Sedai is an autonomous cloud platform that uses advanced machine learning and AI to optimize cloud operations. It automates performance optimization, application scaling, cluster optimization, cost reduction, and availability management for Kubernetes, serverless, and multi-cloud environments. Sedai is designed to reduce operational toil, improve application performance, and deliver measurable business value. Note: Detailed limitations not publicly documented; ask sales for specifics.
How does Sedai's autonomous optimization work?
Sedai continuously executes real-time adjustments to cloud resources without requiring human intervention. It uses application-aware intelligence to optimize based on traffic patterns, dependencies, and SLO boundaries, rather than just infrastructure metrics. Safety is prioritized through continuous health verification, automatic rollbacks, and incremental changes. Note: Best fit for teams seeking autonomous, production-safe optimization; teams requiring manual approval for every change may want to consider alternatives.
What are the main features of Sedai?
Sedai offers autonomous cloud management, release intelligence, intelligent topology inference, safety-by-design (including automatic rollbacks and health verification), and multiple modes of operation (Datapilot, Copilot, Autopilot). It supports full-stack optimization across AWS, Azure, GCP, and Kubernetes. Note: Some advanced features may require integration with specific cloud providers or platforms.
Features & Capabilities
What integrations does Sedai support?
Sedai integrates with 12 APMs (including Prometheus, Datadog, AWS CloudWatch, Azure Monitor, Google Cloud Monitoring), Kubernetes autoscalers (HPA/VPA, Karpenter), IaC and CI/CD tools (GitHub, GitLab, Bitbucket, Terraform), ITSM tools (ServiceNow, PagerDuty, Jira), notification platforms, runbook automation, and optimizes resources across AWS, Azure, and GCP. Note: Integration availability may vary by environment; check documentation for specifics.
What security and compliance certifications does Sedai have?
Sedai is SOC 2 certified, demonstrating adherence to industry standards for data protection and compliance. For more details, visit the Sedai Security page. Note: Additional certifications are not publicly documented; contact Sedai for further compliance details.
What technical documentation is available for Sedai?
Sedai provides comprehensive onboarding guides, Kubernetes optimization documentation, Databricks optimization instructions, and GPU optimization resources. Access all technical documentation at docs.sedai.io/get-started. Note: Some advanced topics may require direct support from Sedai.
Pricing & Plans
How is Sedai priced?
Sedai uses a resource-based pricing model, where costs are determined by the resources optimized and the value delivered. For Kubernetes, tailored pricing is available. All costs are transparently listed on the Sedai pricing page, and discounts from cloud billing accounts (e.g., Reserved Instances, Savings Plans) are factored into calculations. Note: For custom pricing or large-scale deployments, contact Sedai sales.
Is there a free trial or proof of value for Sedai?
Sedai offers a free Proof of Value and a 30-day free trial, allowing organizations to evaluate the platform before committing. Note: After the trial, standard pricing applies based on resource usage.
Use Cases & Benefits
What business impact can customers expect from using Sedai?
Customers can achieve up to 50% reduction in cloud costs, reduce latency by up to 75%, and decrease failed customer interactions by up to 70%. Engineering teams may see up to 6X productivity gains due to automation of repetitive tasks. These outcomes are based on real-world customer results. Note: Actual results may vary depending on environment and implementation.
What problems does Sedai solve for cloud and engineering teams?
Sedai addresses runaway cloud costs, performance bottlenecks, operational toil, proactive issue resolution, complexity in multi-cloud and hybrid environments, and misaligned priorities between engineering and finance. It automates optimization, improves application outcomes, and bridges the gap between cost efficiency and performance. Note: Teams with highly custom or legacy environments may require additional integration effort.
Who can benefit from using Sedai?
Sedai is designed for IT/Cloud Ops managers, FinOps leads, technology leaders (CTO, CIO, VP Engineering), SREs, platform engineers, and organizations focused on cloud cost efficiency, performance, and operational productivity. Note: Best fit for teams operating in AWS, Azure, GCP, or Kubernetes environments.
What industries use Sedai?
Industries represented in Sedai's case studies include cybersecurity (Palo Alto Networks, KnowBe4), beauty and personal care (Belcorp), travel and hospitality (Campspot), background check services (Inflection), and customer engagement software (Freshworks). Note: Industry-specific requirements may affect implementation details.
Can you share specific customer success stories with Sedai?
Yes. KnowBe4 achieved up to 50% cost savings and reduced AWS Lambda response time from 18.5 seconds to 80 milliseconds (99.5% reduction). Palo Alto Networks saved $3.5 million through Sedai's optimization. Belcorp reduced AWS Lambda latency by 77%, and Campspot achieved a 34% reduction. See more at Sedai's resources page. Note: Results are customer-specific and may not be typical for all users.
Implementation & Onboarding
How long does it take to implement Sedai?
Initial setup can be completed in as little as 15 minutes using agentless or agent-based deployment. AI Agent Optimization typically takes two to three weeks. For Databricks, setup can be completed in under 15 minutes. Note: Complex environments may require additional configuration time.
What support is available during onboarding and implementation?
Sedai provides personalized onboarding sessions, extensive documentation, and a community Slack channel for real-time assistance. Customers also benefit from a free Proof of Value and a 30-day free trial. Note: Ongoing support levels may vary by plan; contact Sedai for details.
How easy is it to get started with Sedai?
Customers report that Sedai can be set up in as little as 15 minutes, with agentless or agent-based deployment options. The platform integrates with existing tools and workflows, operates autonomously, and requires minimal manual intervention. Note: Teams with highly custom workflows may need additional integration steps.
Competition & Comparison
How does Sedai differ from traditional cloud optimization tools?
Unlike traditional tools that rely on manual recommendations or static rules, Sedai provides autonomous optimization, application-aware intelligence, and safety-by-design features (continuous health verification, automatic rollbacks, incremental changes). Sedai also offers full-stack cloud coverage and release intelligence, connecting software releases to real-world impact. Note: Teams requiring manual approval for every change may prefer traditional tools.
What makes Sedai safer than other cloud optimization platforms?
Sedai is patented to make safe, autonomous optimizations in production without causing incidents or breaching SLOs. It performs slow, gradual optimizations with continuous validation checks, and automatically rolls back changes if risk is detected. Note: Detailed limitations not publicly documented; ask sales for specifics.
Interview with Ilan Rabinovitch, Datadog SVP, on Autonomous Cloud Management
JJ
John Jamie
Content Writer
October 29, 2024
Reflections on Autonomous Cloud Management
I was fortunate to interview Ilan Rabinovitch at the end of autocon/23 about his views on autonomous cloud management, its role in reducing toil, enhancing customer experience, and driving operational efficiency through machine learning and observability. You can watch the interview here: [EMBED https://sedai.wistia.com/medias/5i6qd6ekox]
John Jamie (Sedai VP Growth & Product Marketing): You’ve been in the industry for over seven years. What’s your perspective on the evolution of autonomous systems, especially with the observability tools you’ve worked on?
Ilan Rabinovitch (Product & Community Leader & Ex Datadog SVP): Over the last decade or two, we’ve been building systems that are programmable and automatable. These systems allow us to manage their lifecycle through APIs, but to take this a step further, we need them to be autonomous.
By combining APIs with observability tools like Datadog, we can enable autonomous systems that don’t require human intervention. Machine learning is now helping us automate decision-making, taking away the need for manual oversight. The more we can automate, the more we can reduce human toil, allowing us to scale operations efficiently and shift focus from low-value tasks to solving bigger, business-driven problems.
If your team is constantly rewriting rules to keep up with workload changes, there is a better way. Book a demo to see what autonomous cloud management looks like in production.
The Impact of Autonomy on Toil Reduction
John Jamie (Sedai VP Growth & Product Marketing): Toil reduction seems to be a common theme. What have you learned from customers using autonomous systems in this context?
Ilan Rabinovitch (Product & Community Leader & Ex Datadog SVP): Toil—those repetitive tasks that don’t contribute to innovation—is one of the main pain points. A common term I’ve heard is “undifferentiated heavy lifting,” which essentially refers to work that teams do but don’t find rewarding.
During this conference, I heard a customer mention that their teams spend 60% of their time on maintenance and toil, and only 40% on innovation. That’s exactly what we aim to flip with autonomous systems. Ideally, we want to see 80% of time spent on innovation, with 20% or less dedicated to toil. While reaching zero toil might be a bit of a stretch, we can certainly reduce it significantly by using automation.
Beyond toil, autonomous systems also improve operational efficiency. They provide better customer experiences—faster websites, quicker API responses—all of which directly correlate with revenue growth.
Ready to move from observability to autonomous action?
Book a Sedai demo to turn monitoring insights into automated optimization that improves performance and reduces cloud costs.
Operational Efficiency and Cost Savings
John Jamie (Sedai VP Growth & Product Marketing): What other outcomes are companies realizing from adopting autonomous systems?
Ilan Rabinovitch (Product & Community Leader & Ex Datadog SVP): Apart from eliminating toil, efficiency is a big driver. Autonomous systems enable faster decision-making and resource management, which in turn boosts both the top and bottom lines of a company.
For example, autonomous systems can make decisions instantly, like adjusting scaling groups or routing traffic, without waiting for human input or the next sprint cycle. The immediate impact on cost savings is enormous, as you’re able to prevent resource wastage. Additionally, these systems help balance performance, customer experience, and costs based on real-time traffic. When the system can optimize these elements on the fly, it creates a massive opportunity for both revenue growth and margin improvement.
Guidance for Teams Starting with Autonomous Systems
John Jamie (Sedai VP Growth & Product Marketing): For someone unfamiliar with autonomous systems, where would you suggest they begin?
Ilan Rabinovitch (Product & Community Leader & Ex Datadog SVP): Start small. Take a problem that you’ve been reluctant to address, either because you don’t have the time or you’re concerned about the risks. Let an autonomous system handle it through smaller, controlled experiments. This helps build confidence in automation without the risk of large-scale disruptions.
The beauty of autonomous systems is that they don’t rely on humans pressing buttons or running commands. If an issue arises, the system can roll back the changes and restore operations without intervention. The fear of breaking production systems is significantly reduced. Focus on the smaller wins, and gradually scale the system to handle bigger parts of your environment.
Platform teams that combine Datadog visibility with autonomous optimization report both faster incident resolution and lower cloud costs — because the two capabilities compound each other. Book a demo to see that combination applied to your environment.
Conclusion
Ilan Rabinovitch’s feedback highlights the transformative potential of autonomous systems in the cloud. By combining observability tools with machine learning, organizations can reduce toil, improve efficiency, and deliver superior customer experiences. For companies just starting, small steps towards autonomy will lead to significant long-term gains.
