It's time to take the next step in cloud management—AI-driven autonomous systems are solving the complexity challenge in modern cloud environments.

The Challenge with Traditional Cloud Management Approaches

Traditional manual and rule-based automated systems struggle to keep up with the complexity of managing cloud environments leads to persistent issues with cost control and performance optimization.

Symptoms and Root Causes of Inefficiency

These traditional approaches often result in:

• Persistent cost issues due to inefficient resource allocation; estimated cloud wastage across multiple surveys is virtually unchanged in the last 5 years
• Missed opportunities to improve customer experience
• Long lists of unimplemented recommendations

The Challenges of Manual Cloud Optimization

• Time-Consuming Changes: The time needed to make effective, safe optimization changes makes it uneconomic. Modern microservices are small, and new configurations may have short lifetimes due to new releases or shifts in traffic patterns.
• Outdated Model: The increased pace of development and scalable nature of the public cloud means this approach is no longer a fit for today's dynamic cloud environments.

The Challenges of Using Rule-Based Automation for Cloud Optimization

• Lack of Learning: Rule-based systems do not learn over time, so rules don’t reflect the actual behavior of your applications.
• Inability to Handle Complexity: Rule-based automation cannot support complex situations. It struggles with balancing cost, performance, and availability goals (which all matter) while working with a large set of lagging and leading metrics.

The AI-Driven Solution

AI-driven autonomous cloud management offers a transformative approach to these challenges:

• Adapt on the Fly: AI systems offer real-time insights and adapt to changes faster than any human or basic automated system. Autonomous systems can monitor and adjust resources in real-time for optimal performance and cost-efficiency.
• Reinforcement Learning: Systems can now evolve as new data is generated, improving performance/cost and reducing downtime. The underlying methods include ranking metrics, clustering data, and classifying lead/lag indicators while closely monitoring the entire system.
• Complex Data Handling: AI can manage complex datasets and balance multiple objectives seamlessly, something traditional automation struggles with.

Why Autonomous Cloud Management is Production-Ready Now

• Proven Reliability and Scalability: Extensive testing and real-world deployments have demonstrated the robustness of AI-driven systems. Paypal runs 2M remediations a year with an autonomous system, and here at Sedai we have customers with thousands of services and millions in spend being run autonomously.
• Real-World Use Cases: Enterprises across various industries like Palo Alto Networks, Experian, KnowBe4 & Belcorp have implemented AI-driven cloud management, witnessing significant improvements in cost, time savings, and operational resilience.
• Safety: AI creates unique, safe execution paths that go beyond the capability of automated systems while retaining all the important elements (distributed locking, ticketing & approvals, rollback plans, and use of maintenance windows for changes for select services).

Taking the Next Step with AI & Autonomy

Autonomous cloud management is no longer a futuristic concept but a present-day reality. By adopting AI-driven solutions, cloud users can overcome the limitations of traditional manual & automated approaches, meet their optimization goals, and gain the freedom and flexibility to work on interesting high value projects.

If this sounds interesting, please feel free to start a free trial or see a live demo.