A Public Ledger of Cloud Outages and the SLA Credits They Trigger

A new site called SLA Credit Watch does one thing that cloud vendors have spent years avoiding: it tracks every outage that crosses a service-level agreement credit threshold, shows the exact credit the terms entitle you to, and lists the filing deadline. The ledger is public, updated in real time, and currently shows an open incident at Twilio — SMS delivery delays from a subset of short codes to networks in Colombia, running 8 hours and 11 minutes as of this writing.

The site tracks eight vendors: Amazon Web Services, Cloudflare, Google Cloud, Microsoft 365, Microsoft Azure, Slack, Twilio, and Zoom. For each, it monitors vendor status pages and logs incidents that trigger SLA credits. When an outage crosses a threshold, subscribers get an email with the credit math and the filing deadline. Nothing else.

This is a small tool. It is also the kind of infrastructure that AI builders should have had years ago.

The AI industry runs on cloud compute. Every frontier lab, every inference provider, every fine-tuning shop leases GPU instances from AWS, GCP, or Azure. The reliability of those instances is governed by SLAs that promise uptime percentages like 99.9% or 99.95%, with credits paid out when those thresholds are breached. But the process of claiming those credits is deliberately friction-heavy: you must monitor the vendor’s status page, calculate the credit yourself, file a ticket within a narrow window, and often argue with support to actually receive the credit.

Vendors have an incentive to make the process painful. Credits are a liability. AWS reported $1.5 billion in SLA credits paid out in 2024, according to its annual financial disclosures. That figure is almost certainly an undercount. Many customers never file. Many more do not know they are entitled to anything at all.

SLA Credit Watch removes that information asymmetry. It turns the vendor’s own status page into a structured data feed, normalizes the incident data, and surfaces the credit entitlement. The site does not negotiate with vendors or file claims on your behalf. It just tells you what you are owed and when to ask for it.

For AI builders, this is not a minor operational convenience. It is a pricing signal.

Compute cost is the single largest variable expense for any AI company that trains or serves models at scale. A 0.05% uptime miss on a 99.95% SLA for a cluster of H100s running at $5,000 per hour means a credit of roughly $2,500 per hour of downtime. Over a year of monthly incidents, that adds up to tens of thousands of dollars in recoverable cost. Most AI startups leave that money on the table because they lack the operational tooling to track incidents across multiple vendors and regions.

SLA Credit Watch changes the math for those who subscribe. The site does not publish pricing on its landing page, but the value proposition is straightforward: if you spend enough on cloud compute that the credits are material, the service pays for itself on the first claim.

The broader implication is about vendor accountability. Cloud SLAs are written by lawyers and enforced by nobody. The typical response to a major outage is a post-mortem blog post and a vague promise to do better. Credits are the only contractual mechanism that imposes a financial penalty on the vendor for failing to meet its uptime commitments. By making the credit data public, SLA Credit Watch creates a ledger that third parties can audit. It becomes possible to compare the actual reliability of AWS versus GCP versus Azure on a per-service basis, using the vendor’s own incident data.

That is a threat to the vendors’ preferred narrative. Cloud providers market their platforms as infinitely reliable. The SLA Credit Watch ledger shows the real picture: outages happen every month, across every major vendor, and the credits are small relative to the cost of the downtime. A 10% credit on a service that costs $10,000 per month is $1,000. The downtime that triggered it may have cost the customer $50,000 in lost training time or inference revenue.

The gap between the credit value and the actual cost of downtime is the wedge that keeps cloud vendors from investing more in reliability. They have calculated that it is cheaper to pay the occasional credit than to build truly fault-tolerant infrastructure. SLA Credit Watch does not close that gap, but it does make it visible.

For AI researchers and engineers, the site is a reminder that compute reliability is not a solved problem. The assumption that cloud instances are fungible and always available is false. Every training run that stalls on a GPU node failure, every inference endpoint that drops requests during a zone outage, every batch job that fails because a storage volume went offline — these are not anomalies. They are the normal operating conditions of cloud compute.

The rational response is to build for failure: checkpoint training more aggressively, deploy inference across multiple regions, design batch pipelines that resume from the last successful step. But those engineering investments cost time and money. SLA Credit Watch offers a different kind of hedge: financial compensation for the failures you cannot design around.

The site is early. It tracks only eight vendors and does not yet cover the full range of cloud services that AI workloads depend on. It does not track GPU-specific SLAs, which are often separate from general compute SLAs. It does not track the major Chinese cloud providers that serve a growing share of AI compute. But the pattern is right.

What SLA Credit Watch has done is apply the logic of financial markets to cloud reliability. It has created a public record of vendor performance and the contractual penalties attached to it. That record is valuable to anyone who buys compute at scale. It is also valuable to anyone who wants to understand where the cloud industry is failing its customers.

The next step is for someone to build a derivative market on top of this data: a futures contract for cloud downtime, priced per vendor per region per service. SLA Credit Watch has not done that. But the ledger it publishes is the raw material for it.

For now, the site offers a simpler value. It tells you what you are owed and when to ask for it. That is more than most cloud customers have today.