{"best_for":["Enterprise data teams","Organizations with strict data residency requirements","Technical teams experienced with Kubernetes"],"citation":{"dataset":"aitoolsforbusiness-agent-tool-export","directory_tool_url":"https://aitoolsforbusiness.ai/iomete","json_profile_url":"https://aitoolsforbusiness.ai/data/tools/iomete.json","markdown_profile_url":"https://aitoolsforbusiness.ai/data/markdown/tools-md-026.json","schema_version":"1.4.0","suggested_citation_label":"AI Tools for Business: iomete (https://aitoolsforbusiness.ai/iomete)"},"features":["Self-Hosted Architecture: Supports deployment on-premises, in private or public clouds, or hybrid setups via Kubernetes.","SQL Editor: A web-based environment for writing and executing queries with auto-completion and syntax highlighting.","Real-time Streaming: Supports ingesting and processing data streams from Kafka, Kinesis, and Pulsar.","Data Access Controls: Provides security management at the row and column level, including data masking.","ML Notebooks: Integrated interactive environments supporting Python, R, and Scala for machine learning workflows.","Data Catalog: A central repository for managing metadata, tracking lineage, and discovering data assets."],"freshness_status":"fresh","name":"iomete","pricing_note":"Offers a Free Tier (up to 100 vCPUs). Paid tiers include an Enterprise Plan starting at $500 per vCPU per year (with a $100,000 minimum) and a Business Critical Plan with custom pricing (with a $250,000 minimum).","pricing_url":"https://iomete.com/pricing","primary_category":"Data & Analytics","profile_last_verified":"2026-06-06T17:26:01.179Z","secondary_categories":[],"short_description":"iomete is a self-hosted data lakehouse platform that combines data lake flexibility with data warehouse performance for enterprise data teams.","slug":"iomete","sponsorship_status":"none","url":"https://aitoolsforbusiness.ai/iomete","use_cases":["Maintaining Data Sovereignty: Hosting data within a company's own trust perimeter to help meet GDPR, HIPAA, or SOC2 compliance requirements.","AI and ML Model Training: Using the lakehouse and ML notebooks to prepare datasets and train models on local or hybrid infrastructure.","Real-time Operational Analytics: Streaming data from Kafka or Kinesis into Iceberg tables for SQL-based analysis.","Hybrid Cloud Data Management: Deploying clusters across multiple regions or combining on-premises data centers with public cloud resources."],"website_url":"https://iomete.com/"}