September 23, 2025
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Sedai is an autonomous cloud optimization platform that uses AI to continuously improve cost, performance, and reliability across cloud workloads. It positions itself as a “self-driving” cloud, capable of learning from workload behavior, predicting future needs, and acting automatically to adjust resources.
The platform works across Kubernetes, serverless, VM-based infrastructure, and storage systems. By applying machine learning and reinforcement learning techniques, Sedai aims to deliver ongoing optimization that reduces costs, improves availability, and prevents failures.
In some cases, Sedai may not be the best fit. Because its optimization relies heavily on historical usage data, it can struggle with the unpredictability of spiky or AI-driven workloads. Its live migrations restart jobs rather than preserving state, and its flexibility at the workload level is limited.Â
For teams that need more precision, alternatives such as DevZero offer predictive algorithms that better handle demand spikes, live migration without restarts, and granular policies across clusters, nodes, and workloads. Other tools like Karpenter, Cast AI, and Kubecost also provide targeted capabilities, giving organizations different paths to address their unique challenges.
What are the benefits of using Sedai?
Sedai offers a wide range of advantages for engineering and operations teams:
- Release intelligence and Smart SLOs — Tracks new releases for latency, cost, and error budgets while automatically setting and maintaining SLOs.
- Storage Optimization - Continuously improves how cloud storage is provisioned and consumed, reducing costs tied to unused capacity and aligning resources with actual usage patterns.
- Data Processing Optimization - Tunes big data and analytics workloads such as pipelines, ETL jobs, and machine learning data prep, ensuring they run more efficiently and cost-effectively.
- Multi-layer coverage — Supports Kubernetes, serverless, dataflows, and more, making it suitable for mixed environments.
For companies seeking automation at scale, these features promise both financial and operational benefits.
How does Sedai work?
Sedai connects to your cloud and monitoring infrastructure in an agentless way, meaning it does not require invasive installation. Once deployed, it continuously analyzes workload behavior and applies optimizations through its Decision Engine.
At the core are specialized AI agents that focus on specific objectives, such as reducing cost, improving availability, or maintaining performance. These agents learn from workload patterns over time, applying reinforcement learning to predict and act. Every action is validated for safety before implementation, which reduces risk for teams that want to embrace autonomy without fear of downtime.
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For optimization, Sedai dynamically scales workloads by adjusting replicas and tuning CPU and memory settings. It also works at the infrastructure level, draining nodes, managing clusters, and recommending purchasing strategies such as a balance of on-demand, spot, and savings plans. Beyond cost, it applies autonomous remediation to resolve reliability issues proactively. Sedai also enhances release workflows by providing intelligence on performance changes during new deployments and by aligning resources to meet Smart SLOs.
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In practice, this means Sedai runs continuously in the background, aiming to maintain cost efficiency, reliability, and performance without requiring manual intervention.
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What are Sedai’s disadvantages?
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Cast AI provides essential benefits, but there are also tradeoffs to consider:
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- Focus on breadth over depth — Sedai covers many workload types, but in doing so, it does not always offer the deep workload-level optimization needed for GPU-heavy AI environments.
‍ - Complexity of adoption — Its AI-driven, autonomous approach requires a mindset shift. Teams used to manual or semi-automated processes may face a learning curve.
‍ - Less manual control — By design, Sedai reduces the role of human intervention. For some organizations, especially those in highly regulated industries, this may create discomfort.
‍‍ - Enterprise orientation — Sedai’s feature set is best suited for large-scale, multi-cloud deployments. Smaller organizations may find the platform too heavy for their needs.
‍‍ - Reactive risk — Although Sedai validates actions, some teams may be wary of delegating mission-critical changes entirely to automation. Trust-building can take time.
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For many enterprises, these tradeoffs are acceptable. But for teams seeking precision control in AI-driven workloads, the limitations can be meaningful.
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Alternatives to Sedai
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Several alternatives are available in the cloud optimization and Kubernetes ecosystem:
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- Karpenter – An open source autoscaler from AWS that provisions right-sized nodes. Effective at cluster elasticity but limited to node-level scaling.
‍‍ - KEDA — Open source project for event-driven autoscaling in Kubernetes. Scales workloads based on external event sources but lacks deeper cost or workload-level optimization.
‍ - DevZero – An AI-native optimization platform with predictive scaling, state-preserving live migration, microVM isolation, and workload-level GPU optimization. Best suited for AI pipelines, training, and inference jobs.
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These tools vary in scope, from observability to node-level scaling. The key choice is between reactive, finance-focused solutions and proactive platforms that optimize for performance and safety.
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Why DevZero is different
While Sedai is strong for broad, autonomous optimization across multiple layers of the cloud, DevZero was designed to go deeper into workload efficiency and reliability. Its differentiators include:
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- Granular policies — Control at the cluster, node, and workload level, allowing teams to fine-tune optimizations with much more precision.
‍ - Predictive algorithms — Uses advanced machine learning to forecast demand, rather than relying only on historical usage patterns. This ensures better alignment of resources and improved performance during workload spikes.
‍ - Live migration without restarts — Moves workloads seamlessly with state preserved, avoiding the cold starts and disruptions that Sedai and other platforms cannot prevent.
‍‍ - GPU-aware optimization — Right-sizes GPU workloads themselves, not just the nodes, keeping these expensive resources fully utilized and productive.
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‍For companies operating at the frontier of AI-driven workloads, DevZero provides a more future-ready foundation.
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