Best Time-Series Database in 2026: 9 Engines Ranked

There is no single best time-series database. For most teams, the answer is TimescaleDB if you want to stay in PostgreSQL, or ClickHouse if you are running analytical queries over billions of rows. QuestDB leads on raw ingestion speed, InfluxDB owns metrics and observability, and Prometheus is the default for Kubernetes monitoring.

Which one fits depends on your workload: how fast data arrives, whether your queries filter by time or join against business data, how much history you keep, and whether you would rather run the database yourself or hand it to a managed service.

This guide ranks the nine time-series databases worth considering in 2026, ordered by DB-Engines popularity ranking, with a straight answer on where each one actually fits. One disclosure up front: we run two of these nine as managed services at Selfhost.dev, so we know the operational side well. We will still tell you when QuestDB or InfluxDB is the better call.

What is a time-series database?

A time-series database (TSDB) is a database optimized to store, compress, and query data points indexed by time, treating the timestamp as the primary axis rather than just another column. Where a general-purpose database tracks current state, a TSDB is built for an append-only stream of measurements and events that rarely change once written.

Time-series data comes in two shapes:

• Metrics (regular): readings at fixed intervals, like CPU usage sampled every 10 seconds.
• Events (irregular): points generated when something happens, like a trade, an error, or a click.

What separates a TSDB from a plain relational table is a handful of features built for that data:

• High-throughput ingestion for sustained, high-volume writes.
• Columnar, type-aware compression (delta-of-delta timestamps, XOR-encoded floats) that often cuts storage by 90% or more, depending on the data and codec.
• Time-based partitioning that splits data into time chunks so scans and deletes stay fast.
• Retention and downsampling that expire or roll up old data automatically.
• Temporal query functions for time-bucketing, moving averages, and gap filling.

These traits are why time-series databases anchor DevOps observability, IoT and sensor telemetry, and financial market data.

Do you actually need a dedicated time-series database?

You need a dedicated time-series database when time is the axis you query on and the write, retention, or storage load has outgrown what a general-purpose table handles comfortably. Below that point, plain PostgreSQL goes surprisingly far.

Reach for a TSDB, or time-series extensions, once one of these becomes a bottleneck:

• Sustained, high-rate ingestion your write path struggles to keep up with.
• Retention and downsampling you are managing with cron jobs and manual deletes.
• Time-based partitioning you are hand-rolling to keep queries fast.
• Storage cost that compression would materially cut.
• The same time-window aggregations recomputed over and over on raw data.

There is no universal row-count threshold here. Row width, indexing, partitioning, write rate, retention, VACUUM behavior, and hardware all move the line, so treat the bottleneck, not a magic number, as the signal.

Many teams miss the middle path: extend PostgreSQL rather than migrate off it. TimescaleDB is a Postgres extension that adds hypertables (automatic time-based partitioning), columnar compression, and continuous aggregates, giving you time-series performance without leaving SQL. If you already run managed PostgreSQL enabling TimescaleDB is a smaller step than adopting a separate database.

When vanilla PostgreSQL stops keeping up with the workload, the nine engines below are the field worth comparing.

Best time-series database in 2026: the 9 engines compared

For most teams, the shortlist narrows to five engines: InfluxDB, TimescaleDB, QuestDB, Prometheus, and ClickHouse. The table below compares all nine worth a look in 2026, ordered by DB-Engines popularity ranking, a composite popularity score rather than a direct adoption count, so the order is not ours to spin. The final column is the one most comparisons leave out: whether you can run the engine as a managed service, and what a small production instance costs.

Prices appear only where we can state them from our own service. Competitor managed pricing shifts with configuration, so we name the offering rather than guess a number.

The 9 best time-series databases, reviewed

InfluxDB

It is the most widely adopted purpose-built time-series database and ranks first on DB-Engines. Version 3 is a Rust rewrite on Apache Arrow and Parquet, with SQL alongside InfluxQL and the Telegraf agent’s 300+ integrations. Best for metrics and observability.

Watch out: v3 is still maturing, high availability and clustering sit in the commercial Enterprise tier, and the Flux language is not supported in v3 (SQL and InfluxQL are).

Prometheus

It is the cloud-native monitoring standard, scraping metrics from Kubernetes and alerting through PromQL. If your data is infrastructure telemetry read through dashboards and alerts, this is the default.

Watch out: it is single-node by design, not built for long-term storage, and it does one job rather than general time-series work.

Kdb+

It is the entrenched choice for high-frequency trading, with in-memory columnar speed that is hard to match on tick workloads. Best for capital-markets teams already invested in its q language.

Watch out: it is proprietary, q has a steep learning curve, and licensing is expensive.

TimescaleDB

It is the open-source PostgreSQL time-series extension, stewarded by Tiger Data (formerly Timescale; its managed cloud is Tiger Cloud). It adds hypertables, continuous aggregates, and columnar compression to Postgres, so you keep full SQL, joins, and the whole ecosystem. Best for teams who want time-series performance without leaving Postgres.

Watch out: it scales out through read replicas rather than sharding, so it is not built for extreme unbatched ingestion. It is also available managed on Selfhost.dev.

QuestDB

It is built for raw ingestion speed and pairs SQL with real time-series operators like ASOF, WINDOW, and HORIZON joins. QuestDB’s own published TSBS benchmarks report multi-million-row-per-second ingestion, though real-world numbers depend on schema and hardware. Cold data tiers to open Parquet. Best for financial ticks, high-frequency IoT, and write-heavy workloads.

Watch out: a smaller ecosystem and a newer managed cloud than the incumbents.

Apache Druid

It is designed for real-time streaming analytics, ingesting from Kafka and serving sub-second aggregations on fresh data. Best for interactive dashboards over event streams.

Watch out: it runs several coordinated node types, so operational complexity is high, and joins are limited next to SQL-native engines.

TDengine

It is purpose-built for industrial IoT and edge-to-cloud deployments, with native MQTT support and, unusually, free clustering in its open-source core. Best for device fleets and sensor historians.

Watch out: its community is stronger in Asia-Pacific than the West, and the AGPL-3.0 core matters if you plan to embed or resell it.

VictoriaMetrics

It is a fast, resource-efficient store for Prometheus-style metrics, often run as long-term storage behind Prometheus, with free horizontal clustering and strong compression. Best for large monitoring pipelines.

Watch out: it is metrics-shaped and PromQL-first, not a general-purpose engine for events or business data.

ClickHouse

It is a columnar OLAP database rather than a purpose-built TSDB, but its compression and scan speed make it strong for large-scale analytical queries over time-stamped events and logs; we go deeper in our real-time analytics database guide. Best for billions of rows of append-heavy data.

Watch out: it favors batched inserts over single-row updates, and self-hosting carries real operational weight. It is available as managed ClickHouse on Selfhost.dev.

Best time-series database by use case

The best time-series database depends on your workload, and the mapping is fairly settled. IoT fleets lean toward TDengine or TimescaleDB, observability toward Prometheus and VictoriaMetrics, financial ticks toward QuestDB or Kdb+, large-scale analytics toward ClickHouse, and teams already on Postgres toward TimescaleDB.

One shift matters for AWS teams: Amazon Timestream for LiveAnalytics stopped accepting new customers on June 20, 2025, and AWS now points new deployments to Amazon Timestream for InfluxDB. If you were planning on the original Timestream, InfluxDB or a managed Postgres and TimescaleDB setup are the practical replacements.

No single row here is a mistake. The point is to match the engine to the shape of your data rather than to a leaderboard.

How to choose the best time-series database

Choose by the question that eliminates the most options first, then confirm the rest:

• Already on PostgreSQL and want to stay in SQL? TimescaleDB.
• Observability or logs at large scale? ClickHouse, or Prometheus and VictoriaMetrics for pure metrics.
• Raw ingestion above everything? QuestDB.
• Kubernetes monitoring? Prometheus.
• Metrics and IoT with a wide plugin ecosystem? InfluxDB.

Those shortcuts get you close. A mature choice weighs six dimensions together:

• Ingestion rate: Sustained writes per second, and whether they arrive batched or one row at a time.
• Query pattern: Point lookups, wide time-range scans, or high-concurrency dashboard reads.
• Cardinality: How many unique series (tag or label combinations) you track. High cardinality is where monitoring-first engines have historically struggled and columnar engines tend to hold up better, and it often decides more than raw throughput.
• Retention: How long you keep data, and whether downsampling and expiry must run automatically.
• Joins: Whether you need to join time-series against business data, which favors SQL-native engines.
• Operational model: Self-hosted or managed, and who owns HA, backups, and upgrades.

Two dimensions are hard to reverse: the query language shapes every query your team writes for years, and native temporal joins like ASOF are painful to emulate if an engine lacks them. The operational model is the one most shortlists skip, and it is where the real cost lives.

Managed vs self-hosted: The cost the shortlists skip

The real cost of a time-series database is rarely the license, which for most of these engines is free. It is the operational surface area, and time-series workloads have an unusually large one.

A system ingesting continuously, around the clock, generates work a quiet transactional database does not. We cover the general version of this tradeoff in managed vs self-hosted databases; for time-series specifically, it shows up as:

• Backups and point-in-time recovery over data that never stops arriving.
• Retention and downsampling that must run reliably, or storage grows without bound.
• Replication and failover for data you often cannot reconstruct if a node dies.
• Storage tiering as hot data ages into cheaper cold storage.
• Capacity planning against a write rate that generally only goes up.

Self-hosted, all of that is yours to build and operate. A managed service takes on most of it, though exactly what is covered varies by provider and plan, so check the specifics rather than assume.

It also decides which pricing models work. A time-series workload ingests around the clock by definition, so it never earns the idle discount consumption pricing is built to give: the same ClickHouse Cloud Basic service costs $66.52 a month at six active hours a day and $186.27 running 24/7, and continuous ingestion only ever lands on the second figure.

The same logic catches the ingest path. ClickPipes Postgres CDC bills $0.20 per GB for continuous replication, and a workload defined by never stopping never stops paying that meter. It is free on ClickHouse’s own managed Postgres today, but only for the duration of the beta, and they have not said what happens after. We priced the CDC line on a ClickHouse Managed Postgres bill alongside everything else on it.

Selfhost.dev is one option here. Managed TimescaleDB starts at about $5/mo and managed ClickHouse at $56/mo for a small production instance, against roughly $173/mo for a comparable setup on ClickHouse Cloud. The database runs in your own AWS account through BYOC, and billing is prepaid and pauses to $0 when a resource stops, so a runaway bill is not possible. You can drive the whole thing with 150+ MCP tools from your editor.

See the pay-as-you-go cost for your workload, or deploy managed TimescaleDB free with a starter credit.

The bottom line

The best time-series database depends on your workload. Stay in Postgres with TimescaleDB, reach for ClickHouse when analytics run to billions of rows, and pick QuestDB when raw ingestion is the constraint. Each of the nine here wins something real, and the honest answer is to match the engine to your data.

The decision you actually live with is whether you run it yourself or let someone manage it, and whether the bill can surprise you. If you would rather not own high availability, backups, and upgrades, managed TimescaleDB on Selfhost.dev runs it for you on prepaid, pause-to-$0 pricing.

Deploy managed TimescaleDB free, or price it pay-as-you-go first.

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