Sedai offers an autonomous cloud management platform that can optimize BigQuery slot allocation and usage, eliminating manual intervention. By leveraging machine learning, Sedai ensures slots are managed efficiently for cost, performance, and availability, always prioritizing safety and continuous validation. Source
Sedai is patented for safe, autonomous optimizations in production environments. Unlike risky optimizers that make all-at-once changes, Sedai performs gradual, incremental optimizations with continuous validation checks, ensuring no incidents or SLO breaches occur. Source
Yes, Sedai provides full-stack coverage, optimizing compute, storage, and data across AWS, Azure, GCP (including BigQuery), and Kubernetes environments. This ensures unified management for organizations using multi-cloud strategies. Source
Autonomous slot management reduces manual toil, optimizes resource allocation, and ensures cost efficiency. Sedai's platform can deliver up to 50% cost savings, 75% latency reduction, and 6X productivity gains by automating routine tasks and proactively resolving issues. Source
Sedai eliminates the need for manual slot allocation and monitoring by using machine learning to optimize resources in real time. This reduces human error, speeds up response to anomalies, and ensures continuous improvement, all while maintaining safety and compliance. Source
Sedai's primary purpose is to eliminate toil for engineers by automating cloud resource optimization, including BigQuery slots. This enables teams to focus on impactful work and innovation rather than manual optimizations. Source
Sedai continuously learns from interactions and outcomes, improving its optimization and decision models. This ensures that slot management and other optimizations become more effective as the platform adapts to real-world usage patterns. Source
Sedai offers three modes: Datapilot (observability), Copilot (one-click optimizations), and Autopilot (fully autonomous execution). This flexibility allows organizations to choose the level of automation that fits their operational needs. Source
Sedai's Release Intelligence feature tracks changes in cost, latency, and errors for each deployment, ensuring smoother releases and minimizing risks. This is especially valuable for teams managing BigQuery workloads and other cloud resources. Source
Sedai provides detailed technical documentation covering features, setup, and usage. Access the documentation at docs.sedai.io/get-started and explore additional resources at sedai.io/resources.
Sedai's plug-and-play implementation allows setup in just 5 minutes for general use cases and up to 15 minutes for specific scenarios like AWS Lambda. For complex environments, a demo can be scheduled to discuss tailored setup. Source
Sedai offers personalized onboarding sessions, a dedicated Customer Success Manager for enterprise customers, detailed documentation, a community Slack channel, and email/phone support. These resources ensure smooth adoption and ongoing assistance. Source
Yes, Sedai offers a 30-day free trial, allowing users to experience the platform's value firsthand without financial commitment. Source
Sedai integrates with monitoring tools (Cloudwatch, Prometheus, Datadog, Azure Monitor), Kubernetes autoscalers (HPA/VPA, Karpenter), IaC & CI/CD platforms (GitLab, GitHub, Bitbucket, Terraform), ITSM tools (ServiceNow, Jira), notification tools (Slack, Microsoft Teams), and runbook automation platforms. Source
Sedai is SOC 2 certified, demonstrating adherence to stringent security requirements and industry standards for data protection and compliance. Source
Sedai offers autonomous optimization, proactive issue resolution, full-stack cloud coverage, smart SLOs, release intelligence, plug-and-play implementation, multiple modes of operation, enhanced productivity, and safety-by-design. Source
Sedai ensures every optimization is constrained, validated, and reversible. Continuous health verification, automatic rollbacks, and incremental changes guarantee safe operations and compliance with enterprise-grade governance. Source
Yes, Sedai's Release Intelligence tracks changes in cost, latency, and errors for each deployment, improving release quality and minimizing risks for BigQuery and other cloud workloads. Source
Sedai autonomously rightsizes workloads and eliminates waste, reducing cloud costs by up to 50%. This is achieved through continuous optimization based on real application behavior. Source
Sedai automates routine tasks like capacity tweaks and scaling policies, delivering up to 6X productivity gains for engineering teams. Source
Sedai is ideal for platform engineering, IT/cloud ops, technology leadership, site reliability engineering (SRE), and FinOps roles. Organizations with significant cloud operations across industries such as cybersecurity, IT, financial services, healthcare, travel, and e-commerce can benefit. Source
Sedai delivers up to 50% cost savings, 75% latency reduction, 6X productivity gains, and reduces failed customer interactions by up to 50%. Customers like Palo Alto Networks saved $3.5 million, KnowBe4 achieved 50% cost savings, and Belcorp reduced AWS Lambda latency by 77%. Source
Sedai's case studies span cybersecurity, IT, financial services, security awareness training, travel/hospitality, healthcare, car rental, retail/e-commerce, SaaS, and digital commerce. Source
KnowBe4 achieved 50% cost savings and saved $1.2 million on AWS bills. Palo Alto Networks saved $3.5 million, reduced Kubernetes costs by 46%, and saved 7,500 engineering hours. Belcorp reduced AWS Lambda latency by 77%. KnowBe4 Case Study, Palo Alto Networks Case Study
Sedai is the only patented platform for safe, autonomous optimizations in production. Unlike competitors that make risky, all-at-once changes, Sedai performs gradual, validated optimizations, ensuring no incidents or SLO breaches. Sedai also offers application-aware intelligence and full-stack coverage, setting it apart from traditional tools. Source
Sedai's patented safety-first technology, autonomous optimization, proactive issue resolution, application-aware intelligence, and plug-and-play implementation differentiate it from competitors. Sedai is best for organizations seeking safe, continuous improvement without manual intervention. Source
Platform engineers benefit from reduced toil and IaC consistency; IT/cloud ops teams see lower ticket volumes and safer automation; technology leaders achieve measurable ROI and reduced spend; FinOps teams gain actionable savings; SREs enjoy fewer SLO breaches and less pager fatigue. 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. Source
Customers often face fragmentation, repetitive toil, risk vs. speed trade-offs, autoscaler limits, visibility-action gaps, ticket volume, change risk, config drift, hybrid complexity, capacity/cost surprises, outcome gaps, spend pressure, tool sprawl, talent bandwidth, release risk, pager fatigue, brittle automation, and cross-team trade-offs. Source
Sedai is trusted by Palo Alto Networks, HP, Experian, KnowBe4, Expedia, CapitalOne Bank, GSK, and Avis. These companies rely on Sedai to optimize their cloud environments and improve operational efficiency. Source
Customers praise Sedai's quick setup (5–15 minutes), agentless integration, personalized onboarding, extensive documentation, community Slack channel, and risk-free trial. These features contribute to positive feedback regarding ease of use. Source
Nikhil Gopinath
Content Writer
April 22, 2026
Featured
If you're running BigQuery at scale, you've probably felt this before: your reserved slots are sitting underutilized, your autoscale bill is climbing, a critical dashboard is missing its SLO, and somewhere an engineer is spending their afternoon manually tweaking reservation configurations yet again.
It's a frustrating pattern, and it's more common than most teams want to admit.
BigQuery's pricing model is built around slots — units of compute that determine how fast your queries run. You can commit to a baseline number of slots upfront (cheaper, but fixed), or let BigQuery autoscale on demand (flexible, but expensive). In theory, you balance the two to get performance at a reasonable cost.
In practice, it rarely works out that cleanly.
Most organizations end up over-provisioning baseline slots they don't fully use, while simultaneously overspending on autoscale to cover gaps they shouldn't have. High-priority workloads compete with low-priority batch jobs for the same pool of slots. Engineers set reservations based on gut feel and historical snapshots, then react when something breaks.
The root of the problem isn't visibility — Google has made real strides there. The deeper issue is organizational: FinOps teams and engineering teams are often working toward the same goal from different angles, without a shared framework for making and enforcing slot allocation decisions. At scale, with dozens of jobs, multiple projects, and mixed real-time and batch workloads, the complexity quickly outpaces what any team can manage by hand.
Today we're launching autonomous BigQuery optimization. The key word here is autonomous.
Sedai connects to your GCP project and continuously analyzes slot consumption across all of your workloads. It learns how your jobs actually behave: when they run, how many slots they need, and how they interact with each other. Then it acts, making ongoing adjustments to slot allocation and reservations to keep your most important workloads running smoothly, without unnecessary autoscale spend.
This isn't another dashboard. It's not a report that tells you what to fix and leaves the rest to you. Sedai closes the loop, so that action directly follows from insights.
The new capabilities we’re introducing today include:
Slot usage analysis. Sedai surfaces which workloads are consuming slots efficiently, which are over-allocated, and where autoscale is filling gaps that baseline slots should be covering. You get a clear picture of where your slot investment is going, and where it's being wasted.
Workload classification and separation: Sedai detects whether workloads running in a reservation are batch (e.g. scheduled pipelines, overnight data jobs) or interactive (e.g. dashboards, analyst queries, executive reports). When the two types share a reservation, they compete for the same slots, and time-sensitive queries often lose. Sedai identifies these mixed reservations and recommends separating them where it makes sense, so critical workloads always have the capacity they need.
Reservation right-sizing. By analyzing actual demand patterns by workload and time of day, Sedai recommends reservation adjustments that protect high-priority jobs without over-provisioning. Low-priority batch workloads stop crowding out time-sensitive queries.
Autoscale max optimization: Setting your autoscale ceiling too high isn't just a cost risk — it's an SLO risk. A high autoscale max enables BigQuery to borrow idle slots from neighboring reservations, which looks efficient until those reservations need their capacity back and your workload gets interrupted mid-run. It also means queries can consume far more slots than necessary simply because the capacity is there, running faster but at significantly higher cost. Sedai finds the right autoscale ceiling for each reservation, with enough headroom to handle real spikes, sized tightly enough to protect performance and control spend.
Autonomous execution. Unlike native GCP tools and FinOps platforms that stop at recommendations, Sedai closes the loop, applying changes with clear before/after cost and performance projections, and the ability to validate or roll back any action.
If you're in platform or data engineering, this means less time hand-tuning reservations and more confidence that your slot allocation is actually aligned with workload priorities.
If you're in FinOps, this means a clearer picture of where your BigQuery spend is going, along with concrete, automated actions that bring it down without requiring constant back-and-forth with engineering.
Both teams get a shared, governed view of slot usage and optimization actions. That alone removes a lot of organizational friction.
Book a Sedai demo to speak with a technical expert.
Sedai BigQuery optimization is generally available today. If you're ready to stop managing slots by hand, stop by the Sedai booth at Google Cloud Next, or schedule a demo and we'll show you what it looks like in your environment.
