IOMETE

One control plane. Spark on every Kubernetes cluster you run. With IOMETE multi-cluster: → Define users, access rules and catalogs once, not in every cluster → Manage all your data planes from a single console → Pick a data plane, then a namespace, when you create compute → See quotas for every cluster in one view Self-hosted, on your own infrastructure. #DataLakehouse #Kubernetes #ApacheSpark

You do not need to trade Spark compatibility for infrastructure control. IOMETE gives data teams the Spark, SQL and notebook workflows they know—on infrastructure they control. Customers save 2–5x over cloud-only platforms. See the cost model →

Your most sensitive data should not force you into someone else’s infrastructure model. IOMETE runs on-prem, in private cloud, or hybrid—so your data and compute stay inside the boundary you choose. Same Spark, SQL and notebooks. No cloud-only lock-in. See how the model works →

At petabyte scale, the lakehouse decision is no longer only about query performance. It is about who owns the compute—and who keeps the markup. IOMETE runs a full Spark lakehouse on your infrastructure: on-prem, private cloud or hybrid. Same Spark, SQL and notebooks. Customers save 2–5x versus cloud-only platforms. See the cost math →

Your regulator does not audit your vendor's promise. It audits your control. Under DORA, a SaaS-only lakehouse is not just a data platform choice. It is a third-party ICT dependency your team has to document, test, monitor, and defend. IOMETE gives financial-services data teams a different architecture: a self-hosted lakehouse running inside your own infrastructure boundary. Storage, compute, metadata, encryption keys, access logs, and audit trails stay under your control. This is not theoretical. IOMETE is used in financial-services environments with air-gapped deployment requirements, holds SOC 2 Type II certification, and supports HIPAA and GDPR compliance. If your current Databricks or Snowflake setup leaves you explaining vendor concentration risk to compliance, the next conversation should be architectural, not contractual. Book a DORA architecture review.

100,000+ vCPUs. Four data centers. Five legacy analytics platforms consolidated into one self-hosted lakehouse. That is not a benchmark slide. It is IOMETE running at production scale inside a customer's own infrastructure perimeter. The commercial case is simple: at enterprise scale, SaaS lakehouse economics compound fast. DBUs, credits, egress, platform fees, and managed-service markups turn your own data into someone else's margin. IOMETE runs the lakehouse on infrastructure you control: on-prem, private cloud, public cloud, or hybrid. You keep the open architecture, Spark/Iceberg foundation, SQL, notebooks, catalog, governance, and workload isolation. You also keep the infrastructure economics. The proof is now concrete: large-scale production deployment, a tier-1 bank moving overnight batch to sub-60-second processing, and regulated teams using IOMETE where data cannot leave the trust boundary. See the self-hosted cost model.