Managing SaaS workloads with AI

With currently over 30,000 SaaS solutions¹ worldwide and growing, it’s often hard for users to select the right platform. One thing that can help make that decision is to choose SaaS tools that have excellent cloud performance and offer an unmatched level of service, which only AI can guarantee at scale.

While many – if not most – tech companies are already using AI or claim to be “AI-powered,” choosing a SaaS platform doesn’t get any easier for the customer. However, AI can bring tangible benefits to SaaS platform users, providing exceptional customer value and experience, often in ways that are not apparent at first glance.

One of those areas is using AI behind the scenes of a SaaS platform – for example, in workload management and IT asset management. 

The complexity of managing cloud workflows expands as companies scale and the demand becomes increasingly dynamic. As this happens, allocating resources and monitoring performance manually around the clock by teams of people simply becomes unsustainable. This can negatively impact the cost and the efficiency translated into the eventual customer experience. Luckily, AI can help improve both outcomes. 

AI can analyse enormous amounts of data, automate complex processes and predict trends. When it comes to workload management and optimisation, this becomes indispensable in making applications run efficiently, even as you scale. It also leads to improved user experience, reduced operational costs, and enhanced overall performance. Here are a few ways this can happen:

AI-driven workload optimisation involves assessing the specific needs of various applications and services such as; computing power, memory, storage, network bandwidth, etc. 

To improve both performance and cost-effectiveness, AI can analyse the intricate details of workloads and historical data, and provide actionable insights on the improvements you need to make to your cloud infrastructure. 

AI can predict future requirements and the most suitable configurations that align with your workload demands, such as:

• cloud instance types 
• geographical regions 
• or even specific data centres.

A common challenge for SaaS companies when it comes to allocating cloud resources for optimal performance is reducing resource waste. 

AI models scan resource consumption in real-time and predict high and low demand times to pinpoint possible savings. This helps reduce resource waste and lower operational costs that – when they go unnoticed – accumulate over time.

Because AI tools can dynamically adjust resources in real time as workloads change, you can make sure that your SaaS application always has the necessary resources to perform optimally. 

AI can also help balance load by distributing traffic intelligently across servers, reducing latency and preventing bottlenecks. All this can result in a smoother user experience, even during peak demand.

Factors to consider in planning your next investment for workload management and cloud performance with the help of AI:

1. Scalability

Look for cloud services that can align with your platform’s future growth plans and the everyday flexibility demands. This way you can plan to prevent significant downtime or performance drops. 

2. Integration and compatibility

Your new AI tools should integrate seamlessly with your existing infrastructure, tools, and services. Consider technologies that perform well across multi-cloud or hybrid-cloud environments. This will allow you considerable flexibility in choosing the best cloud providers and regions for specific workloads.

3. Automation and self-learning capabilities

AI and machine learning should help you see major improvements in performance and cost efficiency and optimise allocating resources automatically, according to demand. In this regard, predictive analytics can also help you anticipate resource needs and avoid potential bottlenecks, adjusting the workloads proactively.

Additionally, look for AI solutions that continuously improve workload optimisation strategies over time. 

4. Cost-efficiency

When selecting your next cloud services for optimal workload management, evaluate the total cost of ownership against expected performance gains. Consider flexible, pay-as-you-go pricing models if you tend to experience large workload fluctuations. 

5. Performance monitoring and analytics

Invest in tools that offer comprehensive real-time monitoring and analytics for cloud performance, as well as insights into end-user experience. This can help optimise workflows and improve customer satisfaction.

6. Security and compliance

Tight cyber security is a non-negotiable aspect of workload management, so make sure the new technology offers robust security features to protect workloads from cyber threats. It should also help ensure compliance with relevant regulations and standards, especially if you manage sensitive data in industries such as finance or healthcare, or operate internationally.

7. Vendor reliability and support

AI can be an eye-catching buzzword. When evaluating vendors, look closely at their track record regarding reliability, a clear roadmap for future improvements, and commitment to innovation. 

Consider the level of support availability and managed services offered, as this is also key understand the support that will be offered to you. 

AI can help users get a much better quality SaaS service. What’s important, however, is to look beyond the AI hype and into tangible benefits that the new technology can bring. A key factor to consider is a smooth, uninterrupted user experience, even when the demand is high. And that’s precisely what AI cloud solutions provided by a reliable vendor can do.

Contact us to learn more.