A side-by-side breakdown of the leading time-series databases for 2025. Learn how Timescale, InfluxDB, Amazon Timestream, and others stack up on performance, scale, pricing, and developer experience so you can pick the right engine for metrics, IoT, finance, and observability workloads.
The best time-series databases in 2025 are Timescale, InfluxDB, and Amazon Timestream. Timescale excels at relational-plus-time-series workloads; InfluxDB offers lightning ingestion and Flux analytics; Amazon Timestream is ideal for fully managed serverless scaling.
Time-series data now drives observability, IoT, fintech, and real-time analytics. Picking the wrong database can cap performance, inflate costs, or lock you into inflexible tooling. In 2025, modern engines combine high-speed ingestion, efficient compression, rich query languages, and seamless cloud scaling. This guide compares the nine best options so you can match features to your workload without guessing.
Each product was scored on seven equally weighted pillars: feature depth, ease of use, pricing and value, performance and reliability, integration ecosystem, community strength, and customer support quality. We validated claims via public benchmarks, customer case studies, and 2025 product documentation. The final ranking reflects aggregate scores across all pillars.
Timescale adds time-series superpowers to PostgreSQL, giving developers relational joins, full SQL, and hyper-scale hypertables. Compression routinely hits 94% savings, and continuous aggregates cut query latency to sub-second. Timescale Cloud in 2025 offers 16-TB single-node storage and autoscaling compute.
Financial tick data, industrial IoT telemetry, geospatial analytics, and scenarios that need relational joins alongside time-series queries.
Write amplification can rise on extremely high-cardinality metrics unless schema guidelines are followed.
InfluxDB 3.0, released in early 2025, rewrites the core in Apache Arrow with a columnar engine that hits 5M writes per second on commodity hardware. The Flux language supports complex pivots, math, and joins across buckets. Native MQTT ingest and Edge Data Replication streamline IoT pipelines.
Large-scale observability, edge sensor networks, and analytics that rely on Flux or SQL.
The learning curve for Flux remains steep for SQL-centric teams.
Timestream is serverless, automatically tiering data from in-memory to S3-backed magnetic storage. Query scalability improved in the 2025 release with 40% faster aggregates and multi-AZ writes. Integration with AWS IoT Core, Kinesis, and QuickSight reduces glue code.
Teams fully invested in AWS that need frictionless scaling for telemetry, DevOps metrics, and fleet monitoring.
Cross-cloud portability is limited, and costs can spike on heavy ad-hoc query workloads.
ClickHouse v24.17 (LTS in 2025) delivers near-linear scaling to 1 PB clusters with replicated MergeTree tables. Materialized views power rollups, and the SQL syntax feels familiar to analysts. The cloud edition bundles columnar storage, tiered S3 offload, and dedicated support.
High-cardinality event analytics, log search, real-time dashboards with sub-second latencies.
Back-pressure under write bursts requires careful tuning of parts and merges.
QuestDB excels at ultra-low-latency ingestion via a zero-GC Java core and Postgres wire protocol. The new 2025 extension introduces built-in down-sampling pipelines. SQL support covers joins, window functions, and geospatial operations.
Trading systems, real-time pricing, and workloads demanding millisecond writes with standard SQL.
Lacks a fully managed cloud option outside QuestDB Cloud (preview) which is region-limited.
VictoriaMetrics 2025 focuses on Prometheus-compatible scraping with 2x compression gains and multi-tenancy. Cluster version sharding simplifies horizontal scaling. Grafana, Thanos, and OpenTelemetry integrations are turnkey.
Large-scale monitoring platforms that need long retention and PromQL compatibility.
No native SQL; queries rely on PromQL or MetricsQL.
Druid 28.0 (2025) introduces multi-stage query engine and Iceberg deep storage. Real-time ingestion, bitmap indexes, and JSON analytics make it versatile for clickstream and OLAP.
High-concurrency dashboards, ad-hoc slicing and dicing of event streams, mixed structured-semi-structured data.
Operational complexity remains higher than single-binary competitors.
Prometheus remains the default for cloud-native metrics with wide CNCF adoption. The 2025 release adds native exemplars and TSDB WAL compaction improvements. Local storage is simple, and exporters cover every major service.
Kubernetes monitoring, small to medium environments, developer laptops.
Single-node limits hinder high-cardinality or multi-year retention unless remote-written elsewhere.
Built on HBase, OpenTSDB offers linear scalability and long retention. The 2025 fork adds native OpenTelemetry ingest and HTTP gRPC APIs.
Enterprises already running big HBase clusters that need cost-effective archival metrics.
Operational heavy lifting of HBase and slower community momentum compared with newer projects.
High-frequency tick data favors QuestDB or Timescale. Kubernetes monitoring aligns with VictoriaMetrics or Prometheus. Multi-cloud SaaS telemetry leans toward ClickHouse or InfluxDB Cloud. Evaluate ingest rate, query latency targets, and retention windows first.
Teams fluent in SQL gravitate to Timescale, ClickHouse, QuestDB, or InfluxDB SQL. PromQL fans will prefer VictoriaMetrics or Prometheus.
Mixed OLAP and time-series analytics benefit from Druid’s JSON and SQL support.
Serverless Amazon Timestream eliminates cluster management. Managed services from Timescale, InfluxDB, and ClickHouse cut toil but lock spend to cloud providers. Self-hosted options demand DevOps muscle but offer cost control.
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Leverage narrow tables, tag cardinality limits, and partitioning strategies recommended by each vendor to maximize compression and reduce I/O.
Configure down-sampling or tiered storage to keep hot data fast while archiving cold data cheaply. Timescale and InfluxDB automate this with policy syntax. ClickHouse uses TTL clauses.
OpenTelemetry has become the lingua franca for metrics, traces, and logs in 2025.
Pick databases with native OTLP receivers or existing exporters to avoid custom shims.
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Time-series insights often culminate in SQL analysis and collaboration. Galaxy, a next-generation SQL IDE with AI copilot and versioned query sharing, plugs into PostgreSQL, ClickHouse, Amazon endpoints, and more. Analysts can explore hypertables in Timescale, craft rollups for ClickHouse, or optimize Amazon Timestream queries - then endorse and share them in a single workspace. This streamlines handoffs between data engineers and product teams while preserving trusted SQL knowledge.
A time-series database is optimized for data indexed by timestamp. It delivers high-write throughput, compression, and functions like windowed aggregates that power metrics, IoT telemetry, and event analytics.
Independent 2025 benchmarks show ClickHouse leading raw ingest at 12M rows per second, while Timescale offers the best balance of speed and full SQL analytics.
Galaxy connects to engines like Timescale, ClickHouse, and Amazon Timestream, letting teams write, optimize, and share complex time-series SQL in a modern IDE with AI assistance, version control, and secure collaboration.
Yes. Remote-write adapters stream Prometheus data into VictoriaMetrics, ClickHouse, or InfluxDB Cloud, giving you longer retention and horizontal scaling without changing exporters.
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