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Cloudflare freed 100 TB of memory: when a server needs optimization, not a new plan

A fresh analysis of Cloudflare reveals how a single data structure inflates memory usage. I examine the metrics a website owner needs before scaling up server capacity.

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Memory modules and neatly stacked metal blocks on a dark surface
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On September 18, 2026, Cloudflare announced it had reduced memory (RAM) usage in its infrastructure by more than 100 TB. The cause of the overspend was a data structure in its Pingora-based load balancing service. Fixing the algorithm produced an effect that, across thousands of servers, translated into a massive number.

For a small online store, the scale is different, but the question remains the same: why is there not enough memory? I would start a conversation about a new plan by answering that. Additional gigabytes are useful when the workload grows. However, if an application holds data uncontrollably, increasing the limit only delays the next failure.

Used memory does not necessarily mean a problem

A graph showing high consumption explains little on its own. Part of the memory may be occupied by useful cache. It is more concerning when response time increases simultaneously, the system starts using the disk heavily instead of RAM, or processes are terminated due to resource shortages.

Ask the administrator to show resource consumption by service: database, site handlers, search, and background jobs. You need data from a period of normal operation and a traffic peak. A snapshot taken on a quiet Sunday evening does not answer what happens during a promotional campaign.

Three scenarios with different solutions

First scenario: memory runs out only when exchanging data with the accounting system. In this case, examine the size of a single batch of imported products, the level of parallel loading, and the schedule. If import, backup, and catalog recalculation start simultaneously, the issue may lie in the organization of these tasks.

Second scenario: memory usage gradually increases even with steady traffic, then drops sharply after a restart. This is a sign to look for held objects, unfinished tasks, and unlimited caches. Regular restarts can temporarily sustain operations, but they do not explain the root cause.

Third scenario: each process behaves predictably, but the number of buyers has grown and all resources are occupied with useful work. In this case, increasing server capacity or separating services may be justified. It is also unwise to save on the server at the cost of lost orders.

What should be included in an optimization task

A good task includes initial measurements, a problematic scenario, and acceptance criteria. For example: when the catalog is loaded and orders are being placed simultaneously, the site must respond within the established time limit, and handlers must not terminate abnormally. A reduction in memory usage alone does not prove an improvement.

After a change, compare identical conditions: the number of visitors, catalog volume, and enabled integrations. Otherwise, it is easy to attribute the result to optimization when it actually occurred simply because a sale ended.

When a tariff increase is immediately necessary

If the site is already losing orders, a temporary reserve of resources may be the fastest way to restore operations. It is important to record which problem this addresses and when the team will return to diagnostics. For long-term changes, a test copy and the ability to roll back are essential.

The Cloudflare figure is the result of specific engineering work, not a promise of the same savings for any VPS. A useful takeaway for a site owner is this: before committing to permanent increased spending, determine exactly what the additional gigabytes are purchasing—business growth or a delay in fixing a bug.

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