Infrastructure placement should follow the workload: how demand changes, where data lives, what the application depends on, and who will operate it. Cloud, colocation, and hybrid designs can each be sensible answers. The useful comparison is the cost and complexity of delivering the same performance and recovery targets, including the people needed to keep the service running.
Start with demand and useful capacity
Measure normal demand, peaks, idle periods, and growth before choosing a platform. Include CPU, memory, storage throughput, latency, and GPU utilization where relevant. A server can look inexpensive per core while leaving most cores idle or waiting for storage.
Predictable, sustained workloads are candidates for owned hardware. Variable demand, experiments, and short-lived environments may benefit from cloud elasticity. A stable baseline with independently scalable workers can justify a mixed approach, provided those workers can reach their data efficiently.
Cloud also supports committed purchasing. AWS describes EC2 Savings Plans as hourly spending commitments lasting one or three years. Model the commitment your baseline can actually consume. Amazon EC2 overview.
Decide which responsibilities your team can own
Colocation puts your equipment in a third-party facility. Equinix's overview distinguishes customer ownership of servers from rented data center space and describes supporting power, cooling, and on-site assistance. Equinix colocation overview.
That control can matter for particular GPUs, storage controllers, drive layouts, or network interfaces. Assess the whole specialist platform, including memory capacity, interconnects, software support, replacement parts, and expected upgrade cadence. Hardware control also brings procurement, firmware, repairs, and lifecycle planning.
Managed services can change the staffing calculation. For example, AWS states that FSx handles hardware provisioning, patching, and backups. Replacing that service means accounting for the operating work as well as storage capacity. Amazon FSx overview.
Name the people responsible for monitoring, patching, incident response, and recovery. Remote hands can perform agreed physical tasks; your team still needs operational ownership. If buying hardware is premature, consider hosted bare metal or VPS hosting while demand becomes clearer.
Compare total cost over the same planning horizon
Use a common period and equivalent service targets. Compare delivered transactions, completed jobs, or usable storage at the required latency, rather than treating a virtual CPU as interchangeable with a physical core. Include capacity reserved for failures and growth.
Build two complete models:
- Colocation: equipment, support contracts, spares, financing, rack space, power, connectivity, cross-connects, remote hands, software, labor, backups, and disaster recovery. Spread equipment cost over a realistic useful life, with explicit assumptions about refreshes and residual value.
- Cloud: compute under realistic commitments, unused commitments, storage capacity and operations, data transfer, support, managed services, licenses, labor, backups, and disaster recovery. Include network components and duplicated capacity required by the architecture.
Add migration engineering and the period when both environments run. Separate monthly operating expense from upfront cash needs. Avoid counting both equipment depreciation and the full purchase price as expenses in the same comparison.
Run low, expected, and high-demand scenarios. Colocation is not automatically cheaper: low utilization, early hardware replacement, or additional staffing can erase an apparent advantage. Cloud commitments can also become expensive when demand moves or disappears.
Follow the data before moving compute
Large datasets create data gravity: applications become costly or slow to separate from the information they use. Map each transfer's direction, volume, frequency, and destination, including replication, backups, analytics, and recovery traffic.
Storage price alone misses part of the bill. Amazon S3 lists storage, requests, retrievals, transfer, and replication among its cost components. Check the applicable service and route rather than assuming every transfer has the same treatment. Amazon S3 pricing.
Place tightly coupled processing near its data unless measurements support the separation.
Make hybrid dependencies explicit
A hybrid design adds a connection between operating environments. Specify bandwidth, latency, routing, encryption, and failure behavior with your connectivity plan. Measure application response times over the intended path, including repeated database calls and replication lag.
Trace identity, DNS, secrets, logging, and deployment dependencies. Can the colocated application authenticate if its cloud identity service or connecting link is unavailable? Can operators reach either environment during an incident?
Cloud is not inherently unreliable, and two locations do not automatically provide resilience. Identify shared failure points, decide which services must continue independently, and test loss of the link as well as loss of a host.
Pilot a placement you can reverse
Start with a representative workload that has clear boundaries. Record performance, transfer volume, actual spending, and operator effort against the existing environment.
Define rollback triggers before migration. Preserve a working deployment path, establish how writes will be reconciled, and budget for overlap. Test restoration from backup into a clean environment and measure recovery time and data loss against your targets.
Use the pilot evidence to request an infrastructure plan with workload, capacity, connectivity, and recovery requirements attached.