Garbage in, garbage out. This old adage has never been more relevant. As businesses rack to unlock value from artificial intelligence (AI), the spotlight is shifting to the one system AI models can’t live without: the database.
Today’s AI models rely on massive volumes of structured and unstructured data. It’s no surprise then that, with AI using and creating more data than ever before, the attention of IT leaders and their teams is increasingly fixed on the growing number of databases where all that data is being stored. Poor performance, data gaps, and quality issues can silently seep into training pipelines, leading to unreliable, biased, or outdated outcomes. With AI both generating and consuming more data than ever, database performance isn’t just an infrastructure concern; it’s a strategic imperative.
But simply, monitoring database performance is no longer enough. Visibility into query performance, schema changes, infrastructure metrics and data health empowers teams to spot bottlenecks before they cascade into outages, model failures or operational delays. IT teams must acquire a comprehensive, real-time view of their databases, right down to the smallest health or performance metric. It’s not just about uptime; it’s also about insight.
At SolarWinds, we’ve seen firsthand how the right observability strategy empowers businesses to make their data environments AI-ready. The most resilient and future-proof systems are typically built on four key pillars: monitoring, diagnosing, optimising, and observing everywhere.
Monitoring as the foundation
What or where you choose to monitor can make or break your database observability strategy. For most businesses, the right metrics—like query execution times, CPU usage, memory consumption, and storage I/O—are needed to obtain a real-time view of database health. Any fluctuation in these can disrupt data pipelines and compromise model performance.
For example, high I/O utilisation may indicate a performance bottleneck caused by heavy data transactions, which will lead to slower query execution, increased memory usage, and inevitable downtime. With real-time insight, that bottleneck becomes a blip, diagnosed and resolved before it impacts model input.
Focusing on a select set of metrics relevant to the business allows IT teams to gain meaningful insights without being overwhelmed by excessive alerts, logs, or dashboards. Think about the database metrics that matter to employees and customers. Focusing on these helps ensure you’ll have the necessary information to diagnose systems in less time, with fewer resources, but for greater impact.
Diagnosing with confidence and speed
Troubleshooting in AI-driven environments can be daunting, especially when models fail silently. With real-time visibility into key database metrics, IT teams can diagnose issues with precision and efficiency. Without structured observability, troubleshooting often devolves into guesswork as multiple errors compete for attention. Advanced diagnostic tools, coupled with real-time monitoring, can streamline diagnostic processes by grouping alerts, prioritising errors, and filtering out irrelevant data. This reduces noise and alert fatigue while allowing IT teams to quickly zero in on the root causes of an issue and limit disruption to training or production.
Some leading solutions go further, using AI to investigate query performance and detect emerging anomalies. This proactive approach not only resolves immediate performance problems but also uncovers adjacent inefficiencies or bottlenecks before they escalate.
Optimising for continuous excellence
Continuous optimisation is critical to help ensure databases remain resilient to ever-growing and ever-changing workloads. They evolve—new data is ingested, new features are engineered, and new models are deployed. Observability-driven insights help IT teams gain clarity into what’s working and where improvements are needed, allowing them to focus optimisation efforts where they will yield the greatest impact on database stability and performance.
This goes beyond performance tuning. In cloud-native environments, optimisation also translates to cost control. Right-sizing compute and memory for AI data flows helps avoid overprovisioning, ensuring performance without overspending.
By conducting regular reviews of key metrics like query or indexing times, developers can make necessary optimisation tweaks, and assess for improvement in real time. Ultimately, this creates more resilient data layers that evolve with their AI strategies.
Observe everywhere, all at once
Database observability strategies must also consider how databases today are often spread across multiple on-premises, cloud, and hybrid environments. After all, modern AI systems don’t rely on a single database. Each environment comes with its own requirements and challenges, but to help ensure they maintain continuous visibility into their databases, IT teams should consider a unified observability solution.
The ideal unified observability should come with pre-built monitoring, analysis, diagnostic, and optimization tools that readily integrate across a range of database environments. This allows for seamless cross-environment correlation and analysis of performance metrics, enabling teams to spot inefficiencies, bottlenecks, or resource issues as data traverses multiple environments and systems.
The pillars to greater database performance
Monitoring, diagnosing, optimising, and observing everywhere.
These are the four key pillars to effective database observability, and they are critical for any organisation aiming to thrive in an AI-first world. Neglecting one or two of these pillars may result in database operations that aren’t nearly as robust, scalable, and adaptable to ever-increasing demands from the business and customers.
In the age of AI, your database is more than just a backend system—it’s the launchpad for innovation. The only way to keep it strong is to observe it deeply and consistently.
Sascha Giese started his IT career in the early 2000s as a network and system administrator in a public school, where he learned that teaching teachers how to use this thing called the internet is more complicated than resolving spanning tree problems.
Joining SolarWinds in 2014 as a Solution Engineer, Sascha Giese quickly became a subject matter expert on all products in the SolarWinds portfolio. He contributed significantly to the SolarWinds Certified Professional® (SCP) exams and training curriculum. As a Tech Evangelist, his mission is clear: to bring the often-overlooked human element of IT teams into focus.
As Sascha Giese eloquently puts it, “IT no longer supports the business; IT runs it instead.” His unwavering belief in the transformative power of IT serves as a powerful reminder of the potential and impact of our work in this field.
He studied Media Informatics at the University of Lübeck, Germany, and holds industry certifications from various vendors, such as Cisco, Microsoft, VMware, and Amazon.
- About Sascha Giese
- About SolarWinds
Sascha Giese started his IT career in the early 2000s as a network and system administrator in a public school, where he learned that teaching teachers how to use this thing called the internet is more complicated than resolving spanning tree problems.
Joining SolarWinds in 2014 as a Solution Engineer, Sascha Giese quickly became a subject matter expert on all products in the SolarWinds portfolio. He contributed significantly to the SolarWinds Certified Professional® (SCP) exams and training curriculum. As a Tech Evangelist, his mission is clear: to bring the often-overlooked human element of IT teams into focus.
As Sascha Giese eloquently puts it, “IT no longer supports the business; IT runs it instead.” His unwavering belief in the transformative power of IT serves as a powerful reminder of the potential and impact of our work in this field.
He studied Media Informatics at the University of Lübeck, Germany, and holds industry certifications from various vendors, such as Cisco, Microsoft, VMware, and Amazon.
IT management, Application & Server Management, Network Management, Virtualization Management, Log & Security Information Management, Storage Management, IT Alert & On-call Management, IT Help Desk, File Transfer, and Database Performance
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