Talk to us
Whether you're buying, selling, partnering, or investing — pick what fits and our team will get back to you within one business day.
A real human, fast
Someone on our team replies within one business day — no bots, no ticket queue.
Routed to the right team
Buying, selling, partnering, or investing — you reach the people who can actually help.
Independent & unbiased
No pushy sales. Just honest guidance grounded in the ecosystem.
Tailored to your context
Tell us what you need and we shape the next steps around it.
Who are you? Pick the option that fits best.
The cloud was the only answer for a decade. Now the smartest teams are asking a sharper question — should some of it come back?
The short version: Cloud repatriation — moving workloads back on-prem or to colocation — is not a rejection of the cloud. It is a correction. For steady, high-volume workloads (and increasingly heavy AI inference), owning infrastructure can be far cheaper and more predictable than renting it. The 1% do not pick a side; they put each workload where it is cheapest to run well.
For a decade, "move to the cloud" was the only acceptable answer. In 2026, a quieter, more interesting question is spreading through boardrooms and engineering all-hands: should some of this come back? For a growing number of companies, the answer is yes — and it is saving them a fortune.
Cloud repatriation is moving applications and data out of the public cloud back to on-premises servers, private cloud, or colocation facilities. The key word is some. This is rarely an all-or-nothing exit; it is a workload-by-workload decision to relocate the systems where cloud economics stopped making sense.
This is not a fringe idea. In a widely cited essay, "The Cost of Cloud, a Trillion Dollar Paradox," investors Sarah Wang and Martin Casado of Andreessen Horowitz argued that at scale, cloud spend can meaningfully suppress margins — and that repatriation can recover them. The most public example is 37signals (maker of Basecamp and HEY), which left AWS and reported large multi-year savings after buying its own hardware.
This is the fresh part. Just as the cloud-versus-own debate seemed settled, AI reopened it. Training and, especially, continuous inference are compute-hungry, and renting GPUs at cloud rates gets expensive fast. For organizations running heavy, always-on AI workloads, owning or colocating GPU capacity can be dramatically cheaper. If your AI usage is scaling, model the build-versus-rent math before you sign another year of GPU rental — the logic mirrors classic build-vs-buy.
Repatriation is a trap if you treat it as a trend instead of a calculation. Stay in the cloud when your workloads are bursty or seasonal, when you are still finding product-market fit, when you need global reach quickly, or when you lean heavily on managed services you would otherwise have to run yourself. And remember what the cloud quietly did for you: capacity planning, hardware refresh, and resilience. Take those back only if you have the team to do them well. Our public vs. private vs. hybrid cloud guide is the right starting frame.
Opinion, clearly labeled. "Cloud-first" and "cloud-only" got blurred into the same slogan, and it cost companies dearly. The 1% treat infrastructure like any other buying decision: no religion, just total cost of ownership per workload. Sometimes that is the hyperscaler. Sometimes it is a rack you own. The advantage is in refusing to pick a permanent side.
Cloud repatriation is moving workloads and data out of the public cloud back to on-premises hardware, private cloud, or colocation. It is not an anti-cloud stance — it is a workload-by-workload decision to run steady, predictable, high-volume systems where they are cheaper and more controllable, while keeping variable or elastic workloads in the cloud.
Mainly cost, performance, and control. At steady, large scale, public-cloud pricing can exceed the cost of owning hardware, and egress fees and premium managed services add up. Some teams also repatriate for latency, data residency, or predictable capacity. A widely cited example is 37signals (Basecamp/HEY), which publicly left AWS and reported large multi-year savings.
No. The public cloud remains the right default for early-stage products, spiky and unpredictable demand, global reach, and access to managed services. Repatriation is a correction at the margins for mature, steady workloads — the pendulum finding balance, not swinging fully back.
Steady-state, predictable, high-volume workloads with stable capacity needs — think large databases, storage-heavy systems, and increasingly GPU-intensive AI inference at scale. Workloads that are bursty, seasonal, or still finding product-market fit usually belong in the cloud.
AI inference and training at scale are compute-hungry and expensive on rented GPUs. For organizations running heavy, continuous AI workloads, owning or colocating GPU capacity can be dramatically cheaper than paying cloud rates — one reason repatriation is back on the agenda in 2026.
You take back responsibility for capacity planning, hardware, resilience, and the operational skills the cloud abstracted away. Repatriation only pays off with honest total-cost modeling and the team to run infrastructure well. Done casually, it trades one set of costs for another.
Tags
The 1% Stack
Saaskart's media & intelligence series for software buyers, founders, and operators — opinionated takes on SaaS, AI agents, and the stacks that separate the 1% from everyone else.
Explore thousands of vetted tools, AI agents, and service providers on Saaskart — compare features, pricing, and real buyer reviews in one place.
