shift because the business case is speed, some get re-platformed onto managed services, and some are rewritten. We tell you which bucket each one is in and what the run cost looks like afterwards.
New systems built on Lambda, ECS, EKS, API Gateway, EventBridge and Step Functions. Event-driven where the work is genuinely asynchronous, and boringly synchronous where it is not, because distributed systems are a cost as well as a capability.
Lakes on S3 with Glue catalogues, pipelines that fail loudly, and warehouses in Redshift sized to the queries people actually run. We instrument the pipeline so a bad batch is caught before a dashboard quietly reports the wrong number.
CI/CD, infrastructure as code, environment parity and deployment that does not need a person awake at midnight. Where you have a platform team, we work inside their standards. Where you do not, we help you decide whether you need one.
Landing zones with Control Tower, account separation that survives an audit, encryption and key management, and logging your own risk function can query. Built to fit the frameworks you already answer to.
Monitoring, on-call, patching, and a monthly review of cost and performance against the baseline we set at handover. You get a named engineering lead, and the option to take the whole thing in house whenever you want it.
End-to-end process
Step 1
We inventory what you run, what it costs today, and what depends on what. The dependency map is usually the part that surprises people, because it is where the migration order comes from.
Step 2
Account structure, networking, identity and guardrails go in before the first workload does. Retrofitting an account strategy onto a live estate is one of the more expensive ways to learn this lesson.
Step 3
Low-risk workloads first, so the runbook gets exercised on something that will not page anyone. Each wave ends with the environment reproducible from code and the rollback tested.
Step 4
IAM scoping, encryption, logging and a full read of the bill against the estimate. This happens before cutover, when a surprise is still cheap.
Step 5
A scheduled switch with a rollback that somebody has actually run, then a period of close support while the real traffic patterns show up. Nothing behaves exactly as staging suggested.
Step 6
Right-sizing against real usage, Savings Plans once the shape is known, and documentation your team can run from. Where you want us to stay, we stay on a support agreement rather than a dependency.
Industry
What a regulated bank needs from a landing zone and what a retailer needs from an autoscaling group are not the same brief. We have been building software for 16+ sectors for 16+ years, which mostly means we know which of your constraints are real and which are habit.
Expert Insights
The AWS conversation I have most often is about the bill, and it almost never turns out to be a pricing problem. It is instances sized for a load test nobody ran, non-production environments idling through the weekend, and no tags, so finance cannot tell which team spent what. Fix the tagging first. You cannot argue about a number nobody can attribute.
Lambda vs. Fargate
This is usually the first real decision on an AWS build, and it is the one most often made out of habit. It sets what you pay when nothing is happening, how much of the platform you are responsible for, and how easily the thing can be tested. Both are right answers, for different workloads.
| Factor | AWS Lambda | AWS Fargate |
|---|---|---|
| What you deploy | A function. A zip or a container image, with a handler AWS calls. | A container. Your image, your process, your port. |
| What AWS runs for you | Everything. There is no host, no scheduler and no capacity to think about. | The host and the scheduler. You still define the task, the service and the scaling rules. |
| Scaling | Per request, to zero and back, in milliseconds. Concurrency is the unit. | Per task, in seconds to tens of seconds. You set the target and the bounds. |
| What it costs when idle | Nothing. | The tasks you have asked to keep running. |
| Ceiling to watch | 15 minute execution limit, 10GB memory, and cold starts on anything with a heavy runtime. | No execution limit. Cost climbs with idle capacity rather than with requests. |
| Skills it needs | Event-driven design and discipline about function size. Local testing is the weak spot. | Ordinary container skills. Most teams already have them. |
| Best suited to | Spiky, short, event-shaped work: webhooks, queue consumers, scheduled jobs, glue between services. | Steady traffic, long-running processes, anything already containerised, anything with an awkward runtime. |
| Our default | Start here for new event-driven work and for anything with an unpredictable load curve. | Start here when the workload is a service rather than an event, or when the team is already shipping containers. |
Why Choose Us
Sphinx has completed 16 successful years in the industry, helping businesses with technology and digital transformation, including AI-powered solutions across major industry verticals.
We have a dedicated and reliable team of AI engineers, ML scientists, data engineers, and MLOps specialists covering every role in the AI development lifecycle.
We have delivered more than 1,500 solutions through a results-driven approach, many to recurring clients who keep expanding their AI capabilities with us.
We have a dedicated in-house team, experienced and skilled across AI development, software engineering, QA, DevOps, and cloud infrastructure.
We are trusted by 750+ clients for best-in-class AI solutions at competitive rates, with the delivery accountability enterprise clients demand.
Our client-centric approach and high-calibre AI engineering, built on current models and frameworks, have made us a trusted partner for businesses building AI.
Connect with us
What we build on AWS, what a migration involves, and what to do about the bill. Get in touch.
The ones most enterprise estates are built on: EC2, ECS, EKS, Fargate and Lambda for compute; S3, RDS, Aurora, DynamoDB, Redshift and Glue for data; SageMaker and Bedrock for machine learning; and CloudFormation, CDK or Terraform underneath all of it. If a service is genuinely new to us we will say so rather than learn it on your budget.
Sometimes the answer is no, and it is worth hearing that early. A stable system with predictable load and a paid-off data centre can be cheaper where it is. What usually justifies a move is elasticity, a data platform you cannot build on-premise, or a hardware refresh you would rather not fund. We assess before we propose.
It depends on the dependency map more than the number of servers. A single well-understood application can move in weeks. A connected estate moves in waves over months, and the first wave is deliberately something low risk so the runbook gets tested on a workload that will not page anyone at 3am.
Yes, and it is one of the more common reasons people call. The usual findings are the same: instances sized for a load test that never happened, storage with no lifecycle policy, idle non-production environments running at weekends, and no tags, so nobody can attribute any of it. Most of that is fixable without touching the application.
Always, in Terraform or CDK depending on what your team already uses. An environment that exists only because somebody clicked through the console cannot be reliably rebuilt, reviewed or rolled back, and six months later nobody can say what changed.
Yes, and that is how a good proportion of our AWS work runs. We fit inside your standards, your repositories and your review process. Where you want the capability to stay with your team afterwards, the handover is part of the engagement rather than an afterthought.
Yes. Monitoring, patching, on-call and a monthly review of cost and performance against the baseline taken at handover. You get a named engineering lead, and you can take the work in house whenever you want to.
