Which Time Series Engine Wins on Compression and Queries?
IoT and monitoring data demand databases that store billions of data points efficiently. We compare 6 time-series databases on ingest speed, compression, and query language.
At MG Software we recommend TimescaleDB for most time-series use cases. Its full SQL compatibility and seamless integration with PostgreSQL makes it possible to combine time-series data with existing application data without managing a separate database. For dedicated monitoring stacks we use Prometheus with Grafana, and for long-term metrics storage we choose VictoriaMetrics for its superior compression.
Time-series databases have become essential infrastructure in 2026 for any organization handling large volumes of timestamped data. From IoT sensors streaming millions of data points per minute in smart manufacturing and logistics, to observability platforms tracking every request across distributed microservices, the need for specialized storage has never been greater. Financial institutions rely on time-series databases to store tick-level market data with sub-millisecond precision, while devops teams use them to power proactive alerting on billions of daily metrics. Traditional relational databases simply cannot keep up with the write throughput, compression ratios, and time-based query patterns these workloads demand. In this comprehensive guide we evaluate six leading time-series databases on ingestion speed, compression efficiency, query language, scalability, high availability, and cost, so you can make an informed decision for your specific use case.
How did we select these tools?
Each time-series database was tested with datasets from 10 million to 1 billion data points, measuring ingestion speed, aggregation query times, compression ratios, retention policies, and the learning curve of the query language.
How do we evaluate these tools?
- Write performance and ingestion speed at high volumes of millions of data points per second
- Query language and flexibility for time-series analysis, aggregations and downsampling
- Compression efficiency and storage costs for datasets ranging from hundreds of terabytes to petabytes
- Integration with monitoring and visualization ecosystems like Grafana, Datadog and OpenTelemetry
- High availability, clustering and automatic failover capabilities for production environments
- Cost per million data points at scale, including license model and cloud pricing tiers
1. InfluxDB
The most popular purpose-built time-series database, now in version 3.x with a completely rewritten engine based on Apache Arrow and DataFusion. InfluxDB 3 supports standard SQL alongside InfluxQL, significantly lowering the learning curve compared to the now-deprecated Flux language. The free tier of InfluxDB Cloud provides 5 GB of storage with unlimited write speed, while paid plans start at approximately $0.10 per GB of write volume.
Pros
- +Purpose-built for time-series with ingestion speeds exceeding 1 million points per second
- +InfluxDB 3 supports standard SQL, making the learning curve much lower than with Flux
- +Extensive integrations with Telegraf (300+ plugins), Grafana and Kapacitor for alerting pipelines
- +Managed cloud option with free tier and open-source self-hosted version under MIT license
- +Built-in support for downsampling and automatic retention policies per bucket
Cons
- -Migration from InfluxDB 2.x to 3.x requires schema adjustments and tooling updates
- -Cloud version can become expensive above 100 GB daily write volume
- -Clustering functionality and high availability only available in enterprise edition
- -The ecosystem around Flux scripts is no longer actively maintained
2. TimescaleDB
PostgreSQL extension that adds time-series superpowers to your existing Postgres database. TimescaleDB 2.x combines the familiar SQL ecosystem with hypertables, automatic partitioning, and continuous aggregates. The Timescale Cloud managed service starts at $0.07 per vCPU-hour. Because it is standard PostgreSQL under the hood, every existing Postgres tool, ORM and driver works out of the box, keeping the adoption barrier low for development teams.
Pros
- +Fully SQL-compatible, so there is no new query language to learn
- +Seamlessly combines with regular PostgreSQL tables in the same database instance
- +Excellent compression (up to 95%) via columnar chunks and automatic partitioning
- +Continuous aggregates for real-time dashboards without manual materialized views
- +All PostgreSQL extensions (PostGIS, pgvector) are directly usable alongside time-series data
Cons
- -Performance at extremely high ingestion rates (above 2 million points per second) is lower than InfluxDB
- -Horizontal scaling across multiple nodes requires a paid enterprise or cloud license
- -Higher operational complexity than fully managed alternatives like InfluxDB Cloud
- -Retention policies require manual configuration of data retention per hypertable
3. QuestDB
High-performance time-series database written in Java and C++ with a focus on extremely fast ingestion via the ILP protocol (InfluxDB Line Protocol). QuestDB 8.x supports SQL with time-series extensions and includes a built-in web console. The open-source version is free and runs as a single Docker container. QuestDB Cloud is available as a managed service with pay-per-use pricing starting at $0.06 per GB of storage per month.
Pros
- +Extremely fast ingestion: benchmarks show over 4 million rows per second on a single node
- +SQL interface with time-series-specific functions like SAMPLE BY and LATEST ON
- +Built-in web console for ad-hoc queries and quick data exploration
- +Open-source under Apache 2.0 with simple single-binary deployment
- +Very low latency for aggregation queries thanks to memory-mapped file I/O
Cons
- -Smaller ecosystem and community than InfluxDB or TimescaleDB
- -More limited functionality for non-time-series workloads and joins
- -Managed cloud version is relatively new and still lacks some enterprise features
- -No built-in replication in the open-source version for high availability
4. ClickHouse
Column-oriented analytical database originally developed by Yandex and now maintained by ClickHouse Inc. ClickHouse excels at real-time analytics over large datasets and is increasingly adopted for time-series workloads thanks to built-in support for time-series functions. ClickHouse Cloud offers serverless pricing starting at $0.10 per GB of ingested data. Compression ratios of 10:1 or higher make it cost-effective for long-term storage of historical metrics and events.
Pros
- +Lightning-fast analytical queries on billions of rows with sub-second response times
- +Excellent compression (10:1 to 40:1) through column-oriented storage and specialized codecs
- +SQL-compatible with powerful aggregation functions and materialized views for real-time
- +Active open-source community and ClickHouse Cloud as a fully managed option
- +Horizontally scalable with built-in sharding and replication across multiple nodes
Cons
- -Not specifically designed for time-series, requiring careful schema design and partition strategies
- -Complex configuration for high availability in self-hosted environments
- -Point queries on individual rows are significantly slower than in row-oriented databases
- -Learning curve for optimally configuring MergeTree engines and materialized views
5. Prometheus
The standard monitoring database in the cloud-native ecosystem, part of the CNCF and the second project to achieve "graduated" status after Kubernetes. Prometheus 2.x uses a pull-based model to scrape metrics and offers PromQL as a powerful query language for alerting and dashboards. It is completely free and open-source. For long-term storage, Prometheus is commonly paired with Thanos, Cortex or Grafana Mimir as a remote write backend.
Pros
- +De facto standard for Kubernetes monitoring with native service discovery
- +PromQL as an expressive query language for metrics, alerting and recording rules
- +Pull-based model makes service discovery and dynamic target management straightforward
- +Seamless Grafana integration for dashboards and the entire CNCF observability ecosystem
- +Completely free and open-source with no license costs, even for production use
Cons
- -Does not scale horizontally without external solutions like Thanos, Cortex or Grafana Mimir
- -Only suited for numeric metrics, with no support for logs, traces or events
- -Local storage is not durable and limited to weeks of retention on a single node
- -High memory usage with many active time-series (high cardinality) can cause out-of-memory crashes
6. VictoriaMetrics
High-performance open-source time-series database and monitoring solution that is fully compatible with Prometheus and Grafana. VictoriaMetrics provides up to 10x better compression than Prometheus and handles millions of data points per second on modest hardware. The single-node version is free under Apache 2.0, while the cluster version enables horizontal scaling. VictoriaMetrics Cloud offers a managed service with pricing based on active time-series count and ingestion volume.
Pros
- +Up to 10x better compression than Prometheus, drastically reducing storage costs
- +Drop-in replacement for Prometheus with full PromQL and remote write compatibility
- +Excellent performance on modest hardware: 1 million data points per second per core
- +Cluster version provides horizontal scaling and high availability out of the box
- +MetricsQL extends PromQL with additional functions for rollup, range and label transformations
Cons
- -Smaller community than Prometheus, although growing rapidly in 2026
- -Enterprise features like downsampling and anomaly detection require a paid license
- -Documentation is less extensive than that of Prometheus or InfluxDB
- -No built-in alerting; requires a separate tool such as vmalert or Grafana Alerting
Which tool does MG Software recommend?
At MG Software we recommend TimescaleDB for most time-series use cases. Its full SQL compatibility and seamless integration with PostgreSQL makes it possible to combine time-series data with existing application data without managing a separate database. For dedicated monitoring stacks we use Prometheus with Grafana, and for long-term metrics storage we choose VictoriaMetrics for its superior compression.
How MG Software can help
MG Software helps organizations choose, implement and optimize time-series databases that match their specific data volumes and budgets. Our team has hands-on experience setting up complete monitoring stacks based on Prometheus, Grafana and VictoriaMetrics, as well as integrating TimescaleDB into existing PostgreSQL environments. We design retention strategies that minimize storage costs without losing valuable historical data, and build dashboards that give your team real-time insight into application performance, infrastructure load and IoT sensor data. From architecture consulting to production deployment and ongoing maintenance, we guide you through the entire process.
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