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Leaving the cloud can make sense when a company has a large, predictable workload, already has people capable of operating production systems, and can show that owning or colocating infrastructure costs less after hardware, facilities, staffing, resilience, and migration are counted. It is not a universal way to save money: cloud capacity can be valuable for uncertain demand, sharp recurring peaks, managed services, or rapid expansion into new regions.
Cloud repatriation—the move of workloads from public-cloud services to owned or colocated infrastructure—changes who operates the machines and how capacity is obtained. It does not make operations, security, or recovery someone else’s problem.
Why are companies leaving the cloud?
Some companies reconsider cloud when recurring compute, database, storage, or managed-service bills remain high relative to a workload that is steady enough to size in advance. With owned hardware, costs shift toward buying servers, reserving data-center capacity, replacing equipment, and operating the system. Whether that trade is favorable depends on the workload and the organization—not on a general rule that rented infrastructure is always more expensive.
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37signals, the company behind Basecamp and HEY, made that case for its own business. In an October 19, 2022 essay, co-owner and CTO David Heinemeier Hansson described the company as medium-sized, with stable growth, and argued that cloud had not reduced its operations headcount. He wrote that HEY was paying more than $500,000 per year for Amazon database and search services at that time. That is a historical company figure for 2022, not a current price or a benchmark for other businesses.
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The company’s February 2023 estimate put its 2022 cloud spending at $3.2 million, including just under $1 million to store 8 petabytes in S3. Its initial effort targeted compute and related server costs; leaving S3 was planned separately. In February 2023, 37signals projected about $7 million in server-expense savings over five years. Its later cloud-exit overview estimates about $10 million in infrastructure savings over five years and a 50–66% cost reduction. Those are company estimates, not independently audited results or a forecast for other organizations.
These figures describe different dates and scopes: historical 2022 spending, an early 2023 server-savings projection, and a later estimate of infrastructure savings. They should not be combined into a single realized-savings figure.
Is cloud computing always cheaper?
No single price comparison settles the question. A cloud bill may include services that would have to be replaced or operated after a move, while ownership brings costs that are easy to omit from a server purchase price. Compare equivalent workloads over the same time horizon and include the people and resilience needed to keep them running.
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| Decision factor | Cloud may fit better when… | Owned or colocated infrastructure merits analysis when… |
|---|---|---|
| Workload | Demand is uncertain, highly irregular, or has very large recurring peaks. | Baseline demand is stable and growth is predictable enough to size capacity. |
| Cost | Fast provisioning or managed services justify their recurring cost. | Recurring infrastructure charges remain substantial after optimization. |
| People | The organization lacks operational capability and needs managed services to get started. | Existing staff can operate production infrastructure without assuming unrealistic staffing savings. |
| Resilience | Provider services simplify a design the team cannot otherwise operate. | The organization can fund and operate replicas, backups, failure testing, and geographically separated capacity. |
| Speed and geography | Capacity must be available quickly in multiple locations. | Procurement lead times are acceptable, with cloud capacity or a CDN retained where useful. |
| Migration scope | Provider-specific services make exit costly or risky. | The team can replace dependencies, move data, validate behavior, and operate the destination. |
A useful model compares the full cost over an explicit period. 37signals said its own cost model assumed a five-year server life; that is an assumption from its case, not a universal replacement schedule. A company’s model should account for servers and replacement, colocation and connectivity, power where applicable, software and support, data transfer, staff time, backups, spare or failover capacity, and the cost and risk of migration.
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The case does not establish a neutral break-even point or a general percentage of companies that save money by leaving cloud. A company estimate can show why one organization chose to move; it cannot substitute for modeling another organization’s usage, service mix, geography, and operating capability.
What does cloud repatriation actually involve?
It is more than copying virtual machines to physical servers. The target environment must provide the compute, storage, database, search, networking, deployment, monitoring, backup, and recovery functions the application depends on. Provider-specific managed services may need to be replaced, and application behavior must be checked after data and traffic move.
37signals’ account describes a move from public-cloud virtual machines and managed services toward owned servers in professional data centers. The company says it used open-source alternatives for database and search, retained its existing operations team, and operated redundancy across two sites. This illustrates that leaving a cloud provider does not require building a private data center; a colocation provider can supply the facility while the customer owns and operates the servers.
A practical sequence for evaluating a move
- Map the bill to workloads. Separate compute, databases, search, storage, data transfer, and other services. Match costs to usage patterns and identify which charges persist even when demand is low.
- Document dependencies. List managed services, provider-specific APIs, deployment assumptions, data formats, network rules, and operational tools. Identify what must be replaced or changed before an application can run elsewhere.
- Model the destination over a stated life. Estimate hardware purchases and replacement, colocation, connectivity, staff time, support, backup and failover capacity, and migration costs. Compare like-for-like service levels rather than comparing a cloud bill with a server quote.
- Design failure recovery before moving production. Decide how backups are restored, how a site outage is handled, and whether a second location can carry required load. Test recovery rather than treating a second copy of data as proof that the service is resilient.
- Move a bounded workload first. Choose a service with manageable dependencies, validate performance and behavior, and test deployment, monitoring, backups, and recovery in the destination. Use the results to update the cost and risk model before expanding scope.
- Plan the cutover and rollback. Define how data stays consistent during transition, how traffic shifts, what success looks like, and how to return to the prior service if validation fails. Account for any period when both environments must run.
This sequence is practical guidance based on the work a repatriation entails; it is not a published 37signals migration checklist or a universal migration plan.
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What did 37signals move, and what did its design require?
In April 2023, 37signals reported receiving 20 Dell R7625 servers across its Chicago and Ashburn locations. It described their combined capacity as nearly 4,000 vCPUs, 7,680 GB of RAM, and 384 TB of Gen 4 NVMe storage. Those are company-published figures for its own fleet, not a recommended configuration for a typical business.
The company’s December 2023 FAQ says the move was completed in June 2023. It reports that the operations team did not change in size and that each of its two data centers could carry the full required load, with critical infrastructure replicated. The company’s stated approach illustrates the central trade: a smaller infrastructure bill, if achieved, is paired with responsibility for capacity, redundancy, hardware procurement, deployment, and operations.
Hansson wrote, “Reliability is largely a function of redundancy,” in the context of the company’s multi-site design. That is his and 37signals’ explanation of their approach, not a guarantee that redundancy alone ensures reliability. Replicas, backups, geographic separation, and recovery procedures have to be designed and tested for the failures a business needs to withstand.
What are the trade-offs of leaving?
Capacity arrives more slowly
37signals’ FAQ says bringing new physical capacity online can take weeks rather than seconds. That difference matters if demand is unpredictable or a product may need to scale quickly. A company can retain cloud for bursts while running a stable baseline on owned capacity, but it must account for the complexity and cost of operating both.
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Operations continue
Repatriation does not automatically reduce staffing or eliminate routine infrastructure work. 37signals says its operations team stayed the same size and describes continuing responsibilities that include redundancy, security, deployment, colocation, and buying hardware. Its staffing outcome is specific to its team and migration; it is not evidence that another company can make the move without additional expertise or labor.
Security responsibilities change, but do not disappear
Moving machine ownership does not remove application and dependency security work. 37signals’ FAQ argues that many security issues arise in applications and dependencies regardless of who owns the machines. That is the company’s position; the move alone does not establish that owned servers are inherently safer or less safe than cloud infrastructure.
Geographic reach may require a mix
37signals says it uses a CDN for international content delivery. That is an example of combining owned infrastructure with a service that helps distribute content; leaving a cloud provider need not mean abandoning every external infrastructure service. The relevant question is which pieces are economical and operationally sensible to retain.
When should you stay, leave, or use a hybrid?
Stay in the cloud when flexibility or managed services earn their cost
Cloud can be a useful starting point for a simple early product, as well as for workloads with highly irregular demand. Hansson’s essay describes these as cases where cloud can excel; his view is an argument from 37signals’ experience, not a consensus rule. It may also suit teams that lack the skills or capacity to operate the alternative reliably.
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Evaluate ownership when the baseline is substantial and predictable
Owned or colocated infrastructure deserves a serious comparison when a large, stable workload generates recurring costs and the company already has the expertise to operate production systems. Stability alone is not enough: the full cost model must include replacement, facilities, people, resilience, and the work of moving dependencies and data.
Consider a hybrid when the baseline and peaks differ
37signals’ FAQ suggests cloud or a “buy the baseline, rent the spike” approach when demand regularly jumps by 5–10x or more. Treat that as the company’s suggested pattern, not a universal threshold. Hybrid infrastructure can preserve flexibility for peaks, but it also creates integration, monitoring, and cost-allocation work across environments.
The decision is ultimately a fit question: compare demand shape, full costs across an explicit hardware life, staff capacity, recovery requirements, migration risk, geography, and provisioning speed. Choose the operating model the organization can sustain, not simply the one with the most attractive headline price.
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