Decompose the monolith into services you can ship one at a time.
We carve a monolith into independently deployable services along the seams that actually matter — so one team ships one service without a full-system release, and a failure in one stays in one.
Real API contracts, containerized on Kubernetes, resilience built in. We’ll also tell you which boundaries to leave alone. Fixed scope, one lead, steady state in 4–8 weeks.
Not because monoliths are bad — most start as the right call. It becomes the bottleneck when every small change has to be tested and released against the whole application, when one team’s deploy is blocked behind another’s, when the single component under load forces you to scale the entire stack, and when one bad release takes everything down at once.
The code still works — but the rate at which you can safely change it has stalled, and that rate is what determines how fast the business can move.
Microservices fix this by making the unit of deployment small and independent: each service is its own codebase, data, and release. But that only pays off if the boundaries are drawn correctly.
Cut along the wrong seams and you get a distributed monolith — all the network latency and operational overhead of microservices with none of the independence. Getting the boundaries right is the entire engineering problem, and it is what microservices development services are actually for.
A microservice is justified by a specific pressure on the system, not by fashion. For each common pattern: what it does, the benefit it produces, and how that plays out.
Pull the part of the system that changes most often — checkout, pricing, a recommendation engine — into its own service with its own release pipeline. Benefit — that team ships on its own cadence without a full-system release, so lead time drops and risk shrinks to one service.
For example, the pricing team pushes a change at 10 a.m. and again at 3 p.m. without waiting on the next monthly platform release — and if something’s off, only pricing is rolled back, not the whole storefront.
Give the component that spikes under load — search, media processing, an events firehose — its own service so it scales on its own resources. Benefit — you scale the 10% that’s actually hot instead of paying to replicate the whole monolith, cutting infrastructure cost per unit of traffic.
For example, search gets twenty replicas during a sale while the account service stays at two — instead of running twenty copies of everything to feed one busy module.
Wrap a path that must not take the system down — payments, authentication — in a service with its own failure boundary, circuit breakers, and graceful degradation. Benefit — a failure stays contained, so a problem in one service degrades one feature instead of crashing the application.
For example, if the recommendation service times out, the product page still loads and checkout still works — the customer sees a page without “you may also like,” not an error.
Draw service boundaries around business domains so one team owns one service end to end — code, data, deploy, on-call. Benefit — teams stop colliding in a shared codebase, so throughput rises as the org grows instead of grinding on merge conflicts and coordination.
For example, the catalog team and the orders team each own their service and ship in parallel, instead of queuing behind one shared release train and a shared database migration.
Stand up new capability as a service alongside the monolith, and strangle legacy pieces out one at a time rather than in a risky big-bang rewrite. Benefit — you modernize continuously with the system always live, and each piece can use the right language or datastore for its job.
For example, a new fraud-scoring service ships in the stack best suited to it and routes traffic gradually, while the legacy module it replaces is retired only once the new one is proven — no downtime, no rewrite weekend.
Extract cross-cutting capability — auth, notifications, payments, search — into shared services consumed across products through a stable API. Benefit — capability is built once and reused, so new products reach market faster and behave consistently.
For example, a second product line reuses the existing payments and notification services through their APIs instead of rebuilding both, shaving weeks off the launch.
The scope below is the difference between independently-deployable services and a distributed monolith that’s harder to run than the thing you started with.
We map the system to its business domains and draw service boundaries along bounded contexts — the seams where data and ownership genuinely separate, not arbitrary technical layers. This is the decision the whole architecture rests on, and where we’ll argue for fewer services when the evidence says so.
Each service exposes a versioned, documented contract; services talk through synchronous APIs where a caller needs an answer now and asynchronous events where they don’t — so a slow or down service doesn’t stall the ones that depend on it. Contract tests keep the seams honest as services evolve independently.
Each service owns its data behind its own interface — no shared database that quietly re-couples everything. We design the data boundaries, the migration off a shared schema, and the patterns (events, sagas) that keep data consistent across services without distributed locks.
Services are containerized and orchestrated on Kubernetes — health checks, autoscaling, rolling and canary deploys, resource limits per service. We build this on the cloud foundation your cloud engineering sets up, rather than reinventing the infrastructure layer here.
Where the service count justifies it, a service mesh handles service-to-service traffic, retries, timeouts, and mutual TLS. Resilience patterns — circuit breakers, bulkheads, graceful degradation — are designed in, and every service is instrumented with distributed tracing so a request can be followed across the whole call graph.
For a live system, we use the strangler-fig pattern: stand up new services alongside the monolith and route traffic over incrementally, so the system stays up and each cut is reversible. This pairs with our cloud migration services when the move also crosses platforms.
Each service gets its own CI/CD pipeline so it deploys independently; security scanning and policy gates are baked into each one through our DevSecOps practice rather than bolted on after.
What you get when you hire us — all assigned to you
The same delivery model behind all our engineering work, tuned for decomposition — one accountable lead, fixed scope, no handoffs.
Model the domain, find the real seams, and baseline today’s deployment frequency, lead time, and the coupling that’s actually hurting.
Output: a service map, a prioritized decomposition order & the metrics
Design the boundaries, the API contracts, the data-ownership split, and the communication patterns (sync vs. async).
Output: an architecture & contracts you review before a line of code
Develop the services in your own cloud tenant, containerized and orchestrated, with resilience, tracing, and per-service pipelines; for a live monolith, route traffic over incrementally.
Output: independently-deployable services behind your access controls
Staged rollout, per-service deployment and failure metrics watched against the kickoff targets, and your team trained to own, deploy, and extend each service.
Output: a production system & a team that runs it
Most engagements reach a working, independently-deployable steady state in 4–8 weeks — with full IP assignment signed at kickoff.
Independently-deployable services are only an asset if you can ship them safely and keep them up — and that production discipline is what we’re known for, even where the architecture differs. The clearest adjacent evidence:
The release discipline microservices are supposed to deliver — frequent, low-risk, recoverable deploys — is the discipline we run as a standing practice. For a 200+ location restaurant business, we moved production releases from every two weeks to twice a week with zero critical defects sustained across four years (BJ’s Restaurants) — the same evals-before-release, staged-rollout, monitor-after rhythm that lets a fleet of services ship independently without breaking each other.
Adjacent evidence — a restaurant-operations platform, not a from-scratch microservices build.
And the least forgiving thing in a distributed system is money moving through it. We built YardClub, a contractor-to-contractor equipment marketplace, end to end — listings, payments, and the transaction infrastructure underneath — which processed $120M+ in transactions before being acquired by Caterpillar in 2017 (TechCrunch). That is the bar a payments or orders service must clear: correct and available while the system around it changes.
Adjacent evidence — a cloud-native marketplace, cited for transaction-system reliability, not labeled a microservices engagement.
Silicon Prime is a Stanford-rooted Responsible AI and engineering lab, founded in 2011, run by founder Kelvin Tran — 20+ years of production engineering, personally accountable for every engagement. We’ll tell you plainly when a monolith should stay a monolith — which a firm paid by the service won’t.
We argue for fewer services when that’s right. The expensive mistake is over-splitting into a distributed monolith. We draw boundaries on evidence — scaling pressure, team lines, real deploy-coupling pain — and a modular monolith is a recommendation we’ll make and defend, not a fee we avoid.
Production discipline, not a diagram. A service architecture is judged at deploy time and at 3 a.m. We bring the release cadence, staged rollout, tracing, and monitoring that make independence real — not a deck that falls apart in production.
Founder-led, one accountable lead. No account managers, no handoffs — the engineer who designs the boundaries answers for how they run.
Built to transfer, whole-stack in-house. Services, contracts, manifests, pipelines, and code are assigned to you under full work-for-hire IP transfer, and your team is trained to run them. And because the services also need infrastructure (cloud engineering), a secure delivery path (DevSecOps), and sometimes a migration (cloud migration), we run all of it — so the architecture isn’t stranded between vendors.
Isolating payments, ledger, and decisioning into services with their own failure boundaries and audit trails, so a change to one carries contained, traceable risk. Fintech software →
Decomposing catalog, cart, checkout, search, and pricing so the modules that spike on peak days scale independently and deploy without freezing the storefront. Ecommerce software →
Extracting multi-tenant platform services (auth, billing, notifications) for reuse across products, the pattern behind transaction-heavy marketplaces like the one we built for YardClub.
What teams want to know before they decompose a system they depend on.
Often, no — and we’ll say so. A monolith becomes a liability only when specific pains appear: teams blocked behind each other’s releases, one component forcing you to scale everything, or a single deploy that can take the whole system down. If those aren’t biting, a modular monolith — clean internal boundaries, one deployable — gets you most of the benefit at a fraction of the operational cost. We scope the real pain first and recommend the smallest architecture that solves it; Amazon’s own Prime Video team cut costs over 90% by re-consolidating an over-split service (Prime Video Tech).
By the business domain, not the technical layers. We model the system into bounded contexts — the seams where data ownership and team responsibility genuinely separate — and draw services there, so each one owns its data and changes for one reason. Boundaries drawn along technical tiers (a “UI service,” a “database service”) are how teams end up with a distributed monolith, and we steer hard away from that.
This page is the architecture itself — decomposition, contracts, inter-service communication, resilience. Cloud engineering builds the infrastructure the services run on (Kubernetes, networking, IaC); cloud migration moves an existing system onto that platform; and DevSecOps secures the pipelines that ship each service. They’re separate engagements that fit together — we run all of them in-house, so you’re not stitching the architecture together across vendors.
They can — that’s the real risk, and why most of this work is deciding what not to split. Done right — boundaries on real seams, each service independently deployable and scalable — they’re what lets elite teams deploy 182× more often with an 8× lower change-failure rate (DORA 2024). We instrument cost and delivery metrics from day one so the trade is measured, not assumed.
Both, sized to the service count. A handful of services rarely needs a mesh; once you’re running many, a mesh earns its keep handling service-to-service traffic, retries, and mutual TLS. We containerize and orchestrate on Kubernetes as the default, add a mesh only when the topology justifies it, and won’t push tooling whose complexity you’d pay for without using.
Each service owns its data behind its own interface — no shared database silently re-coupling them — and consistency across services is handled with events and sagas rather than distributed locks. Service-to-service traffic runs under scoped, mutually-authenticated access, security scanning and policy gates sit in each service’s pipeline via our DevSecOps practice, and every engagement starts with an NDA and a security review.
You do — completely. Services, API contracts, Kubernetes manifests, per-service pipelines, tracing dashboards, and code transfer under full work-for-hire IP assignment signed at kickoff, and your team is trained to own, deploy, and extend each service. Keep us on a reduced retainer or take the keys; the engagement is built around the handover.
Most engagements reach an independently-deployable steady state in 4–8 weeks under a fixed-scope agreement with one accountable lead — a first decomposition slice in production, not the entire system at once. Cost depends on how many services and how much live-system traffic must be migrated; our AI and software development cost guide gives real ranges, and we scope the decomposition order so value lands early rather than at the end.
Thirty minutes · No pitch deck
Bring the monolith and the pain it’s causing — we’ll tell you honestly which seams are worth cutting, which to leave alone, and what independently-deployable services would change for your release cadence.