Sardine AI Status Page

Jul 24, 2026 · resolved Jul 24

This incident has been resolved.

Jul 16, 2026 · resolved Jul 16

The issue has been resolved.

Jul 15, 2026 · resolved Jul 15

**Summary** On 2026-07-15, API requests experienced elevated error rates and increased tail latency \(p99\). Median latency \(p50\) was not affected and there was little effect on 95th percentile latency \(p95\), so most requests performed normally. We began investigating at 14:34 UTC and applied a fix at approximately 19:56 UTC, after which performance returned to normal. **What happened?** Through the affected period, a portion of API requests returned errors, responded with reason code SITO \(Sardine internal timeout\) or timed out rather than completing, with intermittent spikes during which a larger share of requests were affected. **Why did it happen?** A scaling configuration prevented part of our platform from adding capacity as traffic increased. As traffic rose through the day, available capacity was exhausted, causing timeouts and errors for some requests. **What are we doing about this?** We corrected the configuration at **19:56 UTC**, which immediately restored normal performance. We are also making the affected part of the platform more resilient to sudden traffic increases and adding proactive monitoring so we can detect and respond to this class of issue faster in future. We apologize for the disruption.

Jun 30, 2026 · resolved Jun 30

This incident has been resolved.

Jun 25, 2026 · resolved Jun 25

### Summary Due to a database performance issue, API calls to `/v1/customer` , `/v1/issuing/risks` and `/v1/feedbacks` in June 25th faced increased latency. ### What happened One of the database we use to load certain customer and transaction data experienced slow latency for queries used in our customer and feedback APIs, causing latency issues on those APIs from 15:20 UTC to 16:23 UTC at June 25th. We've identified the root cause and are working on fixes to mitigate the issue. ### Why it happened One of our clients has very high volume and a particular usage pattern that didn't match well with our current database design for this data, causing the entire cluster to degrade in performance. ### What We’re Doing About It We are actively working on a solution to this issue, which includes moving the affected customer/transaction data to more performant databases, eliminating the risk altogether. In parallel we are also looking into actions to gracefully handle the usage patterns that cause degradation in the entire cluster.

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