Build event-driven backends that scale to zero and bill only when they run.
An event arrives, code fires, work gets done, and the bill stops the moment it goes idle. No fleet to patch, no capacity to guess. We build it inside your own cloud account — AWS, Azure, or Google Cloud — with every function and decision assigned to you.
And when serverless is the wrong tool, we’ll tell you. Steady state in 4–8 weeks.
Most backends are sized for their busiest minute and billed for every minute they aren’t. A fleet provisioned for the lunch rush or the Black Friday peak runs — and bills — flat through the 3 a.m. lull.
Someone still patches the OS, renews the certificates, and gets paged when a node falls over. And when real demand finally arrives, the fixed fleet either can’t keep up or was over-provisioned so heavily that the savings never existed. You’re paying for capacity and for the labor of babysitting it, whether or not work is flowing through it.
Serverless inverts that: you write the function, the platform runs and scales it on demand, and you pay per execution — to zero when nothing is happening.
The catch is that this only pays off for the right shape of workload, and wired carelessly it trades a server bill for a per-invocation bill, a cold-start latency problem, and lock-in to one cloud’s plumbing. Getting the fit right is the entire engineering problem — and it is exactly what serverless application development is for.
Serverless isn’t one product — it’s a model that pays off in specific workload shapes: bursty, event-driven, or idle-heavy work where paying per execution beats paying for an idle fleet. For each pattern: what it does, the benefit it produces, and how that plays out.
Stands up an API or backend on functions behind an API gateway, so it scales instantly from zero to a flood and back without a fleet to pre-size. Benefit — you absorb traffic spikes without over-provisioning, and pay nothing when idle, cutting cost-per-request on uneven traffic.
Example: a campaign-landing API that sees nothing for weeks then 50,000 hits in an hour scales up automatically and bills only for that hour — instead of an always-on cluster sized for a peak that comes twice a year.
Triggers a function on every new record, upload, or message — resize the image, validate the record, enrich the event — fanning out to thousands of parallel executions when volume surges. Benefit — throughput scales with the data, and processing cost tracks volume instead of a fixed grid, with no idle workers between batches.
Example: a user uploads a document and a function generates the thumbnail and extracts the text within seconds, scaling to a thousand uploads at once during a busy hour and to nothing overnight.
Replaces always-on cron boxes and manual ops with functions that fire on a schedule or a cloud event — backups, report generation, provisioning, cleanup, alert routing. Benefit — routine operations run reliably with no server to maintain, slashing the labor and idle cost of automation.
Example: a nightly reconciliation job that used to need a dedicated VM now runs as a scheduled function for cents a month, and an account-provisioning task that took hours of manual setup runs in minutes, unattended.
Receives webhooks and events from third-party systems (payments, SaaS tools, partners) and routes, transforms, or acts on them through functions wired to your systems of record. Benefit — integrations stand up fast and scale on their own, so partner traffic never needs a babysat endpoint.
Example: a payment provider’s webhook fires a function that updates the order and notifies fulfillment instantly — an endpoint that costs nothing between events and absorbs a launch-day surge without intervention.
Reacts to an event — an order, a status change, a threshold breach — and fans out notifications, updates, or downstream actions in parallel. Benefit — reactions are immediate and scale with the event volume, improving responsiveness without standing infrastructure.
Example: an inventory threshold trips and a function simultaneously alerts the buyer, updates the storefront, and opens a reorder — work that happens in seconds and only when the threshold is actually hit.
Assembles a backend from managed building blocks — functions, a managed database, object storage, hosted auth — so a product ships without a server tier to run. Benefit — faster time to a launched product and lower operational load, because there’s no backend fleet to provision, scale, or patch.
Example: a new mobile app launches on a serverless backend that elastically scales with sign-ups from day one — the team ships the product instead of standing up and hardening servers first.
The scope below is the difference between serverless that’s cheap and fast and a function sprawl that’s slower and more expensive than the server it replaced.
We start by deciding what actually belongs on serverless. Bursty, event-driven, idle-heavy work is a fit; sustained high-throughput services, long-running jobs, and sub-10ms latency floors usually aren’t. We map your workloads, model per-invocation cost against an always-on baseline, and tell you which pieces to put on functions — run as part of our AI and engineering readiness assessment, with the honest “don’t make this serverless” call included.
We design the event flow — the sources (HTTP, queues, streams, storage events, schedules), the functions that react, and the managed services that hold state — so the system is loosely coupled and each piece scales on its own. Functions are kept stateless and single-purpose; state lives in managed stores, not in the function.
We assemble the backend from managed building blocks — functions plus a managed database, object storage, hosted auth, queues, and event buses — and wire them through your access controls, so there’s no server tier to provision, scale, or patch.
We engineer the parts of serverless that bite if ignored: cold-start latency (provisioned concurrency, runtime and memory tuning, dependency trimming), concurrency limits, and the per-invocation cost model — so the system meets its latency target and its cost target, and we know the crossover point where an always-on service would be cheaper.
Each function runs under least-privilege, scoped permissions; secrets are managed, not embedded; and the whole event flow is instrumented with distributed tracing so a request can be followed across functions and services. Security scanning and policy gates sit in each deployment through our DevSecOps practice rather than bolted on after.
The entire serverless application is defined as code (SAM, Serverless Framework, Terraform, or CDK) so it’s reproducible and reviewable, ships behind a staged rollout, and your team is trained to deploy functions, read the traces and cost dashboards, and extend the system. This builds on the cloud foundation your cloud engineering sets up rather than reinventing the platform layer here.
What you get when you hire us — all assigned to you under full work-for-hire IP transfer
The same delivery model behind all our engineering work, tuned for serverless — one accountable lead, fixed scope, no handoffs.
Map the workloads, model per-invocation cost against an always-on baseline, and decide what belongs on functions and what doesn’t.
Output: a fit verdict, a target architecture & the metrics we’re judged on
Design the event flow, the functions, the managed-service backing, and the cold-start and concurrency strategy — reviewable before code.
Output: an architecture & a cost model with the documented crossover
Develop the functions and wire the event sources and managed services in your own cloud account, defined entirely as infrastructure-as-code, with scoped IAM and tracing in place.
Output: a working serverless application behind your access controls
Staged rollout, then production, with per-function cost and cold-start latency watched against the kickoff targets, and your team trained to deploy and extend it.
Output: a production system & a team that owns it
Most engagements reach a working, production steady state in 4–8 weeks — payment tied to a system that meets its cost and latency targets, not to billable hours.
We won’t claim a serverless case study we don’t have — so here is the genuinely relevant record, each entry labeled for what it is. The closest fit is a cloud-native, event-driven build; the others corroborate the discipline serverless lives or dies on — shipping safely and staying available while load and code change.
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 workload doesn’t belong on serverless — which a firm paid by the function won’t.
We tell you when serverless is the wrong answer. The expensive mistake is forcing a sustained, high-throughput, or latency-critical workload onto functions and paying more for worse latency. We decide the fit on the cost model and the workload shape — and a container or a managed instance is a recommendation we’ll make and defend, not a fee we avoid.
Cost and cold-start engineered, not assumed. Serverless is only cheap and fast when the per-invocation economics and cold-start latency are engineered. We model both before building and document the crossover point where always-on wins — so the bill and the latency are forecasts you’ve already seen.
Founder-led, one accountable lead. No account managers, no handoffs — the engineer who designs the architecture answers for how it runs in production.
Built to transfer, whole-stack in-house. Functions, IaC, the cost model, and code are assigned to you under full work-for-hire IP transfer, and your team is trained to run them. And because serverless sits on a cloud foundation (cloud engineering), often beside microservices, and needs a secure delivery path (DevSecOps), we run all of it — so the system isn’t stranded between vendors.
Event-driven transaction processing, real-time fraud and decisioning, and webhook handling where work is bursty and every event needs a scoped, auditable function — the event-and-payments engineering we proved at marketplace scale. Fintech software →
Storefront APIs and order pipelines that scale to zero off-peak and absorb peak-day spikes without a pre-sized fleet, plus stream processing for catalog, pricing, and inventory events. Ecommerce software →
Managed serverless backends that let a new product launch and scale with sign-ups from day one, with no backend fleet to provision or patch — the pattern behind a cloud-native marketplace like the one we built for YardClub.
What teams want to know before they put real load on a serverless system.
It depends entirely on the workload, and we model it before we build. For bursty, event-driven, or idle-heavy work, paying per execution to zero when idle is dramatically cheaper than an always-on fleet — Toyota Connected scaled to 18× traffic paying only for what it consumed (AWS). But at sustained high throughput, per-invocation pricing crosses over and an always-on container becomes cheaper. We calculate that crossover for your traffic and put the right workloads on each — so the saving is engineered, not assumed.
When the workload is steady and high-volume (the per-invocation bill overtakes an always-on instance), when jobs run longer than the platform’s execution limit, when you need a hard sub-10ms latency floor (cold starts get in the way), or when heavy in-memory state makes statelessness awkward. We’ll say so plainly and put those pieces on a container or managed instance instead — often a hybrid, with the bursty edges on functions and the steady core always-on.
Cold starts are real but engineerable. We tune runtime, memory, and dependencies to shrink them, use provisioned or pre-warmed concurrency where a latency floor demands it, and keep functions lean. For most event-driven and asynchronous work the effect is negligible; for latency-critical synchronous paths we either engineer it down to target or recommend an always-on path for that piece. Either way it’s a measured decision, not a surprise in production.
Serverless is a deployment-and-cost model — functions and managed services, billed per execution, with no servers to run. Microservices is an architecture-boundary model — how you split a system into independently-deployable services; you can run microservices on servers or on serverless, and you can build a serverless app that isn’t microservices. Cloud engineering is the broader foundation — the account, network, IaC, and cost governance serverless runs within. They fit together, and we run all of them in-house, so you’re not stitching the system across vendors.
There’s a real lock-in trade-off and we manage it deliberately. Functions wired directly to one cloud’s proprietary event services are the fastest to build but the hardest to move; abstracting behind portable interfaces and open frameworks (Serverless Framework, container-based functions) costs a little up front and keeps you portable. We pick the level of portability with you based on how likely a move is — and document the proprietary dependencies either way, so the lock-in is a known, deliberate trade rather than a discovery later.
Each function runs under least-privilege, scoped IAM — its own narrow set of permissions, nothing more — secrets are managed rather than embedded, and the event flow is instrumented with distributed tracing so a request is followable across every function and service. Security scanning and policy gates sit in each deployment through our DevSecOps practice, and every engagement starts with an NDA and a security review. The function-level permission boundary is actually one of serverless’s security advantages — done right.
You do — completely. The functions, infrastructure-as-code, event-driven architecture, cost model, and code transfer under full work-for-hire IP assignment signed at kickoff, and your team is trained to deploy, monitor, and extend it. Keep us on a reduced retainer or take the keys; the engagement is built around the handover, and the IaC repository is the application — there’s no black-box environment only we can touch.
Most engagements reach production in 4–8 weeks under a fixed-scope agreement with one accountable lead. Build cost depends on scope — our AI and software development cost guide gives real ranges — and the ongoing run cost is per-invocation economics we model before building (including the always-on crossover), so the first cloud invoice is a forecast you’ve already seen rather than a surprise.
Thirty minutes · No pitch deck
Bring the workload and the bill it’s running up. We’ll tell you honestly which pieces belong on serverless, which belong on something always-on, what it takes to build, and what it costs to run.