Scale Your Customer Count.
Stop Fighting Your Architecture. 📈
I architect highly scalable SaaS platforms so you never have to worry about database downtimes, cache hit-rates, or eventual consistency bottlenecks. Break free from your rigid monolith and build a system designed for hyper-growth.
Architecting high-availability systems for Series A & B startups
Node.js • Postgres • Redis • Kafka • AWS • Microservices
Success is breaking your application.
The architecture that got you to $1M ARR is exactly what will prevent you from reaching $10M ARR. A rigid monolith physically caps your revenue potential.
The Availability Crisis
Sales are closing and user count is rising, but the app keeps crashing during peak hours. Enterprise clients are threatening to churn due to poor performance and frequent micro-outages.
Zero Engineering Velocity
Your original MVP monolith is now a tangled mess. Adding a simple feature takes three weeks. Your developers are spending 60% of their time fighting Jira tickets and patching memory leaks.
Database Bottlenecks
The database is constantly locking, and cache hit-rates are abysmal. You are manually restarting crashed servers instead of relying on a resilient, decoupled data layer.
Who this is for
Both arrive at the same point from opposite directions: the product works, and that is the problem.
Founders whose product is buckling under its own success
Sales are closing and the platform is getting slower, and the customers most likely to churn over it are the largest ones you have.
What changes: Headroom that is measured rather than asserted, and a system whose failure modes are known before a customer finds them.
Technical leads carrying a monolith
The architecture that got the company to product-market fit has stopped being changeable. A small feature takes weeks, and every change touches something it should not.
What changes: Boundaries that let people work in parallel again, reached in an order that never requires a feature freeze.
What you get
Measurement first, then a plan the product roadmap can actually survive.
A decoupling plan
What to split, what to deliberately leave alone, and the order that keeps the product shippable the whole way through. Most rewrites fail on the ordering rather than the design.
Data layer work
Index design, read replicas, partitioning and caching, each justified by the specific query path it fixes rather than adopted because it is the next thing on a list.
Asynchronous boundaries
Where a queue or a broker genuinely belongs, and, just as usefully, where introducing one would be expensive theatre.
Load and failure characterisation
What breaks first, at what load, and in what way. Measured, so that any claim the system now scales is a result rather than a hope.
How the engagement runs
The first phase exists because the bottleneck is almost never where the team believes it is.
1. Measure
Before anything is redesigned, I find what actually breaks first. It is rarely what the team assumes, and it is usually cheaper to fix than the thing they were about to rewrite.
2. Sequence
A plan with no feature freeze in it and nothing that has to land all at once, because a migration the roadmap cannot survive is a migration that gets abandoned halfway.
3. Execute
The data layer first, since that is where most of the latency lives and where the fixes are least disruptive, and only then the service boundaries.
4. Verify
Load characterisation against the new shape, so the headroom you have bought is a number rather than a feeling.
From the workbench
What breaks first, and in what order
Systems fail in a fairly predictable sequence as load grows, and almost never where the team expects. These are the four that arrive first, roughly in order:
| What you see | What it usually is | How to check it yourself |
|---|---|---|
| Reads slow down before writes do | One or two queries without a supporting index, usually on a filter added after the table was designed. | Read the slow query log for an hour of peak traffic. The list is normally short and repetitive. |
| Connections exhaust before the database does | Every application instance holds its own pool, so the total scales with instances rather than with load. | Multiply pool size by instance count and compare it against the database connection limit. |
| Background work starves the request path | Reports, exports and webhooks run on the same resources as user traffic, so the slowest job sets the worst latency. | Check whether your longest-running job and your login request share a database. |
| One customer degrades everyone | No per-tenant limit, so the largest account's usage is absorbed by the shared system. | Rank last month's usage by customer. If the top one is an order of magnitude clear, they are your capacity plan. |
The order matters more than the list. Fixing the fourth before the first is common, expensive, and does nothing, because the queue was never the bottleneck.
FAQs
No. I explicitly avoid grand rewrites. I extract the most resource-heavy domains from your monolith one at a time into independent services, so the rest of your roadmap keeps shipping.
Stress testing identifies the real bottleneck before anything else, usually database locking and cache hit-rates, so the first changes target the exact thing causing outages, not a generic scaling checklist.
A decoupled architecture designed for 99.99% uptime, new data models built for horizontal scaling, and zero-downtime deploy workflows so you can ship in the middle of the day without disrupting active users.
Scaling engagements run on the Growth and Enterprise tiers shown above, or as a custom, project-based scope for a specific decoupling milestone.
Ready to stop firefighting?
The architecture that carried you to product-market fit was correct when it was written. It stops being correct at a specific, findable point, and the useful question is which part gives way first.
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