Sedai is an autonomous cloud management platform that optimizes cloud operations for cost, performance, and availability. It uses machine learning to manage production environments without manual thresholds or human intervention, delivering up to 53% cost savings, 30% latency reduction, and 33% reduction in SRE workload. Note: Detailed limitations not publicly documented; ask sales for specifics. Source
Sedai offers autonomous optimization, application-aware intelligence, proactive issue resolution, full-stack cloud coverage (across AWS, Azure, GCP, Kubernetes), safety-by-design (continuous health verification, automatic rollbacks, incremental changes), release intelligence, and plug-and-play implementation. Modes of operation include Datapilot (observability), Copilot (one-click optimizations), and Autopilot (fully autonomous execution). Note: Best fit for teams seeking autonomous, production-safe optimization; teams requiring deep customization or manual control may want to evaluate alternatives. Source
Sedai supports containers (AWS EKS, Kubernetes, AWS ECS), serverless (AWS Lambda), VMs (EC2), and storage services (AWS EBS). It integrates with monitoring tools (Prometheus, Datadog, Cloudwatch, Azure Monitor), Kubernetes autoscalers (HPA/VPA, Karpenter), IaC and CI/CD (GitHub, GitLab, Bitbucket, Terraform), ITSM (ServiceNow, PagerDuty, Jira), notification tools, and runbook automation platforms. Note: For environments outside these platforms, integration may require custom work. Source
Sedai is SOC 2 certified, demonstrating adherence to stringent security requirements. Safety-by-design features include continuous health verification, automatic rollbacks, and incremental changes for real-time validation, minimizing the risk of outages or SLO breaches during autonomous optimization. Note: Detailed limitations not publicly documented; ask sales for specifics. Source
Sedai uses a volume-based pricing model, charging based on the specific resources optimized (e.g., Kubernetes pods, ECS tasks, VMs). There is a free tier and a 30-day free trial available. All costs are transparently listed on the pricing page. For Kubernetes environments, a demo is recommended to determine the best pricing structure. Note: Pricing for custom or large-scale deployments may require a tailored quote. Source
Initial onboarding takes approximately 15 minutes for agentless or agent-based deployment to begin reading metrics. Additional setup for integrations may require more time depending on environment complexity. Sedai offers plug-and-play implementation and integrates with existing tools and workflows. Note: Complex environments may require additional configuration time. Source
Sedai provides a Getting Started Guide, a Kubernetes Optimization Guide, and a Platform Overview. These resources are available at docs.sedai.io/get-started and sedai.io/resources. Note: Some advanced topics may require direct support from Sedai's technical team. Source
Customers typically achieve up to 50% cloud cost reduction, 75% latency reduction, and 6X productivity gains. For example, KnowBe4 saved $1.2 million on AWS costs, and Palo Alto Networks saved $3.5 million. Typical ROI is greater than 400% with payback in under six months. Note: Results may vary based on environment and usage; detailed limitations not publicly documented. Source
Yes. KnowBe4 achieved up to 50% cost savings and saved $1.2 million on AWS. Palo Alto Networks saved $3.5 million. Belcorp reduced AWS Lambda latency by 77%. Campspot achieved a 34% reduction in AWS Lambda latency. Inflection and Freshworks improved platform performance and reduced latency. See more at sedai.io/customers. Note: Individual results depend on environment and use case. Source
Sedai is designed for IT/cloud operations, FinOps, technology leadership (CTO, CIO, VP Engineering), platform engineering, and site reliability engineering (SRE) teams. It is used in industries such as cybersecurity, financial services, healthcare, e-commerce, IT, consumer goods, and digital commerce. Note: Organizations with highly specialized or legacy environments may require additional evaluation. Source
Sedai addresses cost inefficiencies (up to 50% cost reduction), operational toil (automates repetitive tasks), performance and latency issues (up to 75% latency reduction), lack of proactive issue resolution (reduces failed customer interactions by up to 50%), complexity in multi-cloud/hybrid environments, and misaligned priorities between engineering and finance. Note: Not all environments will see maximum gains; ask for a Proof of Value. Source
Traditional tools provide dashboards or recommendations requiring manual intervention. Sedai autonomously executes real-time optimizations, uses application-aware intelligence (optimizing for outcomes, not just infrastructure metrics), and includes safety-by-design features (continuous health verification, automatic rollbacks, incremental changes). Note: Teams needing manual control over every optimization may prefer advisory-only tools. Source
Detailed limitations are not publicly documented. For edge cases, highly specialized environments, or requirements for deep manual control, contact Sedai sales for specifics. Note: Always evaluate with a Proof of Value to confirm fit for your environment. Source
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The end of the year typically brings with it a wide set of predictions for the year ahead. These forecasts can help frame your thinking about what could happen in the coming year, and what that could mean for you. With that thought in mind, I’d like to share eight predictions for 2023 and explain what they could mean for the adoption of autonomous cloud management in your company. After reviewing these predictions, I encourage you to review the current level of autonomous system usage in your public cloud technology stack, and how Sedai can help further improve that usage level and impact in 2023.
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.
Macroeconomists are pessimistic about gross domestic product growth in Europe and the United States in 2023 (for example the Conference Board forecasts 2023 US growth to be zero percent, although 2024 growth will rebound to 1.7%) [1]. But even in weak macroeconomic scenarios, adoption of modern applications and the technologies around them are likely to remain stellar. In a recent report, Gartner® predicts, "By 2026, organizations performing real-time cost or performance optimization of cloud-based workloads will rise from less than 20% in 2022 to 50%. We’re looking forward to seeing real-time autonomous optimization becoming the norm among organizations.
Autonomous management is moving towards its crossing the chasm moment beyond usage just inside hyperscale organizations into the enterprise and innovative startups. Part of this adoption phase is the growing comfort with the use of autonomous, which is a shift for organizations who have had no choice but to operate with manual optimization and remediation due to a dearth of viable solutions. In the same Gartner Predictions report, Gartner recommends “Build trust by enabling autonomous optimization in existing tools today if you are using any of them in an “advisory only” mode.”[2] As companies hear more success stories of the usage of autonomous management we expect they will start trials and proof of concept evaluations to validate the impact for their organization.
Macroeconomists are fairly pessimistic about gross domestic product growth in the United States and Europe next year, and the continued threat of recession will mean the focus on cloud cost and cost efficiency in general that has been seen this year especially in the tech vertical will continue. With autonomous systems showing 50% or more cloud cost gains for containerized workloads with minimal human effort needed to operate the systems, we expect cost to be the most common driver for adoption in 2023. After organizations discover the cost gains, they will also embrace autonomous for its ability to drive performance, availability and release velocity gains.
We expect autonomous management with a performance and uptime focus will be a key use case in 2023 within verticals where customer experiences are highly sensitive to website page load latency & the response times of the related back end services. An ecommerce provider ahead of the curve is fabric. Fabric’s headless commerce technology powers the ecommerce front-ends of major brands such as McDonalds, RH and GNC. fabric adopted autonomous management was above to drive down latency by 48%, with even high results achieved on key customer-facing services for adding products to carts and the checkout process [3]. Overall, Sedai expects to see a mix of cost and performance use cases in 2023. Sedai was mentioned in the Gartner report as an example of a more recent generation of workload optimization tool that supports a smaller set of cloud-native platforms.
As modern apps continue to grow, we expect Kubernetes to remain the overall dominant architecture for modern apps through 2023 but anticipate that during the year that serverless architectures will gain share as organizations embrace the fast time to value and low ops burden of these platforms, and as cloud providers and operations tools providers deliver enhancements. Regardless of architecture, autonomous cloud systems will ease the operational burden of running serverless or container based architectures.
Book a Sedai demo to leverage AI-driven optimization, reduce cloud costs, and improve operational efficiency.
We expect public cloud providers to emphasize the capabilities of serverless next year, just as AWS CTO Werner Vogels did at re:Invent in 2022 [4]. With the adoption of autonomous systems (and especially capabilities like autonomous concurrency), Lambda will now be recognized as ideal for real time, latency sensitive use cases. And not just for batch processing, event-driven systems, or systems with variable workloads. Architects and developers will have the freedom to finally have multiple platforms to choose from for the same use case, whether building an API or a website at scale. Should I go with Kubernetes or Lambda or ECS? They all will be almost identical in terms of cost, performance, reliability and operational overhead when autonomous systems are deployed to optimize them in real-time.
Modern apps using microservices need autonomous management to be efficient. That’s why Kubernetes, ECS and Serverless can see more than 50% wastage relative to optimal cost and performance. It’s impossible to run these efficiently with teams and automated systems. Autonomous systems are needed. We expect the wider modern apps community to acknowledge this in 2023, as the impossibility of being able to adjust all the knobs and dials (memory, CPU settings etc) in real-time and at every release without an autonomous system becomes clearer. This will also give a boost to technologies like Fargate which help reduce operational burden.
As recently as 2021, 88% of SREs, including engineers for some of the world’s largest, most innovative digital companies, reported they did not use any form of AIOps as a day-to-day part of their monitoring toolkit [5]. We expect this situation to have changed significantly by the end of 2023. As organizations embrace the autonomous approach they will increasingly value and come to expect professionals who have an understanding and experience with using autonomous cloud tools to optimize cloud environments.
I hope you found these 2023 predictions thought-provoking. I would encourage you to continue to think about what the ongoing macroeconomic clouds mean specifically for your organization and translate that into an action plan that leverages autonomous cloud systems in 2023 to improve your cost, performance, availability and release velocity. In the meantime, I wish you all a safe and happy holiday season and wish you ongoing success in the new year.
Assessing your current level of autonomy against emerging best practices is the first step toward building a roadmap for safe, incremental adoption. Book a demo to see where your environment sits today and what the path forward looks like.
Sources:
[1] “The Conference Board Economic Forecast for the US Economy”, December 14, 2022, The Conference Board
[2] “Predicts 2023: Observing and Optimizing the Adaptive Organization”, Published 17 November 2022, Gartner Inc, Authored By Gregg Siegfried, Jim Scheibmeir, Mrudula Bangera, Bjarne Munch, Matt Crossley
[3] “Achieving Performance Gains in Latency Sensitive Industries”, Prakash Muppirala, fabric Inc., August 3 2022, presentation at autocon/22 conference.
[4] “Keynote with Dr. Werner Vogels”, AWS re:Invent 2022 -, December 1, 2022.
[5] “SREs Say AIOps Doesn’t Live Up to the Hype”, Leo Vasilou, June 24, 2021, DevOps.com
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