A curated selection of managed timeseries database services designed to handle high-write ingestion, metric monitoring, and event streaming. This list covers solutions ranging from lightweight observability stacks to massive-scale IoT data platforms, helping developers choose the right infrastructure for monitoring, analytics, and real-time processing.
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A managed timeseries database service optimized for recording, processing, and analyzing high-value streaming data. It offers a scalable infrastructure with Flux query language support, making it ideal for DevOps monitoring and IoT applications without the overhead of self-hosting.
A massive-scale security data lake built on Google's infrastructure, designed to ingest and retain exabytes of security data. It provides powerful querying capabilities for threat detection and compliance, allowing teams to keep data for extended periods without managing underlying storage.
A fast, scalable, and serverless timeseries database service for IoT and operational applications that accesses millions of rows per second. It automatically manages data lifecycle and tiered storage, storing recent data in high-performance memory while archiving historical data to cost-effective storage.
A cloud-native database built on PostgreSQL, designed to handle massive amounts of timeseries data with full SQL support. It offers familiar relational database features alongside advanced timeseries capabilities like continuous aggregates and compression, bridging the gap between SQL and NoSQL.
A managed timeseries database service on Microsoft Azure that combines the power of PostgreSQL with timeseries extensions. It allows developers to leverage existing SQL skills for real-time analytics and IoT data management within the Azure ecosystem.
A software-defined observability platform that collects and analyzes logs, metrics, and traces in real-time. It uses AI to detect anomalies and provide actionable insights, helping engineering teams reduce mean time to resolution by correlating data from various sources.
A popular combination where Prometheus handles local scraping and alerting, while Thanos or Grafana Cloud provides long-term storage and global queryability. This stack is the industry standard for Kubernetes monitoring, offering flexibility and robust ecosystem integration.
A comprehensive monitoring and security platform for cloud-scale applications that integrates timeseries data with logs and traces. It offers out-of-the-box integrations for nearly every cloud service, providing deep visibility into application performance and infrastructure health.
An intelligent APM and observability platform that uses AI to automate discovery and root cause analysis of performance issues. It captures comprehensive metrics and traces with minimal instrumentation, helping enterprises maintain high availability and user experience.
A full-stack observability platform that helps teams build better software faster by collecting data from applications, infrastructure, and users. It offers flexible pricing models and powerful querying tools to analyze timeseries data across diverse tech stacks.
A managed SaaS version of the Grafana ecosystem, including Loki for logs, Mimir for metrics, and Tempo for traces. It allows developers to visualize timeseries data using familiar dashboards while offloading the operational burden of maintaining backend storage components.
A fast, scalable distributed timeseries database built on Cassandra, designed for processing high volumes of metric data. It offers simple API access and integrates well with existing big data ecosystems, making it suitable for large-scale monitoring needs.
A fast, cost-effective open source monitoring solution and time series database compatible with Prometheus and Grafana. Its cloud offering provides managed scaling and high availability, making it a strong alternative for teams seeking efficiency and lower storage costs.
An open source relational timeseries database optimized for high-performance ingestion and complex SQL queries. It combines the speed of timeseries databases with the usability of SQL, allowing developers to run analytical queries on streaming data with low latency.
A distributed, scalable timeseries database built on top of HBase, designed to handle millions of data points per second. It is highly regarded for its stability in large-scale deployments, particularly within Hadoop ecosystems for big data analytics.
A high-performance real-time analytics database designed for fast analysis on large-scale data sets. It excels at handling interactive queries and ad-hoc analysis, making it ideal for use cases requiring low-latency access to timeseries data.
The managed SaaS offering of QuestDB, providing a fully hosted relational timeseries database. It simplifies deployment and scaling for development teams, offering automatic backups and high availability without the complexity of managing on-premise infrastructure.
An open source alternative to DataDog that provides application performance monitoring, logs, and distributed tracing. It uses OpenTelemetry for instrumentation, giving teams full control over their observability data stack while maintaining a modern, user-friendly interface.
While not exclusively a timeseries database, TiDB offers HTAP capabilities that allow real-time analytics on operational data. Its vectorized execution engine makes it suitable for running complex timeseries queries alongside transactional workloads in a single system.
A distributed search and analytics engine that can store and analyze timeseries data for log management and metrics. It is widely used in observability stacks for its powerful search capabilities and seamless integration with Kibana for data visualization.