Service · Architecture
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 — and we'll tell you which boundaries to leave alone.
One deploy becomes many
The real problem
Why the monolith eventually becomes the thing slowing you down.
Not because monoliths are bad — most start as the right call. It becomes the bottleneck when every small change ships against the whole app, one team's deploy is blocked behind another's, a single hot component forces you to scale the entire stack, and one bad release takes everything down at once.
Microservices fix this by making the unit of deployment small and independent — but only if the boundaries are right. Cut the wrong seams and you get a distributed monolith: all the latency and overhead, none of the independence. Getting the boundaries right is the whole engineering problem.
More frequent deploys for elite delivery teams than low performers — the throughput independently-deployable services unlock.
DORA 2024 ↗
Infrastructure cost cut when Amazon re-consolidated an over-split service back to one unit — the cost of getting the seams wrong.
Prime Video Tech, 2023 ↗
Where it earns the complexity
Where microservices earn the complexity they add — and what each delivers.
A microservice is justified by a specific pressure on the system, not by fashion — each pattern with what it does and how it plays out.
Independent deployment of a fast-moving module
Pull the part that changes most — checkout, pricing, recommendations — into its own service and pipeline. That team ships on its own cadence; risk shrinks to one service.
Pricing pushes at 10 a.m. and 3 p.m. without the monthly release — and a bad change rolls back pricing alone.
Independent scaling of a hot component
Give the component that spikes — search, media, an events firehose — its own service, scaling on its own resources. Scale the 10% that's hot, not the whole monolith.
Search runs twenty replicas during a sale while accounts stays at two — not twenty copies of everything.
Fault isolation for a critical path
Wrap a path that must not take the system down — payments, auth — in a service with its own failure boundary and circuit breakers. A failure degrades one feature, not the app.
If recommendations time out, the product page and checkout still work — a page without "you may also like," not an error.
Team-aligned ownership (bounded contexts)
Draw boundaries around business domains so one team owns one service end to end — code, data, deploy, on-call. Throughput rises as the org grows, not stalls.
Catalog and orders each own their service and ship in parallel — no shared release train or database migration.
Polyglot & incremental modernization
Stand up new capability as a service alongside the monolith and strangle legacy out one piece at a time. Modernize continuously, system always live.
A new fraud-scoring service ships in the stack best suited to it and routes traffic gradually; legacy retires once it's proven.
Reusable platform services
Extract cross-cutting capability — auth, notifications, payments, search — into shared services behind a stable API. Built once, reused; new products ship faster.
A second product line reuses the existing payments and notification services through their APIs — weeks off the launch.
As of June 2026 · revisit quarterly
What independently-deployable services do to delivery and cost — the measured evidence.
Independent industry findings — cited as third-party evidence, not Silicon Prime's own client results.
More frequent deploys for elite teams — with 127× faster lead time and ~8× lower change-failure rate.
DORA 2024 ↗
Faster revenue growth at top-quartile Developer Velocity companies — with 20% higher margins; loosely-coupled architecture a named driver.
McKinsey, 2020 ↗
Reported some success with microservices — but teams that own the full lifecycle succeeded at an 18% higher rate (n=1,502).
O'Reilly, 2020 ↗
What's included
What microservices development covers.
The difference between independently-deployable services and a distributed monolith harder to run than what you started with.
Domain decomposition & boundary design
We map the system to its business domains and draw boundaries along bounded contexts — the real seams, not arbitrary technical layers, arguing for fewer services when the evidence says so.
API contracts & inter-service communication
Each service exposes a versioned, documented contract — synchronous APIs where a caller needs an answer now, async events where it doesn't, so a slow service can't stall its dependents. Contract tests keep the seams honest.
Data ownership (database-per-service)
Each service owns its data behind its own interface — no shared database silently re-coupling everything. We design the boundaries, the migration off a shared schema, and the patterns (events, sagas) that keep data consistent without distributed locks.
Containerization & Kubernetes orchestration
Services are containerized and orchestrated on Kubernetes — health checks, autoscaling, rolling and canary deploys, per-service limits — on the foundation your cloud engineering sets up.
Service mesh, resilience & observability
Where the service count justifies it, a mesh handles service-to-service traffic, retries, timeouts, and mutual TLS. Circuit breakers, bulkheads, and graceful degradation are designed in, and distributed tracing follows a request across the call graph.
Decomposition strategy for an existing monolith
For a live system we use the strangler-fig pattern — stand up new services alongside the monolith and route traffic over incrementally, so it stays up and each cut is reversible. Pairs with cloud migration across platforms.
Secure delivery & per-service pipelines
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, not bolted on.
What you get — all assigned to you under full work-for-hire IP
How it runs
How a microservices engagement runs.
The same delivery model behind all our engineering work, tuned for decomposition — one accountable lead, fixed scope, no handoffs.
STEP 01
Map
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
STEP 02
Carve
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
STEP 03
Build
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
STEP 04
Operate & enable
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
Track record
The production discipline a service architecture lives or dies on.
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.
A Stanford-rooted Responsible AI and engineering lab, founded 2011, run by founder Kelvin Tran. We'll tell you plainly when a monolith should stay a monolith — which a firm paid by the service won't.
Release discipline · 200+ locations · 4 yrs
BJ's Restaurants — frequent, low-risk, recoverable deploys are exactly what microservices promise, and the discipline we run as standing practice: releases moved from every two weeks to twice a week with zero critical defects across four years.
Transaction reliability · acquired 2017
YardClub — the least forgiving thing in a distributed system is money moving through it. We built this contractor-equipment marketplace end to end — listings, payments, transaction infrastructure — processing $120M+ before Caterpillar acquired it.
Why decompose with us.
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 IP, and your team is trained to run them. Because the services also need infrastructure, secure delivery, and sometimes a migration, we run all of it — so the architecture isn't stranded between vendors.
Where it pays off first
Where service decomposition pays off first.
Fintech
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 →Ecommerce
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 →SaaS & marketplaces
Extracting multi-tenant platform services (auth, billing, notifications) for reuse across products — the pattern behind transaction-heavy marketplaces like YardClub.
SaaS software →Questions buyers ask before they decompose.
Thirty minutes · no pitch deck
Ready to ship one service at a time instead of the whole system at once?
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.