Architect, build, and harden production systems on AWS, Azure, or GCP.
We design and build the cloud your business runs on: a well-architected foundation, defined in code, secured to your bar, handed to you to own.
New platform, migration, or a runaway bill — we engineer it on the cloud that fits, in your account. Steady state in 4–8 weeks.
Because “moving to the cloud” gets treated as a destination instead of an engineering discipline. Workloads get lifted onto bigger instances than they need, networking and identity are wired by hand and never reviewed, nothing is defined as code — so the environment can’t be rebuilt, audited, or reasoned about.
Meanwhile the monthly invoice climbs while reliability doesn’t. The promised savings never show up, and the architecture becomes a thing the team is afraid to touch.
The cloud genuinely rewards the companies that engineer it well: McKinsey puts the prize at more than $1 trillion in run-rate EBITDA across the Fortune 500 by 2030, with the average industry able to lift EBITDA by over 20% — but only for those that pursue it aggressively and build it right (McKinsey & Company, “Cloud’s trillion-dollar prize is up for grabs,” 2021).
The gap between the chain that captures that and the one that just gets a bigger bill is the engineering: architecture, infrastructure-as-code, security, and cost discipline. That gap is exactly what cloud engineering services exist to close.
Cloud engineering isn’t one deliverable — it’s a set of concrete builds, each fixing a specific way cloud projects go wrong. For each: what it does, the benefit it produces, and a one-line illustration of the help.
Designs the foundation — compute, data, networking, identity, and the failure boundaries between them — against the reliability, security, and cost trade-offs your workload actually has. Benefit — a system that scales and survives instead of one that’s over-built or fragile, because capacity, redundancy, and blast-radius are decided deliberately, not by default.
For example, a checkout service is architected across multiple availability zones with a clear failover path, so a single data-center hiccup degrades gracefully instead of taking the storefront down at peak.
Defines every resource — networks, clusters, databases, permissions — in version-controlled code (Terraform, CloudFormation, or the platform-native equivalent) so the environment is built, reviewed, and rebuilt from a repository, not clicked together in a console. Benefit — environments become repeatable, reviewable, and disaster-recoverable, so standing up a new region or rebuilding after an incident is a pipeline run instead of a multi-day manual scramble.
For example, a team that took days to hand-configure a new staging environment provisions an identical one in minutes from the same code that runs production — and a misconfiguration shows up in a code review instead of in an outage.
Moves workloads off aging on-prem or legacy infrastructure with the right strategy per app — rehost, re-platform, or re-architect — sequenced so nothing critical goes dark mid-move. Benefit — lower infrastructure cost and a platform you can actually build on, instead of a like-for-like copy that just relocates the old problems.
For example, a legacy database under capacity strain is migrated to a managed, auto-scaling service over a planned cutover, so it stops being a 2 a.m. pager risk and the team stops patching servers by hand.
Builds identity, network segmentation, encryption, secrets management, and audit logging into the architecture from the first commit — and codifies the controls a regulated workload has to prove. Benefit — security and auditability are designed in, not bolted on after a finding, which is the difference between passing an audit and scrambling through one.
For example, every resource is provisioned with least-privilege access and encryption by default through the same IaC, so a compliance reviewer reads the policy from the code instead of trusting that someone remembered to set it.
Right-sizes resources, applies commitment and autoscaling strategies, and makes spend visible per team and per service — so the bill maps to value, not to forgotten idle capacity. Benefit — the cloud bill comes under control and stays there, turning an opaque, climbing invoice into a number the team can forecast and defend.
For example, an over-provisioned, always-on cluster is right-sized and set to scale to demand, so the off-peak hours stop billing for capacity nobody is using.
Keeps the foundation healthy after launch — observability, scaling, patching, backup, and incident response — wired so problems surface as alerts, not as customer complaints. Benefit — the platform stays reliable and current without a fire drill, so capacity and failures are handled before they reach users.
For example, a traffic surge triggers automatic scale-out and an alert, instead of a degraded site and a frantic late-night call to add servers by hand.
This is the architect-and-build layer: the cloud foundation your applications run on. It sits next to — not on top of — the CI/CD pipelines and day-2 operations a delivery team runs daily. The scope below is what separates a cloud you own and understand from one you’re merely renting.
We design the architecture — compute, data, networking, identity, failure boundaries — against your reliability, security, and cost targets, and pressure-test it with a well-architected review before a line of it gets built. The honest “you don’t need this much for this workload” call is included.
We codify every resource in version-controlled IaC (Terraform, CloudFormation, or the platform-native tool) so your environments are built, reviewed, and rebuilt from a repository — repeatable, auditable, and recoverable — not assembled by hand in a console.
We plan and execute migration off legacy or on-prem infrastructure with the right strategy per workload — rehost, re-platform, or re-architect — sequenced for a safe cutover, drawing on a 12-year track record of modernizing live systems without taking them offline.
We build least-privilege identity, network segmentation, encryption, secrets management, and audit logging into the architecture from the first commit — and codify the controls regulated workloads in fintech and healthcare have to prove.
We right-size resources, apply commitment and autoscaling strategies, and make spend visible per team and service — so the bill maps to value and stays forecastable, against the 84% who can’t get spend under control.
We instrument the platform for health, scaling, and cost, set up backup and incident response, and train your team to operate it — read the dashboards, run a deploy, recover an environment from code — so you own the capability when we step back.
What you get when you hire us — all assigned to you under full work-for-hire IP
The same delivery model behind all our engineering work, tuned for cloud — one accountable lead, fixed scope, no handoffs.
Map your current footprint, workloads, constraints, and the reliability and cost targets you’re being held to.
Output: a target architecture & the metrics we’ll be judged on
Design the well-architected foundation across compute, data, network, identity, and cost, and validate it through a structured review before building.
Output: an architecture, an IaC plan & a migration sequence
Provision everything as infrastructure-as-code in your own cloud account, with security and observability wired in from the first commit, and migrate workloads against a safe cutover plan.
Output: a running, codified environment behind your controls
Run it in production with monitoring, scaling, and incident response in place, and train your team to own the platform.
Output: a reliable cloud foundation & a team that operates it
AWS, Azure, or Google Cloud — chosen to fit the workload, not the vendor we prefer
Most engagements reach steady state in 4–8 weeks, full IP assignment signed at kickoff, payment tied to the platform we agreed to deliver — not billable hours.
We won’t claim a case study we don’t have — so here is the genuinely relevant record rather than a wall of logos, with the closest fit first.
A contractor-to-contractor marketplace for heavy construction equipment, built end to end as a cloud-native SaaS platform — listings, payments, and the transaction infrastructure underneath. $120M+ processed, acquired by Caterpillar in 2017. The closest fit: cloud engineering at the part that’s least forgiving — money moving through a system that has to stay correct and available at scale.
TechCrunch ↗
A sports-tech platform we’ve built and carried since 2012 — through multiple rounds of re-platforming, legacy migration, and performance optimization without the product ever going offline, now used by USC, the LA Rams, and MLB and MLS teams. Migrating live systems without downtime is exactly the discipline a cloud migration lives or dies on.
bridgeathletic.com ↗
A 200+ location chain whose operations are software-critical, where our Aegis AI process moved release cadence from every two weeks to twice a week with zero critical defects sustained across 4+ years. The infrastructure-as-code, staged rollout, and monitoring that make a cloud platform safe to change are the same discipline behind that record.
bjsrestaurants.com ↗Silicon Prime is a Stanford-rooted Responsible AI lab, founded in 2011, run by founder Kelvin Tran — 20+ years of production engineering, including multimillion-dollar systems for one of the world’s largest automobile manufacturers, personally accountable for every engagement. We’ll tell you plainly when a workload doesn’t belong in the cloud, or doesn’t need the architecture a vendor would sell you — which a firm billing by the managed seat won’t.
What sets our cloud engineering services apart is that you end up owning a platform you understand, not renting one you depend on us for.
Cloud-neutral by design. We engineer on AWS, Azure, or GCP and choose on your workload and constraints — never on a reseller margin or a partner quota. The architecture serves you, not a vendor relationship.
Everything as code, everything yours. The Terraform modules, the architecture decisions, the runbooks — all version-controlled and assigned to you under full work-for-hire IP. No black-box environment only the agency can touch.
Built to transfer, not to retain you. We train your team to operate, rebuild, and extend the platform when we step back. You own the capability; the dependency is optional.
Founder-led, one accountable lead. No account managers, no handoffs — the person who scopes the architecture answers for it in production.
Production discipline, proven over years. A multi-year record of keeping software dependable in production — and the honesty to right-size the build to your actual problem instead of over-architecting it.
Payments, real-time-decisioning, and transaction systems where availability, encryption, and a full audit trail aren’t optional — the part of cloud engineering we proved at marketplace scale. Fintech software →
Clinical and operational workloads inside HIPAA-compliant architectures, where least-privilege identity, encryption, and audit logging are designed into the infrastructure from the first commit. Healthcare software →
Storefronts and order systems architected to absorb peak traffic and scale to demand, with cost engineered so off-peak hours don’t bill for idle capacity.
Long-lived products that need to modernize and re-platform without ever going offline, the way we’ve carried a live platform since 2012.
What teams want to know before they let us engineer the cloud they run on.
Cloud engineering is architecting and building the foundation — the well-architected design, the infrastructure-as-code, the migration, the security and cost model your applications run on. DevOps is the CI/CD pipelines and day-to-day delivery that run on that foundation; managed cloud is keeping it healthy afterward. We do all three, but they’re distinct jobs — and we’ll scope which one your problem actually calls for. Often a team has good delivery practices but a cloud foundation that was never engineered, which is squarely cloud-engineering work. (For the model-serving and ML-pipeline layer specifically, see our MLOps services.)
Whichever fits your workload, estate, and constraints — and we’ll make that call with you, not for a partner quota. AWS for the broadest managed-service catalog and migration tooling; Azure where a Microsoft-centric estate and identity already anchor you; Google Cloud where data, analytics, and Kubernetes-native workloads dominate. We engineer across all three and only recommend multi-cloud when there’s a concrete reason — because it multiplies your operational surface, and that cost is real.
That’s a core part of what we do. We plan migration per workload — rehost, re-platform, or re-architect — and sequence the cutover so nothing critical goes dark. The discipline of migrating and modernizing live systems without taking them offline is exactly what we’ve done on a platform that’s stayed in production since 2012. We’ll be honest about which workloads are a clean move and which need re-architecting first.
Cost engineering is part of the build, not a cleanup project. We right-size resources, apply commitment and autoscaling strategies, and make spend visible per team and service so the bill is forecastable and maps to value. It matters because even cloud-mature organizations struggle here — 84% call managing cloud spend their top challenge and self-report ~27% of spend as wasted (Flexera, 2025). Engineering the cost model in from day one is how you stay off that list.
Security is designed into the architecture from the first commit, not bolted on after a finding. We build least-privilege identity, network segmentation, encryption, secrets management, and audit logging as infrastructure-as-code — so the controls are readable from the repository — and every engagement starts with an NDA and a security review. For regulated workloads we codify the specific controls fintech and healthcare have to prove, so an auditor reads the policy from the code rather than trusting it was set by hand.
Because a console-clicked environment can’t be reviewed, audited, or rebuilt reliably — and that’s where outages and surprise costs live. With infrastructure-as-code, every change goes through code review, a new environment is a pipeline run instead of a multi-day manual rebuild, and a disaster-recovery rebuild is repeatable rather than improvised. It’s also how the platform transfers cleanly to you: the repository is the environment, so you own something you can actually operate.
You do — completely. The infrastructure-as-code, architecture decisions, runbooks, and dashboards transfer under full work-for-hire IP assignment signed at kickoff, and your team is trained to operate, rebuild, and extend the platform. The engagement is built around the handover — keep us on a reduced retainer for managed operations, or take the keys. There’s no black-box environment only we can touch.
Most engagements reach steady state in 4–8 weeks under a fixed-scope arrangement with one accountable lead, and payment is tied to the outcomes we agreed to deliver — a reliable, cost-controlled, owned platform. Build cost depends on scope and the size of the migration — our AI development cost guide covers how we scope and price engineering work — and we model the ongoing cloud run cost before building, so the monthly bill is a forecast you’ve already seen.
Thirty minutes · No pitch deck
Bring the migration, the architecture problem, or the runaway cloud bill — and we’ll tell you honestly what it takes to engineer it right, on which cloud, and what it costs to run.