Operations | Monitoring | ITSM | DevOps | Cloud

This report presents status data for eight major AI systems during January 2026: Anthropic, Cohere, DeepSeek, Google Gemini, Groq Cloud, OpenAI, Perplexity, Replicate, and xAI. The data includes official incidents reported on provider status pages and detection information from IsDown's monitoring system.

January 2026 saw a wave of high-impact service disruptions across social platforms, telecom providers, developer tools, education services, and streaming apps. In several cases, StatusGator detected problems minutes or even hours before providers publicly acknowledged them, and in many cases, providers never acknowledged them at all. Unfortunately, many providers still do not have public status pages, leaving users with little visibility into what is happening during an outage.

PostgreSQL ODBC drivers are no longer background components. For teams running BI, reporting, and ETL on PostgreSQL, the drivers directly affect how fast queries run, how reliably dashboards refresh, and whether data pipelines remain stable as usage grows. As PostgreSQL moves deeper into analytics stacks, these capabilities are driving the demand for these tools, a trend reflected in broader ODBC market growth.

You may have heard that keeping an existing customer is five times cheaper than acquiring a new one. But that isn’t always true. “Hidden costs” often accompany customer retention, loyalty, and increasing “share of customer”. Could you be spending more on customer retention than on winning new customers? This quick guide will walk you through the meaning of Customer Retention Cost (CRC), why it’s important to calculate it, and how to calculate it.

By incorporating observability tools into your stack, you can better understand how your complex infrastructure operates, reduce downtime, and empower developers to identify and fix problems quickly. However, it now takes considerably more work, time, and money to build the best observability tools for your infrastructure and applications. According to a Splunk survey, over half of the firms polled employ eight or more observability tools.

Exponential smoothing is a time series forecasting method that uses an exponentially weighted average of past observations to predict future values. In other words, it assigns greater weight to recent observations than to older ones, allowing the forecast to adapt to changing data trends. In this post, we’ll look at the basics of exponential smoothing, including how it works, its types, and how to implement it in Python.

We’re delighted to announce a strategic partnership between Kosli and TeamTopologies - a collaboration that brings together SDLC Governance automation with the world’s leading framework for organizing business and technology for fast flow of value.

Kubernetes has become the default foundation for a lot of modern application infrastructure. It’s powerful, flexible, and widely supported, which makes it an obvious starting point for many teams building a cloud-native application platform (a standardized way for teams to deploy, run, secure, and operate applications in production). But there’s a distinction that often gets lost early in the decision process: Kubernetes is a framework. It is not a platform.

While there are many benefits to traditional cloud storage solutions, sometimes enterprises need a more scalable way to manage and access large amounts of unstructured data. So while cloud storage may be the perfect solution for small businesses, larger teams or enterprises should consider object storage to meet their storage needs without worrying about high costs, data loss, or compliance issues.