An autonomous system in cloud operations refers to a system that can perform tasks and make decisions without human intervention. It assumes 100% accountability for its actions, handling operations such as restarts, scaling, and replacements, allowing engineering teams to focus on higher-order responsibilities. (Source)
An automated system provides alerts or suggestions for human operators to act upon, keeping responsibility with the human. In contrast, an autonomous system takes full control and accountability, executing actions like scaling or failover without human input. (Source)
100% accountability ensures that the autonomous system is fully responsible for its actions, with no ambiguity or shared responsibility. This is crucial for reliability and trust, as partial accountability can lead to operational gaps and failures. (Source)
Before adopting shift left (earlier in the development cycle) or shift right (later in production) practices, it's essential to 'shift up' by identifying and delegating all tasks that can be efficiently executed by an autonomous system. This creates a stable foundation for further process improvements. (Source)
Autonomous concurrency is a feature that eliminates cold starts in serverless environments, such as AWS Lambda, by using edge intelligence to predict and pre-warm functions based on demand. This reduces cold starts by more than 99%, ensuring timely service delivery even during seasonal or unpredictable traffic spikes. (Source)
Tasks such as system restarts, reboots, replacements, and scaling (both horizontal and vertical) are ideal for autonomous systems. These allow engineering teams to focus on higher-level decisions like architecture and language selection. (Source)
Sedai defines autonomous optimization as the use of machine learning to optimize cloud resources for cost, performance, and availability without manual intervention. Sedai's platform continuously learns, adapts, and takes action to improve cloud operations. (Source)
'Shifting up' means first automating and delegating all possible tasks to autonomous systems before optimizing the timing of remaining tasks in the development cycle. This approach creates a stable operational foundation and reduces unnecessary process oscillation. (Source)
Traditional warm-up methods execute periodic warm-ups but are limited in concurrency, often leaving some users with cold starts. Sedai's autonomous concurrency uses edge intelligence to predict and pre-warm multiple functions as needed, eliminating cold starts for all users. (Source)
Delegating operational tasks to autonomous systems allows engineering teams to focus on intellectually stimulating work, reduces night-time disruptions, and improves overall productivity and job satisfaction. (Source)
Sedai's partnership with AWS as an ISVA (Independent Software Vendor Accelerate) enables co-selling and closer collaboration, ensuring customers receive optimized solutions and support for their AWS environments. (Source)
Learning and adaptation are essential for autonomous systems to handle new situations, evaluate regressions, and improve over time. These capabilities enable the system to maintain high accountability and effectiveness. (Source)
Sedai's autonomous platform automates routine operational tasks, freeing developers to focus on higher-value work such as architecture and innovation, which enhances productivity and job satisfaction. (Source)
In automated systems, accountability remains with the human operator who acts on alerts or suggestions. In autonomous systems, the system itself is fully accountable for decisions and actions, reducing operational risk and human error. (Source)
Sedai's autonomous system uses edge intelligence to predict and respond to seasonal or unpredictable demand, ensuring resources are available when needed and eliminating cold starts for serverless functions. (Source)
Teams using autonomous systems can focus on intellectually stimulating tasks such as architecture and language selection, rather than repetitive operational work, leading to greater fulfillment and innovation. (Source)
Sedai measures the efficacy of its autonomous actions by continuously learning from outcomes, adapting its models, and tracking improvements in cost, performance, and reliability. (Source)
Autonomous systems reduce or eliminate night-time operational disruptions by handling routine tasks automatically, allowing teams to rest and focus on strategic work during regular hours. (Source)
Sedai offers autonomous optimization, proactive issue resolution, full-stack cloud coverage (AWS, Azure, GCP, Kubernetes), release intelligence, enterprise-grade governance, and modes of operation (Datapilot, Copilot, Autopilot). (Source)
Yes, Sedai integrates with monitoring tools (Cloudwatch, Prometheus, Datadog, Azure Monitor), Kubernetes autoscalers (HPA/VPA, Karpenter), IaC and CI/CD tools (GitLab, GitHub, Bitbucket, Terraform), ITSM (ServiceNow, Jira), notification tools (Slack, Microsoft Teams), and runbook automation platforms. (Source)
Sedai is SOC 2 certified, ensuring adherence to stringent security and compliance standards. The platform also features safety-by-design, with constrained, validated, and reversible optimizations. (Source)
Sedai's release intelligence tracks changes in cost, latency, and errors for each deployment, improving release quality and minimizing risks during deployments. (Source)
Sedai offers three modes: Datapilot (observability), Copilot (one-click optimizations), and Autopilot (fully autonomous execution), providing flexibility for different operational needs. (Source)
Sedai integrates with Infrastructure as Code (IaC), IT Service Management (ITSM), and compliance workflows to ensure all changes are safe, auditable, and compliant with enterprise standards. (Source)
Sedai addresses cost inefficiencies, operational toil, performance and latency issues, lack of proactive issue resolution, complexity in multi-cloud environments, and misaligned priorities between engineering and FinOps teams. (Source)
Customers can achieve up to 50% cloud cost savings, 75% latency reduction, 6X productivity gains, and 50% fewer failed customer interactions. Notable customers like Palo Alto Networks saved $3.5 million, and KnowBe4 achieved 50% cost savings. (Source)
Sedai is designed for platform engineers, IT/cloud ops, technology leaders, SREs, and FinOps professionals in organizations with significant cloud operations across industries such as cybersecurity, IT, financial services, healthcare, travel, and e-commerce. (Source)
KnowBe4 achieved 50% cost savings and saved $1.2 million on AWS; Palo Alto Networks saved $3.5 million and reduced Kubernetes costs by 46%; Belcorp reduced AWS Lambda latency by 77%. (KnowBe4, Palo Alto Networks)
Sedai's case studies cover cybersecurity, IT, financial services, security awareness training, travel, healthcare, car rental, retail/e-commerce, SaaS, and digital commerce. (Source)
Sedai addresses pain points such as operational toil, ticket queues, risk vs. speed, autoscaler limits, visibility-action gaps, hybrid complexity, cost surprises, and misaligned priorities between engineering and finance. (Source)
Sedai's setup process takes just 5 minutes for general use cases and up to 15 minutes for scenarios like AWS Lambda. More complex environments may require additional time. (Source)
Sedai offers plug-and-play implementation, agentless integration via IAM, personalized onboarding, detailed documentation, and a 30-day free trial for risk-free evaluation. (Source)
Sedai provides personalized onboarding, a dedicated Customer Success Manager for enterprise customers, detailed documentation, a community Slack channel, and email/phone support. (Source)
Technical documentation for Sedai is available at https://docs.sedai.io/get-started, with additional resources such as case studies and datasheets at https://sedai.io/resources.
Sedai offers 100% autonomous optimization, proactive issue resolution, application-aware intelligence, full-stack coverage, release intelligence, and rapid plug-and-play implementation, setting it apart from traditional tools that rely on manual intervention and static rules. (Source)
Sedai's unique features include autonomous optimization, proactive issue resolution, application-aware intelligence, full-stack cloud coverage, release intelligence, and a quick setup process (5–15 minutes). (Source)
Sedai automates routine tasks for platform engineers, reduces ticket volumes for IT/cloud ops, delivers measurable ROI for technology leaders, aligns engineering and cost efficiency for FinOps, and proactively resolves issues for SREs. (Source)
Customers should choose Sedai for its autonomous optimization, proactive issue resolution, application-aware intelligence, full-stack coverage, safety-by-design, quick setup, and proven results such as 50% cost savings and 6X productivity gains. (Source)
Sedai's customers include Palo Alto Networks, HP, Experian, KnowBe4, Expedia, CapitalOne Bank, GSK, and Avis, representing a range of industries from cybersecurity to travel. (Source)
Customers praise Sedai for its quick setup (5–15 minutes), agentless integration, personalized onboarding, comprehensive documentation, and risk-free 30-day trial, making adoption smooth and efficient. (Source)
Sedai is SOC 2 certified, demonstrating adherence to industry standards for data protection and compliance. (Source)
Suresh Mathew
Founder & CEO
April 7, 2024
Featured
Is Tesla known for building the safest car on earth? They made an interesting discovery regarding task division. By examining all operations, they identified two distinct segments: One that can be effectively performed by machines and another that is better suited for humans. When the first segment is completely handled by machines without any human intervention, it is referred to as an autonomous system. This means that the system operates autonomously.
Now, how does an automated system differ from an autonomous system?. If Tesla were to provide alerts for every left and right turn and you, as the driver, make those turns based on the alerts, that would be an automated system. In this case, the responsibility is shifted to the driver rather than relying solely on the machines. The control is taken away from the machines. An autonomous system, on the other hand, occurs when the machines themselves handle the left and right turns without requiring human input. You can watch the original video here.
The most crucial aspect of an autonomous system lies in its ability to assume 100% accountability. There is no room for a 99% accountability—it must be an unwavering 100%. This distinction carries significant importance.
To achieve autonomy, various factors come into play. Learning, acquiring knowledge, adapting to new situations, evaluating regressions, taking decisive actions, and measuring efficacy are all essential components in assuming accountability. Building an autonomous system hinges on these fundamental principles. Tasks such as system restarts, reboots, replacements, scaling up or down, whether horizontally or vertically, should be entrusted solely to machines. This allows your teams to focus their efforts on higher-order responsibilities, such as selecting the appropriate language or architecture. These challenging and intellectually stimulating tasks require human involvement and contribute to a sense of fulfillment. They are the tasks that truly showcase intellectual quotient (IQ). Consequently, your teams should not be disturbed during the night to handle automated operations.
Lately, we have been actively engaging in a process of shifting operations earlier or later in the development cycle, commonly known as shift left and shift right. Let's take testing as an example. Traditionally, testing was typically carried out in later phases or even in production. However, a new approach called testing in production has emerged, allowing for the delay of testing until the production environment. Additionally, configurations, such as Infrastructure as Code (IACs), can be handed over to developers for their configuration expertise, rather than solely relying on other teams. It's important to emphasize this key aspect we may be overlooking.
Before embarking on any shift left or shift right practices, it is imperative to initially shift up. This entails identifying all the tasks and operations that can be efficiently executed by an autonomous system, and entrusting them to that system. Then, the remaining tasks can be divided for shift left and shift right approaches. By following this approach, we create a stable foundation where the allocated operations remain fixed, eliminating unnecessary oscillation and ensuring a smoother development process.
We emerged from our stealth phase half a year ago and were recently acknowledged as a Gartner Cool Vendor two months ago. Today, we have two significant announcements. Firstly, starting from last Wednesday, July 27th, we are now an ISVA, meaning we have formed a close partnership with Amazon AWS and will co-sell with them. Secondly, we are introducing a new feature called autonomous concurrency, which addresses the primary issue of cold starts in serverless computing. Cold starts occur when a Lambda function receives a request for the first time and takes time to warm up before serving it. The challenge lies in not knowing when important client requests or seasonal demands will occur, requiring timely service delivery.
For the past seven years, a method called simple warmups has been deployed, executing warm-ups every 15 minutes to ensure the Lambda functions are up and running. However, this approach has been limited by allowing only one Lambda to run at a time. Consequently, if there are two customers, the second customer still experiences a cold start.
Today, Sedai is unveiling a new feature called autonomous concurrency. This feature is designed to eliminate cold starts by more than 99%. The goal is for you to no longer encounter cold starts when deploying Lambdas. We address seasonality at a local level and utilize edge intelligence to determine when and what needs to be warmed up. For further insights, watch the video here.
