Operations | Monitoring | ITSM | DevOps | Cloud

OpenAI doesn’t bill per feature, per customer, or per transaction. It bills per token, across multiple models, with usage patterns that can change by the hour. As a result, two API calls that support the same feature can have very different costs. Without a clear way to translate token-level pricing into something product, engineering, and finance teams can reason about, AI spend becomes difficult to forecast and harder to control.

Everywhere around us, technology is evolving at lightning speed, yet the networks which underpin these capabilities often lag behind. This gap creates vulnerabilities that can impact everything from energy grids to emergency services. Forbes recently explored this urgent issue in an article featuring insights from our CEO Bruce McClelland, who shared an informed perspective on why modernization is essential, not optional. I encourage you to take a few minutes to read the full article.

Redis is a widely used in-memory data store, commonly deployed as a cache, session store, message broker, or fast key-value database. Because Redis often sits on the critical path of an application, having visibility into its behavior (memory usage, client connections, command throughput, cache efficiency) is essential for troubleshooting and performance tuning.

Integrating feature flag context into OpenTelemetry traces enhances observability by recording flag states as span attributes, making it easier to analyze how specific flags influence application behavior. When you toggle a feature flag, you're changing the behavior of your application; sometimes, in subtle ways that are hard to detect through logs or metrics alone. By adding feature flag attributes directly to spans, you can make these changes observable at the trace level.

Flaky tests are a significant source of inefficiency that impacts many engineering teams. Along with failing your build, they interrupt your entire development flow, generate excessive CI/CD noise, and, critically, compromise developer trust in the test suite itself. Datadog Test Optimization enables you to manage test suites at scale by pinpointing the flakiest tests, analyzing their history across hundreds of runs, and automatically surfacing the root cause.

We built Bits AI SRE to help engineers investigate and solve production incidents, one of the most difficult aspects of operating distributed systems today. As environments grow more dynamic and complex, resolving issues becomes more challenging. Failures now span more services, involve noisier signals, and encompass larger volumes of telemetry data, making it hard for on-call engineers to find root causes quickly. Today, Bits AI SRE is already helping teams decrease time to resolution by up to 95%.

AWS storage is often a top choice for enterprises due to its reliability and power to store large amounts of data for easy access. However, businesses may find it difficult to navigate and understand S3 storage costs, having to manage different storage classes, data transfer fees, and potential hidden charges. Without fully understanding AWS S3 storage costs, the pricing structure can become overwhelming and cost companies more than initially intended.

Monitoring performance of Heroku applications helps improve user experience. This blog post covers Heroku monitoring add-ons and explores why Hosted Graphite is the best choice in 2026. We'll discuss the benefits and setup process of the Hosted Graphite add-on. We'll also discuss future trends in Heroku monitoring.

Modern software systems are more distributed, complex, and business-critical than they've ever been. A single misconfigured service can take down an entire platform. Teams are aiming for production readiness, which is the state where your services are secure, reliable, observable, and owned. Production Readiness Reviews (PRRs) are one of the key mechanisms to get there.