Choosing a Data Center Location: Why Latency Matters

Most people think “a data center is a data center” and that geography is just a marketing bullet. I learned the hard way that a few extra milliseconds on the wrong side of an ocean can tank conversions, ruin game sessions, and make “fast” hardware feel slow.

The short version: pick data centers physically close to your heaviest traffic, on networks with strong peering to your users’ ISPs, and test real-world latency before you sign anything. CPU, RAM, and NVMe do not matter if every packet is making a 10,000 km round trip through congested routes.

What latency really is (and why you feel it before you see it)

Latency is the time it takes for data to travel from your user to your server and back. It is measured in milliseconds (ms) and usually referred to as RTT (round-trip time).

People obsess over throughput (how many MB/s), when the thing that actually makes a site or app “feel” fast is latency:

• Throughput: how much data you can push per second.
• Latency: how long it takes to get the first byte there and back.

A 1 Gbit pipe with 200 ms latency can feel slower than a 100 Mbit pipe with 20 ms latency for web apps, APIs, and games, because every click, every API call, every handshake waits for that RTT.

The speed of light is your hard limit. Every extra kilometer adds delay that even the most “premium” plan cannot erase.

The physical distance between your data center and your user is not just a map problem. Routing, undersea cables, and congestion all pile on. This is why one provider in Frankfurt can give 15 ms to London, while another gives you 40+ ms from the same city.

How distance and physics shape your latency

The marketing story says “we are connected to Tier 1 networks around the globe.” The physics story says “light in fiber travels at about 200,000 km/s, and your packets take indirect routes.”

Here is what that means in practice.

Speed of light vs real-world routes

Light in vacuum: about 300,000 km/s.

Light in fiber: about 200,000 km/s (roughly two thirds of vacuum).

So the absolute best case one-way latency over 1,000 km of fiber is:

1000 km / 200,000 km/s = 0.005 seconds = 5 ms (one way)
RTT theoretical minimum = ~10 ms

In reality, your packets do not go in a straight line:

• The cable paths are not straight (rights of way, terrain, politics).
• Routers, switches, and queuing add overhead at every hop.
• ISPs may send traffic through transit partners instead of direct peering.

So a “theoretical” 10 ms RTT often turns into 20 to 30 ms in real routes.

Now stretch that across continents:

Route	Distance (approx)	Theoretical RTT min	Typical real RTT
London ↔ Frankfurt	~650 km	~7 ms	10-20 ms
New York ↔ London	~5,600 km	~56 ms	70-90 ms
San Francisco ↔ New York	~4,100 km	~41 ms	60-80 ms
New York ↔ Singapore	~15,000+ km	~150 ms	220-280 ms

Once you cross an ocean, you are in a different category of latency. No amount of caching inside the same region fixes the round-trip delay between continents.

If your users are on one continent and your data center is on another, you are starting every interaction with a handicap of 100+ ms.

Network “distance” vs map distance

Two data centers in the same city can have very different latency profiles to the same user base because of:

• Transit providers: Tier 1 vs regional vs bargain-bin carriers.
• Peering: Direct peering with user ISPs vs sending everything through a third party.
• Congestion: Overloaded uplinks, oversold ports, poorly managed traffic shaping.

So “Frankfurt” or “Singapore” in the control panel does not guarantee good performance. You need to look at who they are connected to and test from the networks your users actually use.

Why latency matters more than people admit

Latency shows up everywhere: page loads, API calls, video calls, game ticks, SSH sessions. The effects are not theoretical; they hit revenue and user retention.

Websites and e-commerce

Every time a browser needs something from your origin, it pays a latency tax:

• DNS lookup.
• TLS handshake.
• HTTP request, response.
• Subsequent API calls for dynamic content.

Even with HTTP/2 or HTTP/3, where multiple requests share a connection, the initial handshake and each new connection to a different domain pay that RTT.

Some rough impact numbers:

Extra RTT	Perceived UX	Risk
+20-30 ms	Barely noticeable in isolation	Adds up over many calls, can drop conversion a few percent
+80-120 ms	Site feels “sluggish”, more time to first interaction	Higher bounce rates, impatience on mobile
+200+ ms	Heavy delay, “click and wait” feeling	Cart abandonment, bad SEO signals, frustrated users

A server 150 ms away means just three blocking round trips already cost almost half a second before any real work happens.

If your checkout flow hits multiple internal services across regions, the accumulated latency can silently eat seconds.

APIs, SaaS apps, and microservices

API-heavy apps suffer more than static sites, because they chat back and forth:

• Single-page apps calling JSON APIs after load.
• Microservices that talk to each other on every user action.
• Mobile apps with chattier protocols or retries on spotty connections.

Put your data center on the wrong continent and your SPA turns into a spinner farm. Place a microservice in another region “for redundancy” without changing call patterns, and your internal graph becomes latency bound.

This is where bad architectural decisions surface: a service that worked fine in one data center suddenly struggles when a new region is introduced, because developers assumed sub-2 ms latency between everything.

Gaming, real-time comms, and collaboration

For games and real-time apps, latency is not a nice-to-have; it is the core requirement.

Rules of thumb:

• 0-30 ms: Feels instant for human perception.
• 30-60 ms: Still acceptable for most games and calls.
• 60-100 ms: Noticeable lag, tolerable for casual use.
• 100-200 ms: Very noticeable, only acceptable for turn-based or non-interactive tasks.

Gamers will complain loudly about 80 ms latency. You can argue with them, or you can put your servers closer to them.

Voice and video can hide certain delays with jitter buffers and echo cancellation, but once you go past 150-200 ms, conversations start to feel awkward, with people talking over each other.

Admin tasks: SSH, RDP, and database work

If you manage servers, latency directly affects your life:

• SSH sessions across oceans feel laggy; typing and editing is unpleasant.
• Remote desktop over high latency is jerky, with visible input delay.
• Database consoles with many round trips per query become painful.

You can live with 150 ms latency to a box you touch once a month. You do not want that between your developers and the systems they use all day.

How to map your users to the right data center

You cannot place servers well if you do not know where your users really are and how they connect.

Step 1: Inspect your traffic geography

Your analytics tool is not just for marketing. It should guide your infra choices.

Look at:

• Sessions by country / region: Top 5 regions by user count.
• Revenue / conversions by region: Where the money comes from.
• Existing latency: If you have RUM (real user monitoring), check “time to first byte” by region.

Example scenario:

Region	Traffic share	Revenue share	Current avg TTFB
US East	45%	50%	120 ms
Western Europe	25%	30%	90 ms
Southeast Asia	15%	10%	320 ms
Other	15%	10%	200+ ms

If your only data center is in Europe, your US East numbers are already worse than they need to be, and Asia is clearly suffering.

Step 2: Identify realistic latency targets

You do not need single-digit ms to everyone. You need “fast enough” relative to what you are building.

Some practical targets:

• Main commerce market: Aim for sub-50 ms RTT to typical users of that market.
• Game regions: Try for sub-30 ms inside a metro area, sub-60 ms across a country or small region.
• Internal traffic between services: Sub-1-2 ms if tightly coupled, 10-20 ms if you are comfortable treating it like a remote service.

If you design your system assuming 1 ms between services then deploy them 80 ms apart, the architecture is wrong, not the network.

Step 3: Test providers from the right vantage points

Do not trust synthetic benchmarks from a user’s laptop sitting next to the data center. Test from where your users actually are.

Use:

• Cloud-based ping / traceroute from multiple cities.
• Real user monitoring beacons built into your app or site.
• External monitoring services with probes across ISPs.

You want to see:

• Median latency per region.
• 95th / 99th percentile latency per region.
• Jitter and packet loss under peak load hours.

If one provider has 30 ms median but nasty spikes to 500 ms during evenings, while another offers a stable 40 ms, the second one is actually better for user experience.

Single region vs multi-region: tradeoffs and traps

The fantasy: “We will add more regions, users will be closer, everything will be faster.” The reality: more regions help, but they also introduce complexity that many teams are not ready for.

Single data center (or region)

Pros:

• Simpler architecture, single source of truth for data.
• Easy deployments and rollbacks.
• No cross-region database replication headaches.

Cons:

• Remote users pay heavy latency for every interaction.
• Cross-continent users have visibly worse UX.
• Regional outages affect everyone.

This model is fine:

• If your user base is concentrated in one region.
• If your product is not latency sensitive.
• While you are still small and fast-moving.

Multi-region read-only, single write region

This is the next step: multiple data centers with:

• Local read replicas for low-latency reads near users.
• Single primary region for writes to avoid conflicts.

Pros:

• Static and cached content is served close to users.
• Most read-heavy operations are fast everywhere.
• Consistency is mostly managed centrally.

Cons:

• Writes still pay latency to the primary region.
• Failover between primary regions is non-trivial.

This model works for many SaaS apps where users do not notice an extra 80 ms on write, but they do care about initial load and navigation speed.

Full multi-region active-active

Now you are running multiple active data centers that all accept reads and writes, often with geo-partitioned data.

Pros:

• Lowest possible latency for both reads and writes per region.
• Region-level failures can be survived without global outage.

Cons:

• Data consistency, conflicts, and replication logic grow complex.
• Deployment and rollback are more difficult.
• Debugging cross-region issues takes real expertise.

This setup only makes sense if:

• Your user base and revenue justify the engineering cost.
• Your app truly needs low write latency across continents.
• You have the team to manage it.

Adding more regions without redesigning your data model is how you end up with “global” infrastructure that still feels slow and fails in odd ways.

Content delivery networks vs data center placement

CDNs help with latency, but they are not a silver bullet.

What CDNs actually fix

A CDN puts edge servers near users and caches:

• Static files (images, JS, CSS, fonts).
• Static HTML pages or fragments.
• Sometimes dynamic content with cache rules.

This reduces:

• Latency to first byte for cached responses.
• Load on your origin data center.
• Exposure to packet loss and congestion between user and origin.

For media-heavy sites or marketing pages, a well-tuned CDN can make many latency issues feel smaller.

What CDNs cannot fix

They do not solve latency for:

• Non-cacheable API responses.
• Real-time features (chats, WebSockets, multiplayer state).
• Authenticated user-specific content that must hit origin.

Also, not all CDN POPs are equal: some regions are just “edge locations” that still send traffic back to a regional hub before reaching your origin.

Your data center location still matters for:

• API origins.
• Database proximity.
• Service-to-service traffic.

If your origin sits in the wrong place, a CDN just hides part of the problem; it does not make a bad core architecture good.

Other factors in data center location (beyond latency)

Latency should lead, but there are several other constraints that interplay with location choice.

Regulation and data residency

Some sectors cannot just “host wherever it is fastest”:

• Privacy rules that require data to stay within specific regions.
• Industry rules for financial or health data.
• Contractual obligations with enterprise customers.

This can force you into regions that are not ideal from a pure latency point of view. In that case, your job is to:

• Minimize latency inside those legal geographies.
• Separate data that must stay vs data that can be global.

Reliability, power, and physical risk

Fast is useless if it is offline.

Look at:

• Power redundancy and generator capacity.
• History of outages for that region / facility.
• Natural risk profile: floods, earthquakes, fires.

Some “cheap” regions cost less per month but sit on weak infrastructure with poor track records. That trade can make sense for non-critical labs or test systems, but not for primary production.

Vendor ecosystem and peering

Some cities are genuine connectivity hubs:

• Big internet exchanges, lots of peering.
• Multiple carriers, easy cross connects.
• Cloud on-ramps to major providers.

Others are edge outposts with thin routes back to core networks.

For example:

City	Typical role	Connectivity profile
Frankfurt, London, Amsterdam	European hubs	Strong peering, dense core networks
Dallas, Ashburn, Chicago	US hubs	Major exchanges, many carriers
Smaller regional cities	Edge	One or two carriers, less peering

Sometimes paying slightly more to be in a better-connected hub beats chasing the absolute cheapest rack in a low-tier city.

Practical decision patterns by use case

Different workloads should push you toward different data center location strategies.

Content-heavy sites with global visitors

Think: blogs, news, marketing sites, documentation.

Reasonable approach:

• Pick one or two core data centers in well-connected hubs (for example, US East and Western Europe).
• Put almost everything behind a CDN with broad global coverage.
• Keep your admin backends in the closest region to your team.

Here, latency from user to CDN edge matters more than user to origin, because almost everything can be cached.

SaaS apps for a specific business market

If your main customers are in a clear region (for example, North America, or DACH countries), do not overcomplicate:

• Pick a primary data center close to the heaviest customer cluster.
• Consider a second region within the same broad market for DR, not for day-to-day serving at first.
• Use RUM to verify that latency is acceptable elsewhere; add regions later if adoption grows.

Spreading too early across regions wastes time on infra that does not move your product forward.

Gaming and latency-sensitive communities

For games, voice communities, and similar, location strategy changes:

• Place servers in or near the cities where your players cluster (for example, one per coast, plus EU, plus key hubs in Asia).
• Use anycast IPs or region selection to route players to their nearest region.
• Keep latency-sensitive state local; use out-of-band channels for cross-region data.

If your players are fighting each other across continents on a single shard, physics wins, not your netcode.

Internal tools and enterprise intranets

If you build tools for a company with offices in known locations:

• Place data centers where the offices are, or close to their main ISPs.
• They might have private MPLS or SD-WAN links; peering into those can matter as much as public internet paths.
• Latency budget should match how intensive the tools are (heavy UI vs simple dashboards).

In these cases, you can sometimes accept higher latency for remote workers while keeping HQ happy.

How to evaluate latency before you commit

Vendors will always show you theoretical numbers. You need real ones.

Step 1: Get test IPs and endpoints

Most serious providers can give you:

• Public “looking glass” servers.
• Test IPs or hostnames in each region.

From your and your users’ vantage points, run:

• ping for raw RTT.
• traceroute or mtr for hop paths and packet loss.

Do this at different times of day to see congestion.

Step 2: Browser-based real user tests

If you already have traffic, insert a simple JavaScript beacon that:

• Measures time to first byte from a target test endpoint.
• Sends back region and latency data to a logging endpoint.

This bypasses synthetic tests and shows you how actual visitors experience the network.

Step 3: Compare providers region by region

Build a simple table:

User region	Provider A RTT	Provider B RTT	Provider C RTT	Notes
US East	18 ms	25 ms	22 ms	A more consistent; B spikes evenings
Western Europe	30 ms	19 ms	27 ms	B has best peering to main ISPs
Southeast Asia	210 ms	190 ms	230 ms	All limited by distance, B slightly better

Do not blindly pick the provider with the lowest global average; pick the one that fits your actual user distribution and priorities.

Latency, redundancy, and cost: balancing the triangle

You will not get perfect latency, perfect redundancy, and perfect cost at the same time. Something has to give.

When to pay more for less latency

You should justify higher spend on closer regions when:

• Your revenue per user is high enough that small conversion lifts are worth it.
• Your product is inherently latency sensitive (trading, gaming, real-time collaboration).
• You have real data connecting latency to churn or lost deals.

For a low-margin content site, moving from 80 ms to 40 ms might not pay for itself. For a high-margin SaaS tool selling to enterprises, it might.

When to accept higher latency for simplicity

There are times when chasing minimal latency is a distraction:

• Your app is mostly offline or asynchronous.
• Your users interact rarely and can tolerate slight delay.
• Your team is small and already stretched on core feature work.

Here, one well-chosen region with a good CDN edge is usually better than a half-baked multi-region setup that engineers cannot support.

Red flags when choosing a data center location

Watch for these signs that a location or provider will hurt you:

• High base latency from key ISPs compared to competitors in the same city.
• Big variance (jitter) in latency during peak hours.
• Routes that bounce through distant countries for no good reason.
• No clear peering strategy or visibility into upstream carriers.
• Long-standing complaints from local users about speed and packet loss.

If your traceroute from a user in Paris to a “local” data center in Paris shows a detour through another continent, that is not just a curiosity; it will hit every interaction.

At this point, no amount of tuning inside the VM fixes the realities of the network.

Concrete checklist for your next data center choice

If you want a simple, practical set of steps, here is a checklist that does not pretend geography is just a marketing choice:

• Map your top 3 user regions by traffic and by revenue.
• Measure current latency and UX metrics (TTFB, API response times) per region.
• Shortlist data centers close to your highest value region(s) in major network hubs.
• Get test IPs and measure real RTT and jitter from those user regions.
• Evaluate provider peering and carrier mix, not just “Tier 1” labels.
• Decide if you truly need multiple regions now or just a better primary location.
• Integrate a CDN for static and cacheable content, but do not use it as an excuse for a bad origin location.
• Document your latency budget for the app (per page, per critical request) and check it against real numbers.
• Factor in legal, reliability, and cost constraints only after you narrow down latency-viable regions.

If you treat data center location as one of the core performance decisions rather than an afterthought, you avoid the classic pattern: spending years tuning code and buying bigger servers, while every packet is still taking the long way around the planet.