CIOPages
For vendorsCorrections to your listing are free
Fix an error, free List a company, free Order a Spotlight listingAbout Spotlight listings
DirectoryData & AnalyticsData Warehouse & LakehouseGreptimeDB

GreptimeDB

About GreptimeDB

GreptimeDB runs on the user's own infrastructure. It uses a columnar engine with object storage as primary storage. Data is ingested through OpenTelemetry and Prometheus Remote Write, and queried with SQL and PromQL. Metrics, logs, and traces share one table model consisting of tags, timestamp, and fields. The core engine and protocol implementations are licensed under Apache-2.0.

Products

  • GreptimeDB Open Source: Core database engine that is free to run in the user's own environment.
  • GreptimeDB Enterprise: Adds scaling, availability, security, and operational capabilities for large deployments.

Used for

  • Ingesting data through OpenTelemetry, Prometheus Remote Write, Loki Push, and Elasticsearch Bulk.
  • Correlating signals in one SQL query when they carry common identifiers.
  • Storing raw events and deriving metrics on demand, or materializing them continuously with Flow.
  • Migrating ingestion one signal at a time without rebuilding collectors.
  • Serving recent data and long retention from the same engine without a separate analytics stack.

From GreptimeDB's own website, read 2026-10-23. 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 GreptimeDB. 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

greptime.com Pricing page
CategoryData & Analytics
SubcategoryData Warehouse & Lakehouse

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

Keep reading

Buyer Guide
Cloud Data Warehouse
Job 04
Control technology spend

AI is billed per use, so its cost grows with adoption while the rest of the budget is under review.

Toolkits
Frameworks and templates

Capability models, architecture templates and IT strategy tools to download.