Backblaze Q2 2026: What a 1.73% Failure Rate Means

Backblaze’s Q2 2026 Drive Stats report puts the analyzed fleet’s annualized failure rate at 1.73%. The report, published September 29, covers April 1 through June 30 and includes 354,415 data drives after exclusions. Annualized failure rate is a rate derived from time in service; it is not the percentage of those drives that failed during the quarter.

That difference matters when choosing disks for a NAS or a local-AI archive. A large fleet offers useful observations, but interpreting them requires the denominator, the drive ages and the conditions behind each row.

Reconstruct the rate before ranking drives

Backblaze’s total row records 1,498 failures across 31,553,350 drive-days. Applying a 365-day annualization gives 1,498 × 365 ÷ 31,553,350 × 100 = approximately 1.73%. That calculation reproduces the rounded published figure.

By comparison, dividing failures by the listed drive count yields roughly 0.423%. That is a different denominator and should not be substituted for the exposure-based rate. Drives enter and leave service, so a count alone does not describe how long the fleet was exposed to failure.

Think of drive-days as the amount of observation behind a result. One disk running for ten days contributes less exposure than one running for a full quarter. Combining the time helps compare groups whose membership changed, although it does not eliminate differences between them.

A high rate can come from an old fleet

The report flags three high-rate models and discusses their ages and sample sizes. It also lists three models with zero quarterly failures. Neither outcome alone establishes a universal model ranking: a zero is an observation over a finite period, and an older cohort has a different history from a newly deployed one.

Suppose two models have the same recorded failure rate, but one has ten times the exposure. The numbers look equally precise in a table; the evidence behind them is not equally strong. A ranking that ignores exposure loses that distinction.

There is another practical mismatch. A cloud-storage deployment and a home NAS can differ in workload, cooling, vibration, power cycling and maintenance. The report does not measure the reliability of your particular enclosure. Its fleet results are a research input, not a prediction for an individual disk.

Three zeroes contain different amounts of evidence

The report’s three zero-failure rows provide a useful worked example. They all display a quarterly AFR of zero, but their total exposure differs. We calculated an illustrative one-sided 95% upper bound using the constant-rate Poisson model described in NIST’s reliability handbook.

With zero observed failures, the chance of seeing zero under a rate λ over exposure D is exp(−λD). Setting that chance to 0.05 gives an upper rate of −ln(0.05) ÷ D. Multiplying by 365 and 100 expresses that rate on the same annualized percentage scale. It is a rate bound under the model, not an annual failure probability for an individual disk.

These are EyesTech calculations from the published exposure totals, not confidence intervals supplied by Backblaze. The same observed zero leaves a much wider range plausible in the row with less observation time. That is why sorting the AFR column alone loses information.

The model assumes independent failures and a constant underlying rate over the analyzed exposure. Real drive cohorts can age, share environmental stresses and change membership. The bounds illustrate sampling uncertainty; they do not account for all those differences or certify a purchase. They also do not say there is a 95% probability that a fixed unknown rate lies below the bound.

Recovery time can matter more than a small AFR difference

For a home storage plan, the useful consequence is how long data stays unavailable and whether it can be recovered. Consider a hypothetical 12 TB of decimal data copied at a sustained 150 MB/s. Even with no overhead, 12,000,000 MB ÷ 150 MB/s takes 80,000 seconds, or about 22.2 hours. Small files, competing I/O, verification and network limits can extend it.

This is arithmetic rather than a measured NAS rebuild. It shows why a failure percentage cannot tell you whether a backup plan meets your recovery-time requirement. Measure a representative restore, including finding the backup, authenticating and verifying the recovered files. A replica that cannot be accessed when the primary machine is down is a dependency worth fixing.

For local AI, separate replaceable model downloads from private datasets, adapters and irreplaceable work. EyesTech’s local-hardware analysis by Jaxson Reed explains why storage capacity and working memory solve different constraints. A large disk library does not establish that a model fits in memory or loads quickly enough for the workflow.

Reproduce the denominator before extending the conclusion

Backblaze publishes its daily drive-test data and field documentation. A deeper comparison could group the exact model IDs, accumulate exposure and inspect age and deployment changes. Check the schema and exclusions before combining quarters; matching capacity labels are insufficient.

We reproduced the aggregate AFR and the illustrative zero-failure bounds from the report’s table. We have not independently processed the full daily dataset for this article. That would be necessary before claiming an age-adjusted ranking or a newly discovered failure pattern.

Use the report to improve a storage plan

When considering a listed drive, record its exact model identifier, cohort age and observation period. Check whether a striking quarterly result persists across longer periods before making it decisive. Avoid treating every disk sold under a capacity or brand name as the same hardware.

For an existing NAS, prioritize the consequence of failure as well as its likelihood. Keep recoverable copies of important files, verify a restore, and plan how a replacement will be obtained. For a local model library, decide which weights can be downloaded again and which datasets or custom artifacts cannot.

Backblaze supplies unusually useful fleet evidence. The most valuable number is often the time behind a percentage.