Architecture debt slows every release
Shared components, manual deployment steps and fragile environments turn each release into a negotiation between teams.
← Industries
B2B software and technology
SaaS companies, cloud-dependent technology vendors and digital platforms with their own engineering teams, running their product on AWS.
Common problems
They tend to show up when growth outpaces the architecture: more customers, more data, bigger buyers and an AI roadmap, on a platform built for an earlier stage.
AWS cost per customer keeps rising
Spend grows faster than revenue, and nobody can say which tenants, features or environments drive it.
Bigger customers expect stronger guarantees
Enterprise deals bring uptime commitments, recovery objectives and incident reporting the platform was not built for.
Product and customer data are hard to use
Analytics, customer-facing reporting and AI all need governed data that today lives across services and copies.
AI features stuck between demo and release
Evaluation, guardrails, tenant data isolation, cost per request and an owner in production are usually what is missing.
Enterprise security questionnaires
Large customers ask how accounts, access, logging and data are separated. Good answers start with the architecture, not the questionnaire.
A typical engagement path
For a SaaS company whose AWS bill and release cycle are both getting worse. Each step is a separate decision.
- 1
Architecture Review (2–4 weeks)
Architecture, cost and risk assessment: cost per tenant, release bottlenecks, reliability gaps and a ranked roadmap.
- 2
Modernization Sprint (4–8 weeks)
A focused platform sprint: account structure, infrastructure as code and a deployment path the team can repeat.
- 3
AI Production Readiness (2–4 weeks)
Production readiness for one AI feature: evaluation, guardrails, tenant isolation and cost per request.
- 4
Your team owns it
Runbooks, tests, a cost view by tenant and a backlog your engineers can run without us.
Relevant services
Assess
Cloud Architecture, Cost & Risk Assessment
An independent view of the platform before a scaling, cost or modernization decision.
What the assessment covers →BuildCloud Platform Foundation & Landing Zone
Accounts, identity, networking and logging that support many teams and enterprise customers.
How we set it up →ImproveReliability, DevOps & Developer Platform
Faster, safer releases and reliability targets your customers can rely on.
What we would change →DataData Platform / Lakehouse Foundation
Governed product and customer data for analytics, reporting and AI.
How we build it →AIProduction AI, RAG & Agent Engineering
An AI feature built to be evaluated, observed and operated in production.
From prototype to production →CostCloud Health, FinOps & Governance Review
Cost per customer made visible, with owners and a monthly cadence.
Where the savings usually are →How we work
Alongside your engineers
We work inside your repositories, pipelines and rituals, document the trade-offs and leave the decision and the backlog with your team.
Fit
Focused, high-stakes problems
One platform constraint, one cost problem or one AI feature with a deadline and an owner. Not an open-ended engineering bench.
Not for us
What we don't do
Staff augmentation, building your product roadmap for you, or running your platform 24×7. We will point you to the right kind of provider.
Further reading
Guides for SaaS teams, plus methods from our payments work that apply to any product running on AWS.
SaaS · Field guide
Cost per customer in a multi-tenant SaaS on AWS
How to measure what each customer costs to serve when they share the same infrastructure, and why it drifts.
FinOps · Field noteCloud cost is an architecture problem
Why rightsizing and discounts recover savings once, and what keeps the bill growing with the business rather than ahead of it.
Data & AI · ArticleFive signs your data platform is not ready for AI
A ten-minute self-assessment before you commit to an AI feature.
Building your product on AWS?
Start with an Architecture, Cost & Risk Assessment, or tell us about the AI feature that needs to reach production.