Operations | Monitoring | ITSM | DevOps | Cloud

If your SaaS organization is experimenting with OpenAI, your cloud bill just got a new line item. And unless you know exactly what drives it, that line item can go from manageable to margin-killer, fast. It’s also worth clarifying that OpenAI is not the same as ChatGPT. ChatGPT is the familiar end-user app with a flat monthly subscription. OpenAI, meanwhile, is the platform behind it — a mix of models, features, and usage-based pricing that shifts depending on what you build.

The most challenging question in AI today isn’t how to build with it. It’s whether you can prove it’s worth what you’re spending on it. Every week, I hear the same thing from engineering and finance leaders: “We know the AI bills are big.

APM tracing records the complete execution path of a request as it travels through your system, including database queries, external API calls, cache lookups, message queue events, and inter-service requests. Each step is captured with precise start and end timestamps, duration, and context such as service name, operation name, and relevant attributes. This lets you pinpoint where latency or errors originate without piecing together metrics and logs manually.

There are a lot of ways to gauge the performance of your DevOps teams and the health of your software, but DORA metrics have emerged as the industry standard. If you aren’t familiar with DORA metrics, take a few minutes to read this comprehensive guide to understanding DORA metrics. DORA metrics were designed to offer a high-level, long-term view of how your teams are performing.

The past few years have seen the incredible rise of cloud-native AI start-ups, many of them born during the pandemic. These companies emerged agile, experimental, and ready to scale. But as their ambitions grow and their AI models become more complex, they face a critical crossroads: how to manage infrastructure sustainably while continuing to innovate at speed. In the early days, public cloud services were the obvious choice.

Agentic AI has sparked a ton of excitement in IT. These intelligent agents can analyze signals, interpret requests, and recommend actions with surprising accuracy. But left on their own, they struggle to translate those insights into reliable execution. The end result is a fragmented picture of great thinking... but limited doing. This is why orchestration matters.

Since launching our AI investigations and insights in a research preview, one thing has become clear: automated root cause analysis delivers a significant return on investment. Teams have confirmed that instant insights don’t just save a few minutes; they fundamentally shorten incident response cycles, free up valuable engineering hours, and reduce the business impact of downtime.

Redgate Monitor now supports Oracle Data Guard environments, giving DBAs instant visibility into replication health, lag and role transitions, so Standbys stay in sync and are ready to protect availability and data when needed. DBAs running Oracle Data Guard know that keeping replicas healthy requires constant vigilance. It often involves querying dynamic performance views, such as V$DATAGUARD_STATS, or running Data Guard Broker commands to check lag and role status.

Vector databases allow you to store, manage, and efficiently query high-dimensional vector data, which are numerical representations of data like text, images, or audio. Pinecone is a fully managed vector database optimized for fast, scalable similarity search—to power a Retrieval-Augmented Generation (RAG) system. This allows you to enhance language model responses by grounding them in relevant context retrieved from your own documents.