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Selecting the correct data center (DC) impacts performance, compliance, reliability, and cost.
  1. Latency and throughput
    • Placing a machine close to the primary users or connected systems reduces round-trip time and can significantly improve interactive workloads (e.g. remote desktops, APIs, low-latency inference).
    Example: A user in Germany will usually experience lower latency from a VM in a Frankfurt data center than from one in North America.
  2. Regulatory and data residency requirements
    • Certain workloads (e.g. handling personal data subject to GDPR) may need data to remain within a specific legal jurisdiction. Choosing an in-data center data center helps meet data residency, audit, and compliance obligations.
  3. Cost considerations
    • Compute and storage pricing can vary by data center.
    • Keeping tightly coupled services in the same data center reduces inter-data center data transfer costs.
  4. Hardware and feature availability
    • Not all data centers offer every GPU/CPU type, storage tier, or GPU generation. Newer hardware typically appears in a limited set of data centers first.
  5. Scaling and capacity planning
    • Some data centers may have higher capacity for burst scaling. Selecting a data center with adequate headroom reduces risk of quota or capacity delays for large fleet expansions.
  6. Data locality for pipelines
    • Analytics, training, or inference pipelines that depend on large datasets perform better when compute and storage are co-located, minimizing cross-data center replication overhead.
  7. Security and network architecture
    • Shorter network paths reduce exposure surface and simplify monitoring. Data center choice can align with existing SOC, SIEM, or zero-trust boundary designs.
  8. Environmental and sustainability factors
    • Data centers differ in grid carbon intensity and renewable energy mix. Selecting a lower-carbon data center can support sustainability reporting.
  9. User experience and SLA alignment
    • Meeting latency SLAs (e.g. p95 < X ms) often hinges on geographic proximity. Data center choice should be validated with real RTT measurements, not assumptions.
Practical guidance:
  • Identify primary user clusters and measure baseline latency (e.g. ICMP + application RTT).
  • Map compliance/data residency constraints early; this can eliminate data centers.
  • Verify required instance types and quotas in target data centers.
  • Model total cost (compute + storage + interconnect).
  • Design a multi-data center failover plan if uptime requirements demand it.
  • Run a small benchmark (network, CPU/GPU, storage I/O) in candidate data centers before committing.
In short, pick the data center that balances latency, compliance, cost, hardware availability, and resilience.