Three major outages in 2025 looked unrelated, but all were triggered by the same hidden architectural weakness. This post breaks down how tiny internal assumptions inside AWS, Azure and Cloudflare cascaded into global failures, and why this pattern matters for anyone building distributed systems.
Cloudflare’s outage this week looked like another routine disruption.
But when compared with the Azure Front Door failure in October 2025 and the AWS DynamoDB DNS incident earlier the same month, the similarities became difficult to ignore.
These were not isolated failures.
They followed a shared structural pattern.
- Different providers.
- Different stacks.
- Different layers.
- Same failure behaviour.
Cloudflare: A Small Metadata Shift With Large Side Effects
Cloudflare’s incident had nothing to do with load, DDoS attacks, or hardware.
It began with a simple internal permissions update inside a ClickHouse cluster.
The sequence unfolded like this:
- extra metadata became visible
- a bot-scoring query wasn’t built to handle it
- the feature file doubled in size
- it exceeded a hardcoded limit
- FL proxies panicked
- bot scoring collapsed
- systems depending on those scores misbehaved
Here is the failure chain in a code-block for clarity:
[Permissions Update]
↓
[Extra Metadata Visible]
↓
[Bot Query Unexpected State]
↓
[Feature File Grows 2×]
↓
[200-Feature Limit Exceeded]
↓
[FL Proxy Panic]
↓
[Bot Scores Fail]
↓
[Turnstile / KV / Access Impacted]
A subtle internal assumption broke.
Everything downstream trusted that assumption — and failed with it.
Azure: A Tenant Rule That Propagated Too Far
Azure’s global outage was triggered by a Front Door policy rule intended for a limited scope.
It propagated globally instead.
That caused widespread routing and WAF issues across:
- Microsoft 365
- Teams
- Xbox services
- airline operations through a partner integration
Different origin compared with Cloudflare.
But the pattern was identical:
A small rule → propagated too broadly → cascaded into global downtime.
AWS: DNS Divergence → Retry Storms → Cascading Failures
AWS’s 15-hour disruption started with DNS metadata inconsistencies in DynamoDB.
Some nodes received updated records.
Others did not.
This partial state triggered:
- request failures
- internal retry amplification
- EC2 and S3 degradation
- outages on Snapchat and Roblox
- checkout issues on Amazon.com
Again, a small divergence scaled unintentionally.
The Shared Failure Pattern
Across all three incidents, the same pattern emerged:
- A small internal assumption stopped being true
- Downstream components implicitly trusted that assumption
- Cascading failures grew faster than mitigation
- Observability degraded because it relied on the same failing layer
This behaviour is increasingly common in modern cloud systems.
Why Cascading Failures Spread So Easily in 2025
Modern internet infrastructure depends on deep layering:
[User Traffic]
↓
[Edge / CDN / Proxies]
↓
[Routing / Policies]
↓
[Service Mesh / APIs]
↓
[Datastores / Metadata / DNS]
Each layer assumes predictable behaviour from the layer below.
So when an assumption breaks — metadata shape, DNS propagation, feature size — the result is:
- retry loops
- rate-limit triggers
- auth failures
- dashboard blindness
- misplaced traffic
- inconsistent partial states
By the time engineers diagnose the issue, the blast radius has often expanded fully.
Why This Matters During Peak Season
Black Friday and holiday traffic create unbearable pressure on global infrastructure.
A 5-minute outage is not actually five minutes.
It becomes:
- retry storms
- cache stampedes
- overloaded databases
- payment failures
- abandoned carts
- traffic spikes during recovery
Industry estimates place peak-season downtime at 7 to 12 million USD per minute for large e-commerce platforms.
These outages are not curiosities.
They are architectural warnings.
What Engineers Should Learn From the 2025 Outages
1. Validate internal assumptions explicitly
Never rely on silent invariants for metadata, routing scopes, or feature limits.
2. Build guardrails against silent state divergence
Especially for DNS, distributed metadata, and config propagation.
3. Treat cascading failure as a first-class failure mode
Not just single-component failures.
4. Ensure observability does not rely on the same failing layer
If your status page dies with your edge, that is not observability.
5. Expect small changes to have global effects
Any system with wide propagation boundaries needs defensive design.
Conclusion: The Internet Isn’t Failing — Our Assumptions Are
What connects AWS, Azure and Cloudflare is not their scale or architecture.
It is the fragility created by unseen assumptions.
- A metadata format.
- A DNS boundary.
- A routing scope.
- A feature file size.
Small internal details, trusted everywhere.
The internet is not fragile simply because systems break.
It is fragile because the connections between systems are stronger and more opaque than we realise.
One question for 2026:
What is the smallest assumption in your architecture that could create the widest blast radius if it stopped being true?
I’d be interested to hear how different teams think about this.
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