CLOUD COMPUTE
Cloud Architecture Built for the Workloads
You Actually Run
AI changed the cloud bill conversation overnight — but most of what runs in your cloud isn’t AI. We help mid-market teams design environments that handle every workload you depend on, from line-of-business apps to GPU inference, without blowing this quarter’s plan.
By Chris Gerhardt · Liftoff Consulting
Vendor-independent by design. No referral fees shaping the recommendation. Just the right architecture for your business.
The 2026 Cloud Compute Reality
Three pressures hitting every mid-market technology leader at the same time.
📈
AI Workloads Eat Capacity
GPU demand, model hosting, and inference at scale break the cost models you designed three years ago for SaaS-like workloads.
💸
Cloud Bills Are Under the Microscope
CFOs and boards now treat cloud as a top-five line item. You’re being asked to grow capability while shrinking spend.
🧩
One Cloud Doesn’t Fit All
AWS, Azure, GCP, and specialty providers each have strengths. Picking the wrong one for a given workload is now a six-figure mistake.
What You Get
A clear picture of where you are, where you should go, and a sequenced plan to get there.
01
Workload Fit Assessment
We map your applications, data, and AI initiatives against the cloud platforms and services that actually fit—technically and economically.
02
Cost-Optimized Architecture
Right-sized compute, smart use of reserved capacity and spot, and FinOps guardrails so spend tracks with business value, not engineer enthusiasm.
03
Migration & Modernization Roadmap
A pragmatic, sequenced plan that respects your team’s bandwidth and avoids the all-or-nothing migration that derails most programs.
Frequently Asked Questions
AWS, Azure, or Google Cloud — which should we use?
It depends on your workloads, your team’s muscle memory, your existing licensing, and where the data needs to live. We map all of that against the platforms’ actual strengths, including AI services and data residency, and recommend the fit. Sometimes the answer is multi-cloud. Sometimes it’s consolidation.
Our cloud bill keeps growing. What can you do about it?
A typical mid-market cloud spend has 25–40% waste hiding inside it — over-provisioned compute, idle resources, missed reserved capacity, suboptimal storage tiers. We surface the waste with FinOps discipline and put guardrails in place so the savings stick.
Do you help with cloud migrations or just strategy?
Both. We design the strategy and roadmap, then either oversee your team’s execution or coordinate with implementation partners. We don’t lift-and-shift servers ourselves — we make sure the right partner does it the right way.
How does AI change cloud strategy?
AI workloads have different gravity than traditional apps — data locality, GPU availability, egress costs, and model lifecycle all matter. We help you decide what to build, what to buy, and where to run it before the platform locks you in.
Should we move everything to the cloud?
No. Some workloads are cheaper, faster, or more compliant on-premises or in a colocation facility. Smart cloud strategy is honest about where cloud helps and where it doesn’t. We protect you from both over-cloud and under-cloud thinking.
Related Services
Most engagements touch more than one of these. Here’s how they connect.
Get a Cloud Strategy Your CFO Will Sign Off On
Bring us your AWS/Azure/GCP bill, your AI roadmap, and your budget pressure. We’ll come back with a plan you can actually execute.
Frequently Asked Questions
AWS vs Azure vs Google Cloud — how should a mid-market company choose?
Choose based on the workloads you actually run, not the vendor with the loudest sales motion. Azure tends to win where Microsoft 365 and Active Directory are already deeply embedded. AWS is usually the default for custom application development and the broadest service catalog. Google Cloud has real advantages for data analytics and certain AI workloads. The right answer for most mid-market companies is one primary cloud and a deliberate exception for the workload that genuinely fits somewhere else.
Why is our cloud bill higher than we expected?
Three reasons account for most of it: oversized instances that were never right-sized after the migration, storage tiers that were never optimized, and idle resources that nobody owns. A typical mid-market cloud bill carries 20–35% waste. The fix is a structured cost review, not a panic-driven repatriation.
Should we move workloads back from the cloud (repatriation)?
Sometimes. Predictable, high-throughput workloads with stable utilization — databases, certain analytics jobs — can be cheaper on-premises or in colocation. But repatriation only pays off when the workload economics are clear and the operational model can support it. It is not a default answer to a high cloud bill.
How does AI change cloud architecture decisions?
AI workloads change the cost curve and the data gravity. GPU inference can dominate a cloud bill quickly. Training workloads have very different egress and storage profiles than line-of-business apps. The cloud you chose for ERP and email is not necessarily the right home for AI inference at scale.
What does a cloud cost optimization engagement actually deliver?
A defensible report identifying where the spend is going, where the waste is, and what to do about it — with the trade-offs spelled out. Typical findings include right-sizing recommendations, storage tier changes, reserved-instance or savings-plan strategy, and idle-resource cleanup. The work is repeatable and the savings are measurable.
Will Liftoff get a commission from AWS, Microsoft, or Google?
No. We do not take referral fees, partner commissions, or platform incentives. The recommendation reflects what fits your business, not what pays us on the back end.