Top 7 Time-Series Databases in 2026

For engineering teams choosing where to put metrics, sensor readings, and tick data. The category has split in two: purpose-built time-series engines on one side, general-purpose columnar databases that happen to be very good at time ordering on the other.

A time-series database stores measurements indexed by time — server metrics, factory sensor tags, financial ticks, telemetry from vehicles and satellites — and answers range queries over them fast. The leaders in 2026 are ClickHouse, InfluxDB 3, TimescaleDB, Prometheus, VictoriaMetrics, Apache Druid and QuestDB. Two changes matter this year. Purpose-built engines have moved to object storage and open file formats, decoupling storage cost from ingest rate. And the vendors serving industrial operations have started shipping self-managed commercial editions again, because regulated plants cannot send data to someone else's cloud. All pricing and feature details below are as of September 2026.

How we picked these

Selection criteria, applied in this order:

• Production maturity. The engine runs in known production deployments at meaningful scale, and has a release history rather than a launch announcement.
• Breadth of workload. Engines that handle both high-cardinality metrics and analytical queries over long history ranked above single-purpose tools.
• Deployment freedom. Self-hosting, air-gapped operation, and an open-source core that keeps your data readable if you stop paying all counted in favor.
• Operational cost at scale. Compression, object-storage tiering, and hardware efficiency, since time-series workloads fail on cost before they fail on features.
• Ecosystem. Query-language familiarity, ingestion protocol support, and integration with Grafana and OpenTelemetry, which also drives choices in LLM observability tooling.

Ranking is our analysis, not a benchmark result. Where a tool wins decisively for one job, that job is named in its "Best for" line.

Quick comparison

1. ClickHouse

ClickHouse is a column-oriented OLAP database released under Apache 2.0 that has become a default answer for time-series analytics, despite not being marketed as a time-series database. The reason is structural: it stores columns separately, sorts rows by a primary key that is usually a timestamp plus a few tags, and applies per-column compression codecs — including delta and double-delta encodings designed for monotonically increasing values. That layout makes scans over billions of rows cheap. ClickHouse also ships a dedicated TimeSeries table engine that stores values against timestamps and label sets, and can act as Prometheus remote-write and remote-read storage. Its own documentation marks that engine experimental, gated behind the allow_experimental_time_series_table setting. ClickHouse Cloud separates compute from storage and bills on usage.

Best for: analytical queries across months or years of dense history, especially when the same store also serves logs and events, a consolidation trend we covered in cloud data warehouse platforms.

Pros

• Apache 2.0 licensed, with no functional ceiling on the self-hosted build.
• Columnar layout with time-series-specific compression codecs (delta, double-delta, Gorilla) rather than generic block compression.
• One engine can serve metrics, logs and product analytics, which removes a system from the stack, and one fewer hop through your data integration pipeline.
• Managed ClickHouse Cloud separates compute from storage, so idle history is not billed as active capacity.

Cons

• Not a drop-in metrics backend: the purpose-built TimeSeries engine is still labelled experimental in ClickHouse's own docs.
• No native equivalent of Prometheus alerting or a metrics scrape pipeline — you supply those.
• Self-hosting demands real schema design work; primary key and partitioning mistakes are expensive to undo at scale.
• Cloud spend is fully usage-driven, so a runaway query or an unexpected ingest spike lands on the bill.

2. InfluxDB 3

InfluxDB is the most widely deployed purpose-built time-series database, and the version 3 line is a rebuild rather than an increment: the engine is written in Rust, stores data as Parquet in object storage, and queries it through Apache DataFusion with SQL alongside the older InfluxQL. InfluxData splits the product four ways. Core is the open-source single-node engine. Enterprise is self-managed with high availability, multi-node clustering, read replicas and long-range compaction, priced custom by CPU configuration with a 30-day trial. Cloud Serverless is multi-tenant and usage-billed — as of September 2026, $0.0025 per MB written, $0.012 per 100 query executions, $0.002 per GB-hour of storage and $0.09 per GB egress. Cloud Dedicated is single-tenant. There is also a fully managed AWS-native path, Amazon Timestream for InfluxDB, billed through your AWS account. A Python processing engine runs transformation and anomaly logic inside the database.

Best for: industrial and telemetry workloads that start on edge hardware and need a supported path to a central store.

Pros

• Object-storage architecture (S3, GCS, Azure Blob) with Parquet files, so retention is not capped by local disk.
• Genuinely wide deployment choice, including an AWS-managed option that draws on existing AWS commitments and EDP discounts.
• Telegraf, the companion collection agent, has 400+ input and output plugins and is one of the most widely deployed telemetry agents anywhere.
• InfluxData holds SOC 2 Type II, ISO 27001 and ISO 27018 certifications across the product line.

Cons

• The open-source Core edition is single-node with no high availability and no enterprise security features — production clustering is a paid tier.
• Enterprise and Cloud Dedicated are both "contact us" pricing, so budgeting requires a sales conversation.
• Three major versions with different storage engines and query languages means migration work and a fragmented body of community knowledge.
• InfluxData's sub-10ms last-value claim rests on its Last Value Cache; treat it as a vendor figure until you test your own query mix.

3. TimescaleDB

TimescaleDB from Tiger Data takes the opposite approach to a purpose-built engine: it is a PostgreSQL extension. Tables become hypertables, partitioned automatically by time and optionally by a space dimension, with columnar compression and continuous aggregates layered on top. Everything else is Postgres — the wire protocol, the SQL dialect, the drivers, the backup tooling, the extension ecosystem. In April 2026 the company announced TimescaleDB Enterprise, a commercially licensed self-managed edition aimed at manufacturing, energy, utilities and defense, with high-availability clustering and automatic failover, incremental backups with point-in-time recovery, an admin console, pre-configured Grafana dashboards, and optional Cloud Sync replication to the managed Tiger Cloud service. It is built to run air-gapped: provisioning, failover, backup and monitoring all work without internet connectivity. Tiger Data says pricing is based on compute capacity rather than data volume, and that data stays in standard PostgreSQL format, so dropping the subscription leaves you on the open-source stack rather than stranded.

Best for: teams with PostgreSQL skills who want time-series capability without adding a second database.

Pros

• Standard PostgreSQL storage format and SQL, which removes the usual proprietary-format lock-in argument.
• Continuous aggregates maintain rollups incrementally instead of recomputing them per query.
• Enterprise edition is designed for air-gapped operation, which most managed-first vendors cannot offer.
• Compute-based pricing avoids the per-tag licensing model that legacy industrial historians use.

Cons

• TimescaleDB Enterprise was announced in April 2026 as an early-access waitlist with general availability planned for later in the year — it is the newest product on this list.
• Inherits PostgreSQL's single-writer architecture; very high ingest rates need careful tuning or sharding.
• The community edition leaves HA, backups, upgrades and monitoring for you to assemble.
• Frequent rebranding — Timescale to Tiger Data, Timescale Cloud to Tiger Cloud — makes older documentation and community answers harder to follow.

4. Prometheus

Prometheus is the de facto standard for infrastructure and Kubernetes monitoring, a graduated CNCF project under Apache 2.0, and the origin of PromQL, whose syntax the rest of this category now has to support. It works on a pull model: the server scrapes HTTP endpoints on a schedule, stores samples in a local time-series database, and evaluates recording and alerting rules against them. Prometheus 3.0 shipped in November 2024 — the first major version since 2.0 in 2017 — bringing OTLP ingestion, a rewritten UI, UTF-8 metric names, and Remote Write 2.0, which carries metadata, exemplars, created timestamps and native histograms in one payload and uses string interning to cut both bandwidth and CPU. Native histograms, which replace hand-chosen bucket boundaries with exponentially spaced ones, remain experimental and off by default behind --enable-feature=native-histograms.

Best for: scraping and alerting on infrastructure and Kubernetes metrics, with long-term storage handed to something else.

Pros

• Apache 2.0, vendor-neutral, CNCF-graduated, with an exporter ecosystem covering nearly every common piece of infrastructure.
• Single binary with a local storage engine — a useful deployment runs in minutes.
• PromQL and the exposition format are the category's lingua franca, so skills and dashboards transfer.
• Remote Write 2.0 materially improves durability and efficiency when shipping to long-term storage.

Cons

• Local storage is explicitly not intended as durable long-term storage; retention beyond weeks means running a second system.
• No native clustering or horizontal scaling; scale comes from federation, sharding, or a downstream project.
• High-cardinality label sets degrade it badly, and the failure mode is memory exhaustion.
• Native histograms, arguably the most important recent feature, are still experimental and require an explicit flag.

5. VictoriaMetrics

VictoriaMetrics exists because Prometheus does not scale horizontally. It speaks the Prometheus remote-write protocol and PromQL, so it drops into an existing setup, then adds its own MetricsQL dialect, a cluster mode where storage and query layers scale independently, and considerably lower hardware consumption for the same series count. The open-source build is a small dependency-free binary configured by command-line flags, with instant snapshot backups. It now spans metrics, logs (VictoriaLogs) and traces (VictoriaTraces). The company reports over a billion Docker pulls, 19 million GitHub downloads and more than 17,000 stars, and publishes case studies from CERN's CMS collaboration, Grammarly, Adidas, MongoDB and Spotify R&D. Grammarly's engineering team is quoted on the vendor's site describing a roughly tenfold reduction in its AWS bill after a proof of concept — their figure, not an independent measurement. Downsampling, anomaly detection, multi-tenancy and long-term support sit in the commercial Enterprise edition, alongside a managed VictoriaMetrics Cloud.

Best for: replacing or backing a Prometheus deployment whose cardinality or storage cost has become the problem.

Pros

• Drop-in Prometheus compatibility, so queries, dashboards and scrape configs usually need no changes.
• Cluster version scales ingestion, storage and query independently, which Prometheus itself cannot do.
• Open-source single-node and cluster builds with no ingestion cap, deployed as one static binary.
• Documented adoption at research and consumer-scale organizations, with named public case studies.

Cons

• Downsampling and anomaly detection — the features that most reduce long-term cost — are Enterprise-only.
• Enterprise pricing is not published; it requires a sales conversation.
• MetricsQL's extensions to PromQL are a mild lock-in risk if queries drift away from standard syntax.
• Metrics-first design: the logs and traces products are newer and less proven than the metrics engine.

6. Apache Druid

Apache Druid is a distributed real-time analytics database built for interactive slice-and-dice queries on event streams, with built-in indexing services for Apache Kafka and Amazon Kinesis — the same stream processing pipelines many teams already run that ingest events as they arrive. It is Apache 2.0 and structurally unlike the others here: a cluster is composed of separate service types — Coordinator, Overlord, Broker, Router, Historical, Middle Manager and Peon — that you scale independently, backed by deep storage (typically S3 or HDFS), a metadata RDBMS and ZooKeeper. Data lives in segments that Historical services cache on local disk; you can also query segments that exist only in deep storage, trading latency for reach. Druid bootstraps from deep storage even if every data server is lost. Release 32.0 added Projection, which precomputes aggregations to speed queries. Imply, founded by Druid's creators, sells the managed Imply Polaris service and publishes much of the project's release documentation.

Best for: user-facing analytical dashboards where thousands of concurrent queries must return in under a second.

Pros

• Native streaming ingestion from Kafka and Kinesis with exactly-once semantics, not a batch loader bolted on.
• Deep-storage-backed durability: the cluster rebuilds from object storage after total data-server loss.
• Independent scaling per service type gives precise control over where you spend hardware.
• High query concurrency is a design goal, which suits embedding analytics in a product.

Cons

• The most operationally complex system on this list — six or seven service types plus ZooKeeper and a metadata database.
• External dependencies on ZooKeeper and an RDBMS add failure modes before you ingest a single row.
• Less suited to high-cardinality metrics monitoring than the Prometheus-lineage tools.
• The main commercial support path runs through a single vendor, Imply, which narrows your options if that relationship sours.

7. QuestDB

QuestDB is a low-latency time-series database under Apache 2.0, written largely in zero-GC Java with SIMD-accelerated query execution, and aimed squarely at workloads where ingest latency is the binding constraint — market data, trading systems, industrial telemetry. It accepts writes over the InfluxDB line protocol and speaks PostgreSQL wire protocol for queries, so existing clients and BI tools connect without a custom driver. SQL is extended with time-series-specific syntax, including SAMPLE BY for downsampling and ASOF JOIN for aligning series recorded at different instants, which is the operation financial workloads need most and which most SQL engines make painful. Recent releases moved storage toward open formats including Parquet, with tiered storage separating hot and cold data. The company describes ingest of millions of events per second on a single server; that is QuestDB's own figure. Enterprise adds high availability with automatic failover, SSO and role-based access control with audit logs, and SLA-backed support.

Best for: capital-markets and high-frequency telemetry workloads that need ASOF JOIN and predictable ingest latency.

Pros

• Apache 2.0 core with no ingestion caps or per-node fees in the open-source edition.
• ASOF JOIN and SAMPLE BY handle time-alignment and downsampling in plain SQL.
• PostgreSQL wire protocol and InfluxDB line protocol support mean existing tooling connects unchanged.
• Single-server performance is strong enough that many deployments avoid clustering entirely.

Cons

• Smallest community and ecosystem of the seven, with correspondingly fewer integrations and third-party answers.
• High availability, RBAC, SSO and audit logging are Enterprise-only, and Enterprise pricing is not published.
• Narrower analytical surface than ClickHouse or Druid — it is a time-series engine, not a general OLAP database.
• Largely single-node by design, so horizontal scale is a paid, newer capability rather than a mature one.

How to choose

If you already run PostgreSQL and your time-series volume is measured in billions rather than trillions of rows, TimescaleDB is the shortest path — you keep your drivers, your SQL and your operational habits.

If you are monitoring infrastructure, start with Prometheus because the ecosystem assumes it, then add VictoriaMetrics the moment cardinality or storage cost hurts. That sequence is cheaper than picking a scalable backend before you know your cardinality.

If the same data has to serve dashboards, ad-hoc analysis and logs, ClickHouse consolidates the stack. Accept that you will do schema design work that a purpose-built engine would have done for you.

If the workload is industrial — plants, grids, oilfields, satellites — and the data cannot leave the site, the realistic shortlist is InfluxDB 3 Enterprise and TimescaleDB Enterprise, both of which are built for self-managed and air-gapped operation.

If you are embedding analytics in a product and thousands of users will query concurrently with sub-second expectations, Druid is designed for exactly that, and you should budget for the operational complexity.

If ingest latency is the constraint and your joins are time-aligned rather than key-aligned, QuestDB solves a specific problem better than anything else here.

Frequently Asked Questions

What is a time-series database?

A time-series database stores measurements indexed by timestamp and optimizes for writing them in time order and querying them by time range. It differs from a general database in its storage layout, compression of monotonic values, automatic time-based partitioning, and query functions for downsampling, gap-filling and rate calculation.

Is ClickHouse a time-series database?

ClickHouse is a general-purpose columnar OLAP database, not a purpose-built time-series engine, but it is widely used for time-series analytics because its sorted columnar storage and delta compression codecs suit timestamped data. It also offers a dedicated TimeSeries table engine, which its documentation currently marks as experimental.

Can Prometheus store metrics long term?

Prometheus documents its local storage as unsuitable for durable long-term retention. Teams that need months or years of history send data onward using the remote write protocol to a system such as VictoriaMetrics, Thanos, Mimir or ClickHouse, which handles retention, downsampling and horizontal scale.

Which time-series database is cheapest to run?

Cost depends on cardinality and retention more than on list price. The fully open-source options — Prometheus, VictoriaMetrics, QuestDB, Druid and ClickHouse — have no licence fee, so spend is hardware. VictoriaMetrics is frequently chosen specifically to reduce hardware cost for a given series count.

Do I need a time-series database for IoT sensor data?

Not always, but it helps as volume grows. Sensor data is high-volume, append-only and queried by time window, which is exactly what these engines compress and index for. Industrial teams increasingly choose InfluxDB 3 Enterprise or TimescaleDB Enterprise because both support on-premises and air-gapped deployment.

Editor's note — sources

• InfluxDB pricing, editions, certifications and Cloud Serverless rates: InfluxDB pricing and InfluxDB 3 Enterprise
• TimescaleDB Enterprise launch details, April 20 2026: Tiger Data newsroom and TimescaleDB Enterprise
• Prometheus 3.0, Remote Write 2.0 and native histogram status: Announcing Prometheus 3.0 and Prometheus feature flags
• ClickHouse TimeSeries table engine and experimental status: ClickHouse documentation
• Druid architecture, services and deep storage: Apache Druid architecture; release 32.0 details via Imply
• VictoriaMetrics products, adoption figures and customer statements: victoriametrics.com
• QuestDB product, protocols and Enterprise features: questdb.com