
概览
主要功能
- 自主资源大小和缩放
- 持续成本优化
- 实时性能和可用性监控
- 支持计算、容器、无服务器和数据服务
- 与 Datadog、Prometheus 和 CloudWatch 集成
- 基于策略的守护带和批准
价格
- 模型
- Freemium
- 分类
- 王常服务
- 评分
- 4.8 / 5 (5)
使用场景
自主云成本优化
持续调整 AWS、Azure 和 GCP 中的计算、容器和无服务器工作负载,以在 SRE 或 DevOps 团队不进行手动调节的情况下减少云费用。
主动的性能优化
通过 Datadog、Prometheus 和 CloudWatch 的生产测度来预防性地解决性能问题,超出基于警报的监控范围。
Kubernetes 缩放自动化
使用基于策略的守护带和回滚安全性,自动调优 Kubernetes 工作负载的资源请求、限制和缩放配置。
多云可用性管理
通过让 Sedai 在工作量模式的基础上进行闭环配置决策,从而维持可用性 SLA,在多个云供应商和服务之间保持可用性。
优点 & 缺点
优点
- 闭环自动化减少手动调节
- 对多云和多服务的覆盖
- 同时优化成本和性能
- 集成了常见的可观察性工具
- 安全守护带和回滚选项
缺点
- 企业定价可能不适合小型团队
- 自主操作需要信任和注册时间
- 最佳价值取决于工作负载的规模和变异性
评测
5 个评分的平均值。
登录以留下评测。
Solid for our team
We rolled this out across the team last quarter and integrates with common observability tools. Continuous cost optimization fits neatly into how we already work, and support for compute, Kubernetes, and serverless removed a step we used to do by hand. Best value depends on workload scale and variability, which is the main caveat, but it has held up under daily use.
Does the job
Pretty happy overall. Autonomous rightsizing and scaling just works and integrates with common observability tools. but no dealbreakers — I'd recommend it to a friend without hesitating.
Compared a few options
Evaluated this against two competitors. Where it wins: policy-based guardrails and approvals and closed-loop automation reduces manual tuning. On balance the feature set — especially integrations with Datadog, Prometheus, and CloudWatch — justifies the 5 stars for our use case.
Solid for our team
We rolled this out across the team last quarter and closed-loop automation reduces manual tuning. Autonomous rightsizing and scaling fits neatly into how we already work, and autonomous rightsizing and scaling removed a step we used to do by hand. Best value depends on workload scale and variability, which is the main caveat, but it has held up under daily use.
Solid for our team
We rolled this out across the team last quarter and closed-loop automation reduces manual tuning. Performance and availability monitoring fits neatly into how we already work, and performance and availability monitoring removed a step we used to do by hand. but it has held up under daily use.
问答
How much does Sedai cost? How does pricing work?
Sedai uses a simple, volume-based pricing model, designed to ensure our customers achieve a positive ROI. For example, we charge 1 Sedai Billing Unit (SBU) for each vCPU that our platform manages. There are no overage penalties — if you exceed your monthly commitment, you simply pay the same rate for the extra usage. You can also adjust your commitment, as your needs change over time. Please book a demo to discuss our specific pricing options & discounts.
Asked by Noelia Campos · Mar 3, 2026
How does Sedai make sure that optimizations never cause availability issues or degrade performance?
Safe optimization is our secret sauce. When Sedai identifies a potential cost or performance improvement, our platform makes that optimization in gradual steps, while continuously validating the impact of the change on your environment. For example, if Sedai determines it can lower the memory limit of a container without impacting performance, it won’t make a big change all at once, from say 12 to 4 GB. Instead, Sedai will gradually lower the memory to achieve maximum cost savings, until the exact point where performance is impacted.
Asked by Otto Berg · Feb 9, 2026
Which cloud providers does Sedai support? And what specific services can Sedai optimize?
Sedai supports the widest range of cloud services & on-prem workloads in our industry. This includes all the major cloud providers: AWS, Microsoft Azure, Google Cloud Platform, & Oracle Cloud Infrastructure. We’re the experts at optimizing Kubernetes — think EKS, AKS, & GKE, as well as on-prem, self-managed clusters. But our platform is unique in going way beyond K8s. Sedai also optimizes storage, data platforms, and AI/ML workloads. Check out our integrations page for the full list.
Asked by Ethan Brooks · Jan 14, 2026
What makes Sedai different from competitors? Why choose Sedai?
Only Sedai deeply understands how your applications behave, which is why our platform can optimize safely without causing performance or availability issues. Most tools either stop at recommendations or make risky changes based on surface-level metrics like CPU and memory utilization. Sedai goes deeper. Our patented ML models learn what your applications actually need by analyzing traffic patterns, dependencies, and golden signals like latency, errors, traffic, and saturation. That application-aware approach is what makes Sedai safe in production. To date, Sedai has performed more than 25 million optimizations with zero incidents.
Asked by Oscar Lindqvist · Jan 3, 2026
Will I be able to control Sedai’s actions? What if we want to roll back a change?
Sedai offers three operating modes that give you full control. In Datapilot mode, Sedai provides read-only recommendations, for you to implement. In Copilot mode, Sedai lets your team approve optimizations with one click, which Sedai then safely executes. Once you trust the platform, you can turn on Autopilot mode for resources or environments that you select, which enables Sedai to make optimizations autonomously. Across the board, Sedai enables your team to instantly roll back a change, schedule maintenance windows, & pause optimizations for any reason.
Asked by Hasan Demir · Dec 3, 2025
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