Shyft Score
Directory quality rating
48/100
Quiet
Found in 1 source
Our take
The verdict on QuestDB
AI-generated
QuestDB is a high-performance open-source time series database with Grafana support, but lacks commercial backing.
Strengths
- High performance with low latency
- SQL support for time series
- Built-in Grafana support
Limitations
- No commercial support
- Pricing unknown
- No AI agent support
Best for: Data teams needing a fast, open-source time series database.
Try QuestDB's free tier to see if it fits your workflow.
See how QuestDB fits your stackBenefits
Why teams choose QuestDB
Query massive time series datasets in milliseconds instead of minutes
Reduce infrastructure costs with open-source licensing and efficient resource usage
Get real-time insights from streaming data without delays or bottlenecks
Scale from gigabytes to petabytes without performance degradation
About
What is QuestDB?
QuestDB is an open-source time series database. It offers real-time data ingestion and SQL support for time series queries. It provides low latency performance and a scalable architecture for large datasets. QuestDB includes built-in Grafana visualization support.
Key capabilities
Real-time data ingestion
SQL support for time series queries
High performance with low latency
Scalable architecture for large datasets
Built-in support for Grafana visualization
At a glance
Use cases
What you can do with QuestDB
Storing and analyzing time series data
Monitoring and visualizing real-time data
Building financial trading systems
Managing IoT sensor data
Best for
Data engineersDevOps teamsFinancial analysts
Pricing
QuestDB pricing
QuestDB starts at Free
Starting at Free
Ecosystem
Connected ecosystem
MCP servers, AI skills, and integrations that work with QuestDB
Integrations
GrafanaPrometheusApache KafkaInfluxDBAWS S3PostgreSQLMicrosoft Power BITableauZapier
FAQs
Frequently asked questions
Common questions about QuestDB and its capabilities
QuestDB is an open-source time series database ideal for B2B applications requiring real-time data ingestion and high-performance analytics. It's particularly suited for data engineers, DevOps teams, and financial analysts who need to process large datasets with low latency and visualize trends using SQL and Grafana.
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