TimescaleDB vs InfluxDB

Quick verdict

This page is for engineers choosing a store for time-stamped telemetry: building management system (BMS) points such as AHU supply-air temperature, chiller kW and zone CO2, utility meter readings every 1 to 60 seconds, or equipment data that a CAFM or CMMS uses to raise maintenance work orders. For the wider picture of what a time-series database is and how data flows from devices to dashboards, start with the time-series and IoT data hub.

TimescaleDB, maintained by Tiger Data (formerly Timescale), describes itself as a PostgreSQL extension for high-performance real-time analytics on time-series and event data. Its central object is the hypertable: a PostgreSQL table that is automatically partitioned by time into chunks. The latest release on GitHub when checked was 2.31.0 (20 October 2026). See the TimescaleDB guide for setup and design detail.

InfluxDB is InfluxData's time-series database. The current generation, InfluxDB 3, is written in Rust on Apache Arrow, DataFusion and Parquet; InfluxDB 3 Core is the open source release and InfluxDB 3 Enterprise the commercial self-managed one. The latest InfluxDB release on GitHub when checked was v3.12.1 (dated 14 October 2026 in the release list). Older InfluxDB 1.x and 2.x (with the Flux language) still exist on separate branches. See the InfluxDB guide.

Side by side

Key differences

Data model: relational tables versus tags and fields

In TimescaleDB a sensor reading is a row in an ordinary SQL table. You decide the columns, add foreign keys to an asset table, and create indexes as in any PostgreSQL schema. The hypertable splits the table into chunks by time (for example one day per chunk), and the planner only scans chunks that can match the query's time range. The current documentation creates a hypertable with CREATE TABLE ... WITH (tsdb.hypertable ...):

-- Illustrative DDL, not run here: from the Tiger Data documentation;
-- check every setting against the documentation for your version before use.
CREATE TABLE conditions (
   time        TIMESTAMPTZ      NOT NULL,
   location    TEXT             NOT NULL,
   device      TEXT             NOT NULL,
   temperature DOUBLE PRECISION NULL,
   humidity    DOUBLE PRECISION NULL
) WITH (
   tsdb.hypertable,
   tsdb.segmentby = 'device',
   tsdb.orderby   = 'time DESC'
);

InfluxDB 3 Core organises data into databases and tables, and each table has three kinds of column: time, tags and fields. Tags identify a series and are part of the table's primary key, which also sets the sort order of the Parquet files; the documentation advises that tags hold identifying information such as sensor_id or building_id, with all other data stored as fields. Tag columns cannot be changed once the table exists, so the tag design for a BMS (site, building, AHU, point) has to be settled early. Data is written as line protocol, a text format of table, tag set, field set and timestamp:

# Illustrative, not run here: line protocol format from the InfluxDB 3 Core documentation;
# check it against the documentation for your version before use.
ahu,site=hq,ahu_id=ahu-01 supply_air_temp=13.4,fan_speed_pct=62i 1793059200000000000

For a CAFM or CMMS team the practical question is where the reference data lives. With TimescaleDB, the asset register, the sensor-to-asset mapping and the open work orders can be in the same database and joined in one query. With InfluxDB, those usually stay in a relational database and either the identifiers are copied into tags or the two are combined in the application or a dashboard tool.

Query language and downsampling

TimescaleDB uses PostgreSQL SQL, so window functions, CTEs, joins and the whole PostgreSQL tool chain work unchanged. It adds hyperfunctions such as time_bucket, and continuous aggregates: materialized views that refresh incrementally in the background as data arrives or changes. A refresh policy keeps them current:

-- Illustrative configuration, not run here: from the Tiger Data documentation;
-- check every setting against the documentation for your version before use.
CREATE MATERIALIZED VIEW ahu_hourly
WITH (timescaledb.continuous) AS
SELECT time_bucket('1 hour', time) AS bucket, device,
       avg(temperature) AS avg_temp, max(temperature) AS max_temp
FROM conditions
GROUP BY bucket, device;

SELECT add_continuous_aggregate_policy('ahu_hourly',
  start_offset      => INTERVAL '1 month',
  end_offset        => INTERVAL '1 hour',
  schedule_interval => INTERVAL '1 hour');

InfluxDB 3 supports SQL through Apache DataFusion and keeps InfluxQL for compatibility with 1.x; Flux, the language of InfluxDB 2.x, is not part of InfluxDB 3. Time grouping uses DATE_BIN, and gap filling uses date_bin_gapfill with interpolate or locf. Downsampling and other scheduled transformations run in the processing engine, an embedded Python VM with a plugin library that includes a Downsampler plugin.

-- Illustrative, not run here: InfluxDB 3 SQL pattern from the InfluxDB 3 Core documentation;
-- check it against the documentation for your version before use.
SELECT DATE_BIN(INTERVAL '1 hour', time, '2026-01-01T00:00:00Z'::TIMESTAMP) AS time,
       ahu_id, avg(supply_air_temp) AS avg_temp
FROM ahu
WHERE time >= now() - INTERVAL '1 day'
GROUP BY 1, ahu_id
ORDER BY 1;

In our view, both SQL dialects are comfortable for analysts; the difference is that TimescaleDB SQL is full PostgreSQL with joins to any other table, while InfluxDB SQL is aimed at the time-series tables in that database.

Retention, compression and the data lifecycle

TimescaleDB retention works on whole chunks, not rows: add_retention_policy('conditions', drop_after => INTERVAL '6 months') drops chunks whose data is entirely older than the threshold, which avoids row-by-row DELETE. Combined with continuous aggregates, you can drop raw one-second data after a few months and keep hourly summaries for years. Older chunks can be converted to the hypercore columnstore; the vendor, Tiger Data, reports compression of up to 98% in the columnstore; that is the vendor's figure, not reproduced here, and it depends heavily on the data.

InfluxDB 3 Core sets a retention period per database. By default data does not expire; in Core the retention period can only be set when the database is created and cannot be changed afterwards, so changing it means creating a new database and migrating. The documentation also notes that retention is enforced at query time: expired points are filtered out of results even if they still exist in storage until they are removed. Persistence is Parquet on local disk or object storage, an open file format that other Parquet tools can read; storage cost depends on where you put those files.

Plan retention per signal, not per system. As an illustrative assumption, a BMS might keep raw 1 to 60 second readings for weeks or months, 15-minute or hourly aggregates for years for energy reporting, and alarm events for as long as the maintenance record needs them; choose your own periods from reporting, audit, contractual and diagnostic needs and from storage cost. See how to store IoT sensor data for sizing and schema patterns.

Performance: what actually differs

There is no neutral, like-for-like benchmark between the two that we can point to, and vendor benchmarks are designed by the vendor. What can be said from the documentation is architectural:

• Ingest. InfluxDB writes line protocol to a write-ahead log and persists Parquet files; TimescaleDB writes rows to PostgreSQL, so batching, COPY and connection pooling matter. Late or corrected readings are ordinary UPDATE or upsert operations in TimescaleDB, including on the columnstore according to its documentation.
• Recent-value queries. InfluxDB 3 has a Last Value Cache and a Distinct Value Cache. The vendor reports query response times under 10 ms for last-value queries and 30 ms for distinct metadata.
• Long-range analytics. Tiger Data describes new data as landing in the rowstore and converting to the columnstore, "where analytical queries read fewer bytes and run faster", and continuous aggregates as refreshed in the background; that is the vendor's description, not a measurement. InfluxDB documents Core as built for real-time monitoring and recent data, with historical query capability as an Enterprise addition.
• Cardinality. Many distinct tag combinations (as an illustrative scale, thousands of points per building across hundreds of buildings) affect both systems; model and test with realistic point counts.

The honest approach is a proof of concept with your own point list, write rate, late-data pattern and the five or six queries your dashboards and reports actually run. Our time-series database performance guide covers what to measure and how to keep such a test fair.

Licensing and editions

TimescaleDB. The repository's LICENSE file states that outside the tsl directory "source code in a given file is licensed under the Apache License Version 2.0, unless otherwise noted", that within it "source code in a given file is licensed under the Timescale License, unless otherwise noted", and that the built binaries are split the same way. Tiger Data's edition comparison lists the columnstore, continuous aggregates, retention policies and the job scheduler as Community Edition (TSL) features, not Apache 2 features. Tiger Data's licence page summarises the TSL as: "Roughly speaking, as long as you are not offering TimescaleDB as a hosted Database-as-a-Service, you can use all Community features for free." The TSL agreement (also reproduced on that page) is more detailed: its Internal Use grant covers use "solely for Your own internal business purposes in a manner that does not expose or give access to, directly or indirectly (e.g., via a wrapper), the Timescale Data Definition Interfaces or the Timescale Data Manipulation Interfaces" to anyone other than you, your employees and contractors; a separate grant with its own conditions covers Value Added Products or Services; and section 2.2 prohibits "using any TSL Licensed Software to provide time-sharing services or database-as-a-service services".

InfluxDB. The InfluxDB repository states: "The open source software we build is licensed under the permissive MIT or Apache 2 licenses at the user's choosing." The texts are in its LICENSE-MIT and LICENSE-APACHE files; both grant broad rights subject to conditions, such as keeping the copyright and permission notice (MIT) or meeting the redistribution conditions in section 4 (Apache 2.0). InfluxDB 3 Enterprise, which adds high availability, read replicas, multi-node clustering and long-range compaction, is used under a licence from InfluxData: its licence page lists Trial, At-Home and Commercial licences, describes the Commercial one as a "Commercial license with full access to InfluxDB 3 Enterprise capabilities" and says licences "are primarily based on the number of CPUs InfluxDB can use".

Our editorial view: the self-hosted TimescaleDB Community Edition includes more of the product's features than InfluxDB 3 Core does, but those features are under the source-available TSL rather than an open source licence; InfluxDB 3 Core is under permissive open source licences but is a single-node engine. Not legal advice: this section summarises and quotes the licence texts as published on 27 October 2026; read the licences for the versions you use and take legal advice for your situation.

Architecture: where each fits in an IoT data flow

Neither is a message broker. In a typical building or plant, devices and controllers publish over BACnet, Modbus or OPC UA gateways to an MQTT broker or Kafka (the streaming layer); a collector or consumer writes to the time-series database (the storage and query layer); and a warehouse or lakehouse may hold long-term history for cross-site analytics (the analytics layer). Telegraf is InfluxData's open source collector with MQTT and Kafka inputs; for TimescaleDB, any consumer that can write to PostgreSQL works, including Kafka Connect JDBC sinks and custom services.

Security follows each platform: TimescaleDB inherits PostgreSQL roles, row-level security, TLS and auditing extensions; InfluxDB 3 uses tokens for authentication, with finer-grained controls in Enterprise. For a CMMS that raises work orders from sensor thresholds, the rule engine usually lives in the application or the processing layer, not in the database.

Pricing and licensing

TimescaleDB self-hosted carries no licence fee: Tiger Data's edition page states that "TimescaleDB Community Edition is completely free if you manage your own service", subject to the TSL terms in the licensing section. Tiger Cloud bills compute hourly for the configured size and storage by average GB used. On 27 October 2026 its pricing page listed storage at USD 0.177 per GB-month on the Performance plan and USD 0.212 per GB-month on the Scale plan, and its FAQ states that "Storage is priced the same across all regions, but compute prices will vary depending on the region"; the page states that its "effective storage price" assumes 5x compression. The compute starting prices on the plan cards do not state a region, so we do not quote them. Add-ons such as HA replicas, tiered storage and production support cost extra, and there is a 30-day free trial. Tiger Data also sells TimescaleDB Enterprise for self-managed deployments on quote.

InfluxDB 3 Core: InfluxData's pricing page states that it "is open source and free to download and run with no license required". InfluxDB 3 Enterprise is custom-priced by CPU configuration, with a 30-day trial. Cloud Serverless is usage-based across four dimensions (data in, query count, storage and data out), with a free tier limited to 30-day retention; the pricing page lists unit prices for each dimension but does not state a region for them, so we do not quote them here. Cloud Dedicated is custom-priced by CPU, RAM and storage. Amazon Timestream for InfluxDB is billed by AWS at its consumption rates.

Unit prices are not comparable directly: one bills compute hours and stored GB, the other (Serverless) bills ingested MB, queries and GB-hours. Estimate both with your expected points, sample rate, retention and query count.

Pricing checked on the vendors' official pages on 27 October 2026. Prices change; confirm before buying.

Where each one leads

Limitations

When to choose each

When neither is right

• Volumes are modest (as an illustrative scale, a few hundred points at minute intervals): plain PostgreSQL with partitioning and a BRIN index may be enough; see TimescaleDB vs PostgreSQL.
• The data is infrastructure metrics scraped from exporters for alerting: a metrics system such as Prometheus fits better; see Prometheus vs Datadog.
• The main job is large-scale analytics across many sites and years: a columnar analytics engine or warehouse may suit better; see ClickHouse vs PostgreSQL and Snowflake vs Databricks.
• You are still choosing between several time-series engines: see the time-series database selection guide, which also covers QuestDB (QuestDB guide).

Final recommendation

Frequently asked questions

Sources

• TimescaleDB GitHub repository
• Tiger Data: software licensing (Apache 2.0 and TSL)
• Tiger Data: compare TimescaleDB editions
• Tiger Data: understand hypertables
• Tiger Data: create a hypertable (CREATE TABLE reference)
• Tiger Data: continuous aggregates
• Tiger Data: add_continuous_aggregate_policy
• Tiger Data: data retention
• Tiger Data: hypercore
• Tiger Data pricing
• InfluxDB GitHub repository (InfluxDB 3 Core)
• InfluxDB 3 Core documentation
• InfluxDB 3 Core: get started and data model
• InfluxDB 3 Core: line protocol reference
• InfluxDB 3 Core: query with SQL
• InfluxDB 3 Core: create a database (retention)
• InfluxDB 3 Core: data retention internals
• InfluxData pricing
• InfluxDB releases on GitHub
• TimescaleDB releases on GitHub
• TimescaleDB repository: LICENSE file
• TimescaleDB repository: Timescale License agreement
• InfluxDB repository: LICENSE-MIT
• InfluxDB repository: LICENSE-APACHE
• InfluxDB 3 Enterprise: manage your license

Checked October 2026.

How we research comparisons: our editorial method.