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DirectoryData & AnalyticsData Warehouse & LakehouseOpenEverest

OpenEverest

Open Source

About OpenEverest

OpenEverest is an open-source platform for automated database, storage, and LLM provisioning and management. It supports multiple database technologies and can be hosted on any Kubernetes infrastructure, in the cloud or on-premises. The platform is released under the Apache 2.0 license and is a CNCF Sandbox project. Every technology is implemented as a provider, allowing the catalog to grow without changes to the core platform. Users can build their own provider with the Provider SDK.

Products

  • Unified Database Dashboard: Manages databases, caches, and AI workloads from a single control plane across any Kubernetes infrastructure.
  • Visual Component Explorer: Inspects proxies, pods, and containers in an interactive topology view without kubectl.
  • In-App Log Streaming: Streams and searches Kubernetes pod logs directly inside OpenEverest.

Used for

  • Creating production-ready databases from the UI, API, or CLI
  • Adding nodes, shards, or replicas and adjusting CPU, memory, and storage
  • Scheduling backups to S3-compatible storage and recovering to a point in time
  • Plugging metrics into an existing observability stack
  • Tuning engine parameters, storage classes, scheduling, and external access

From OpenEverest's own website, read 2026-10-24. How we build listings.

How to evaluate Data Warehouse & Lakehouse

This is how the CIOPages Research Team evaluates this category. It is not an assessment of OpenEverest. The category covers two kinds of product, so both frameworks are here. Most buyers need one of them.

Cloud Data Warehouse
25%
Query Performance & Concurrency
TPC-DS benchmarks, concurrent user support, query queuing, automatic scaling, sub-second response for dashboards
20%
Data Ingestion & Integration
Streaming ingestion (Kafka, Kinesis), batch loading, CDC support, native connectors, data sharing/marketplace
20%
Governance & Security
Column/row-level security, dynamic data masking, data lineage, access policies, audit logging, compliance certifications
15%
AI/ML Integration
Native ML runtimes (Python/Spark), feature store, vector search, LLM integration, model serving
10%
Cost Management
Compute/storage separation, auto-suspend, resource monitors, usage attribution, reserved capacity pricing
10%
Ecosystem & Tooling
BI tool compatibility, dbt/Airflow integration, Iceberg/Delta support, partner ecosystem, marketplace
Data Lakehouse Platforms
25%
Open Format & Catalog Interoperability
Native read AND write to Apache Iceberg and/or Delta Lake, Iceberg REST Catalog support, credential vending for external engines, cross-format bridges (Delta UniForm, Apache XTable), and whether grants and lineage travel with the table when another engine reads it
20%
Query Engine Performance & Concurrency
Vectorized execution (Photon, Arrow/Gandiva, native engines), caching and materialization (reflections, result cache, Warp Speed), high-concurrency BI behavior, autoscaling, and predictable performance on your own data — not vendor benchmarks
20%
Data Engineering, Streaming & AI/ML
Batch and streaming ingestion into open tables, incremental/CDC and upsert support, orchestration, ML lifecycle (feature store, training, model serving), notebook and Python/Spark depth, and native LLM/agentic and vector capabilities
15%
Governance, Security & Lineage
A unified catalog spanning tables, files, ML models and (increasingly) unstructured data; fine-grained RBAC/ABAC, row/column masking, data sharing, automated lineage, and consistent policy enforcement across every engine that touches the data
10%
Operational Simplicity & Table Maintenance
Automated compaction, clustering, snapshot expiry and orphan-file cleanup; serverless vs. cluster sizing; multi-cloud and hybrid/on-prem reach; admin and FinOps tooling; and how much table toil the team must own versus the platform absorbing it
10%
Cost Model & Consumption Control
Consumption unit (DBU, credit, capacity unit, bytes/slots), separation of storage and compute, idle-suspend and autoscaling guardrails, egress and cross-region exposure, workload isolation, and the FinOps tooling to attribute and cap spend

CIOPages put this listing together from public sources. It’s information, not an endorsement. How we build listings. Work here? Claim this listing or .

Quick Facts

openeverest.io
CategoryData & Analytics
SubcategoryData Warehouse & Lakehouse

We publish a detail only when we can point at the page it came from. Know when OpenEverest was founded or where it is based? Send it to us, free.

Keep reading

Buyer Guide
Cloud Data Warehouse
Job 01
Decide where AI goes in the business

The board wants an AI plan, and every function wants to go first.

Job 05
Modernize the estate

AI agents need clean data and systems they can call, and old systems block both.