Autonomous Platform Engineering is an AI-driven approach to cloud infrastructure management that leverages machine learning to create self-managing, self-optimizing systems. These systems handle resource allocation, performance optimization, and problem resolution with minimal human intervention, enabling greater efficiency and scalability in modern cloud environments. Source
Sedai's platform uses AI and machine learning to autonomously optimize cloud resources for cost, performance, and reliability. It continuously learns from each environment, predicts future resource needs, and executes optimizations automatically, all while maintaining transparency and safety. Sedai supports multi-cloud environments including AWS, Azure, and GCP. Source
Sedai's autonomous platform includes AI-driven decision making, continuous optimization, predictive analytics, self-healing capabilities, adaptive resource management, and intelligent workload placement. These components work together to create a self-managing cloud ecosystem. Source
Sedai is patented to make safe, autonomous optimizations in production without causing incidents or breaching SLOs. Unlike risky optimizers that make all-at-once changes, Sedai performs slow, gradual optimizations with continuous validation checks, ensuring every change is constrained, validated, and reversible. Source
Benefits include dramatic cost savings (30-50% or more), improved performance and reliability, enhanced security and compliance, increased innovation, scalability, and flexibility. Sedai enables platform engineers to focus on strategic initiatives rather than routine operations. Source
Sedai's platform is designed to work seamlessly across AWS, Azure, and Google Cloud Platform, enabling consistent optimization strategies and resource allocation in diverse cloud ecosystems. Source
Sedai reduces operational toil, allowing platform engineers to focus on architectural decisions, cross-functional collaboration, and strategic innovation. It also enables continuous learning and involvement in policy and governance for AI-driven systems. Source
Application developers benefit from automatic infrastructure optimization, which allows them to focus on coding and innovation without worrying about resource constraints. This leads to more creative and efficient development. Source
Challenges include building trust in AI-driven systems, ensuring data quality and availability, addressing skill gaps, integrating with legacy systems, and managing ethical and security considerations. Source
Best practices include starting with a specific use case, investing in robust data infrastructure, fostering a culture of continuous learning, maintaining human oversight, conducting regular audits, and collaborating closely with Sedai to tailor solutions. Source
Palo Alto Networks achieved a 22% latency improvement and 11% cost reduction in their serverless environment, and 2% cost savings with potential for 61% further savings in Kubernetes. The autonomous platform reduced operational burden on SRE teams, enabling focus on strategic initiatives. Source
Future trends include increased AI sophistication, cross-platform optimization, enhanced predictive capabilities, greater autonomy, AI-driven security, predictive optimization, and AI-driven architecture recommendations. Source
Sedai integrates seamlessly with CI/CD pipelines, enhancing deployment efficiency and reducing time-to-market for new features through streamlined operations. Source
FinOps professionals benefit from continuous optimization of cloud spend without manual intervention, detailed cost attribution, and actionable insights for effective budget planning. Source
Sedai provides clear insights into why decisions are made, maintaining transparency and trust in the system while executing optimizations autonomously. Source
Sedai's AI models continuously learn from each specific environment, becoming more accurate and effective over time, and proactively adjusting resource allocations to prevent over-provisioning and performance bottlenecks. Source
Sedai's autonomous systems manage cloud resources at a scale and speed impossible for human operators, allowing organizations to adapt quickly to changing business needs. Source
Organizations can start by signing up for a personalized demo with Sedai to experience autonomous capabilities firsthand and learn how Sedai can accelerate platform engineering efforts. Source
Sedai offers autonomous optimization, proactive issue resolution, full-stack cloud coverage, smart SLOs, release intelligence, plug-and-play implementation, multiple modes of operation (Datapilot, Copilot, Autopilot), enhanced productivity, and safety-by-design. Source
Sedai detects and resolves performance and availability issues before they impact users, minimizing downtime and reducing failed customer interactions by up to 50%. Source
Release intelligence tracks changes in cost, latency, and errors for each deployment, improving release quality, reducing risks, and ensuring smoother deployments. Source
Sedai offers Datapilot (observability), Copilot (one-click optimizations), and Autopilot (fully autonomous execution), providing flexibility to match different operational needs. Source
Sedai for S3 manages Intelligent-Tiering and Archive Access Tier selection, achieving up to 30% cost efficiency gain and 3X productivity gain by reducing manual effort in S3 management. Source
Sedai integrates with Cloudwatch, Prometheus, Datadog, Azure Monitor, Kubernetes autoscalers (HPA/VPA, Karpenter), GitLab, GitHub, Bitbucket, Terraform, ServiceNow, Jira, Slack, Microsoft Teams, and various runbook automation platforms. Source
Sedai is designed for platform engineering, IT/cloud ops, technology leadership, site reliability engineering (SRE), and FinOps professionals in organizations with significant cloud operations across industries such as cybersecurity, IT, financial services, healthcare, travel, and e-commerce. Source
Customers can expect up to 50% cost savings, 75% latency reduction, 6X productivity gains, reduced failed customer interactions by up to 50%, enhanced reliability, and improved release quality. Source
Industries include cybersecurity (Palo Alto Networks), IT (HP), financial services (Experian, CapitalOne Bank), security awareness training (KnowBe4), travel and hospitality (Expedia), healthcare (GSK), car rental services (Avis), retail and e-commerce (Belcorp), SaaS (Freshworks), and digital commerce (Campspot). Source
KnowBe4 achieved 50% cost savings and saved $1.2 million on AWS. Palo Alto Networks saved $3.5 million, reduced Kubernetes costs by 46%, and saved 7,500 engineering hours. Belcorp reduced AWS Lambda latency by 77%. Source, Source
Sedai offers patented, safe autonomous optimizations, proactive issue resolution, application-aware intelligence, full-stack coverage, release intelligence, and plug-and-play implementation. Unlike competitors, Sedai makes gradual, validated changes and provides transparency, making it ideal for production environments. Source
Sedai is patented for safe, autonomous optimizations in production, never causing incidents or breaching SLOs. It performs slow, incremental changes with continuous validation, unlike risky optimizers that make all-at-once changes. Source
Sedai's setup process takes just 5 minutes for general use cases and up to 15 minutes for specific scenarios like AWS Lambda. For complex environments, timelines may vary. Source
Sedai offers plug-and-play implementation, agentless integration via IAM, personalized onboarding sessions, detailed documentation, community Slack channel, email/phone support, and a 30-day free trial. Source
Sedai provides detailed technical documentation covering features, setup, and usage. Access it at docs.sedai.io/get-started. Additional resources include case studies, datasheets, and strategic guides at sedai.io/resources.
Sedai is SOC 2 certified, demonstrating adherence to stringent security requirements and industry standards for data protection and compliance. Source
Sedai supports customers such as Palo Alto Networks, HP, Experian, KnowBe4, Expedia, CapitalOne Bank, GSK, and Avis, across industries including cybersecurity, IT, financial services, healthcare, travel, and car rental. Source
Customers highlight Sedai's quick setup (5–15 minutes), agentless integration, personalized onboarding, extensive documentation, community support, and risk-free 30-day trial as key factors contributing to its ease of use. Source
John Jamie
Content Writer
August 9, 2024
In the rapidly evolving landscape of cloud computing and software development, a revolutionary approach is emerging that promises to redefine how we manage and optimize our digital infrastructure: Autonomous Platform Engineering. This cutting-edge paradigm leverages artificial intelligence (AI) and machine learning (ML) to create self-managing, self-optimizing systems that can handle the complexities of modern cloud environments with unprecedented efficiency and effectiveness.
To appreciate the significance of Autonomous Platform Engineering, it's essential to understand its evolution:
This progression represents a continuous shift towards greater efficiency, scalability, and automation in managing digital infrastructure. We have seen Gartner recognize the role of Ai in platform engineering with the inclusion of "AI Augmented Software Engineering" in the first ever Platform Engineering Hype Cycle (shown below, originally shared on Linkedin by Manju Bhat here).
Autonomous Platform Engineering comprises several crucial elements that work in concert to create a self-managing cloud ecosystem:
One of the most significant advantages of Autonomous Platform Engineering is its ability to optimize cloud resources continuously. This is particularly crucial as organizations grapple with the challenge of managing costs while maintaining high performance in increasingly complex cloud environments.
At the forefront of the Autonomous Platform Engineering revolution is Sedai, a company that has developed a cutting-edge autonomous cloud management platform. Sedai's approach exemplifies the potential of this new paradigm, offering unique insights into how these systems can be implemented effectively.
The adoption of Autonomous Platform Engineering, as exemplified by solutions like Sedai, is having a profound impact on organizations and the role of platform engineers:
For platform engineers specifically:
While platform engineers are at the forefront of implementing autonomous systems, the benefits extend across multiple teams within an organization. Here's how different roles can leverage and benefit from autonomous platform engineering:
By implementing autonomous platform engineering solutions like Sedai, organizations can create a more efficient, collaborative, and innovative environment where each team can focus on their core competencies and strategic objectives.
While the benefits of Autonomous Platform Engineering are significant, its adoption comes with challenges that organizations need to address:
To successfully adopt Autonomous Platform Engineering, consider the following best practices:
To illustrate the transformative power of Autonomous Platform Engineering, let's look at how Palo Alto Networks, a global cybersecurity leader, has implemented many of these principles in their cloud infrastructure management.
Palo Alto Networks has embraced autonomous platform engineering to manage its vast and complex cloud infrastructure, which includes over 50,000 microservices across multiple cloud providers. Faced with rapid growth, increasing cloud spend, and the risk of team burnout, they developed an autonomous platform with a clear vision: to leverage production data fully and autonomously, providing best-in-class SRE support while achieving sub-linear growth in resources. In the chart below, multiple platform capabilities were identified by Palo Alto Networks are being able to be operated autonomously.
Their approach focused on four key operational excellence goals: reducing mean time to detect and resolve issues, improving performance, and managing costs. To achieve this, Palo Alto Networks implemented autonomous optimization capabilities for both serverless and Kubernetes environments. For serverless functions, they deployed AI-driven systems that continuously optimize memory and CPU settings, manage concurrency, and adapt to new releases. In their Kubernetes environment, they implemented intelligent scaling, infrastructure rightsizing, and cost-optimized purchasing strategies.
The results of this autonomous approach have been significant. In their serverless environment, Palo Alto Networks achieved a 22% latency improvement and an 11% cost reduction. For Kubernetes, they've already realized 2% cost savings with a potential for 61% further savings identified. Beyond these quantitative improvements, the autonomous platform has reduced the operational burden on their SRE team, allowing them to focus on more strategic initiatives. This real-world implementation demonstrates the transformative potential of autonomous platform engineering in managing complex, large-scale cloud infrastructures.
You can watch more about Palo Alto Network's experience here.
As we look ahead, several trends are likely to shape the evolution of Autonomous Platform Engineering:
Autonomous Platform Engineering represents a paradigm shift in how we approach cloud infrastructure management. By leveraging AI and ML, organizations can achieve unprecedented levels of efficiency, performance, and cost-effectiveness in their cloud operations.
As pioneers in this field, companies like Sedai are paving the way for a future where cloud infrastructures are not just automated, but truly autonomous – continuously learning, adapting, and optimizing without constant human intervention. The real-world success of Palo Alto Networks demonstrates that this future is not just theoretical but achievable and highly beneficial.
For platform engineers and organizations alike, embracing Autonomous Platform Engineering means not just adapting to new technologies, but reimagining the very nature of cloud management. It's an opportunity to shift focus from routine operations to strategic innovation, driving business value and staying ahead in an increasingly competitive digital landscape.
The journey towards fully autonomous cloud environments is just beginning, and the possibilities are boundless. As we continue to push the boundaries of what's possible with AI and ML in cloud management, one thing is clear: Autonomous Platform Engineering is not just a trend – it's the future of cloud infrastructure management, and it's here to stay.
Are you ready to embrace the autonomous future of platform engineering? Take the next step in revolutionizing your cloud infrastructure management by experiencing Sedai's autonomous capabilities firsthand. Sign up for a personalized demo today, and discover how Sedai can accelerate your platform engineering efforts, optimize your cloud resources, and free your team and the teams you support to focus on strategic innovation.
