InfluxDB vs Prometheus vs TimescaleDB: Time Series Database Comparison

Servers, containers, IoT sensors, trading platforms and applications all produce data that arrives as a stream of timestamped values. Ordinary databases can store it, but they are not designed for the write volume, time-based queries and automatic expiry that this data needs. Time series databases (TSDBs) are.

Three names come up constantly: InfluxDB, Prometheus and TimescaleDB. They overlap, but they were built with different goals. This guide compares them across data model, querying, scaling, alerting and operations, then helps you pick one.

Table of Contents

1. What Is a Time Series Database?
2. The Three Contenders in Brief
3. Architecture: Push vs Pull vs SQL Extension
4. Side-by-Side Comparison
5. Query Languages Compared
6. Scaling, Retention and High Availability
7. Ecosystem and Integrations
8. Which One Should You Choose?
9. Can You Combine Them?
10. Common Selection Mistakes
11. FAQ
12. Conclusion

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1. What Is a Time Series Database?

A time series is a sequence of measurements ordered by time: CPU usage every 10 seconds, temperature every minute, orders per hour. A TSDB is optimized for this workload:

• High write throughput with mostly append-only data.
• Time-based queries such as “average over the last 24 hours, grouped per 5 minutes”.
• Compression, because neighbouring values are similar.
• Retention and downsampling, so old detail is dropped or summarized automatically.

Two ideas appear in every TSDB: the series (a unique combination of metric name and labels or tags, for example cpu{host="web01"}) and cardinality (the number of unique series). High cardinality is the most common cause of TSDB performance trouble, and each product handles it differently.

2. The Three Contenders in Brief

InfluxDB: the purpose-built TSDB

InfluxDB is a dedicated time series database from InfluxData. Clients push data to it using line protocol over HTTP. InfluxDB 2.x uses the Flux and InfluxQL languages and includes a built-in web UI, buckets and tasks. InfluxDB 3 (Core and Enterprise) is a newer engine that stores data in Apache Arrow and Parquet format and adds SQL support. It fits general time series storage well: infrastructure metrics, IoT, sensor and event data.

Prometheus: the monitoring system

Prometheus is a CNCF graduated project designed for monitoring and alerting, not general data storage. It pulls (scrapes) metrics from HTTP endpoints exposed by applications and exporters, stores them in a local time series store, and evaluates alert rules with PromQL. Together with Alertmanager it forms the standard monitoring stack in Kubernetes environments.

TimescaleDB: time series inside PostgreSQL

TimescaleDB is an extension for PostgreSQL. It turns ordinary tables into hypertables that are automatically partitioned by time, and adds compression, continuous aggregates and retention policies. Because it is still PostgreSQL, you keep full SQL, joins, transactions, indexes and the entire PostgreSQL tool ecosystem. It suits teams that want time series data next to relational data.

3. Architecture: Push vs Pull vs SQL Extension

 INFLUXDB (push)                 PROMETHEUS (pull)               TIMESCALEDB (SQL)
 ─────────────────               ─────────────────               ─────────────────
  App / Telegraf                  App / exporter                  App / ETL / Telegraf
       │                               ▲                               │
       │ HTTP write                    │ scrape /metrics               │ INSERT / COPY
       ▼                               │ every N seconds               ▼
  ┌───────────┐                   ┌────┴──────┐                  ┌──────────────┐
  │ InfluxDB  │                   │Prometheus │                  │  PostgreSQL  │
  │  buckets  │                   │ local TSDB│                  │  + Timescale │
  └─────┬─────┘                   └────┬──────┘                  │  hypertables │
        │ Flux / SQL /                 │ PromQL                  └──────┬───────┘
        │ InfluxQL                     ▼                                │ SQL
        ▼                         Alertmanager                          ▼
     Grafana                      + Grafana                          Grafana / BI /
                                                                     any SQL client

The push versus pull difference matters in practice. Pull makes it obvious when a target is down (the scrape fails), and it is easy to reason about in Kubernetes with service discovery. Push suits short-lived jobs, devices behind firewalls and any source that cannot expose an endpoint.

4. Side-by-Side Comparison

Core characteristics

Feature	InfluxDB	Prometheus	TimescaleDB
Primary purpose	General time series storage	Monitoring and alerting	Time series on PostgreSQL
Data collection	Push (line protocol, Telegraf)	Pull (scrape); remote write to receive	Push via SQL, COPY, Telegraf, ETL
Data model	Measurements, tags, fields	Metrics with labels, float samples	Relational tables (hypertables)
Query language	Flux and InfluxQL (v2); SQL and InfluxQL (v3)	PromQL	SQL
Text and string data	Supported as fields	Not designed for it	Full PostgreSQL types
Joins with other data	Limited	No	Yes, native SQL joins
Built-in UI	Yes (v2)	Basic expression browser	No (use pgAdmin, Grafana, etc.)
Built-in alerting	Tasks and checks (v2)	Alert rules + Alertmanager	No (use Grafana alerting or external tools)
Licence	Open source core, commercial editions	Apache 2.0	Apache 2.0 core; some features under the Timescale License

Operations and scale

Aspect	InfluxDB	Prometheus	TimescaleDB
Default port	8086 (v2), 8181 (v3 Core)	9090	5432 (PostgreSQL)
Storage	TSM (v2), Parquet on object store (v3)	Local disk TSDB	PostgreSQL storage with chunking and compression
Clustering	Enterprise editions	Single node by design; scale via federation or remote storage	PostgreSQL replication; multi-node options vary by version
Long-term storage	Native, with retention per bucket	Local retention is limited; use remote write to long-term stores	Native, with compression and tiering options
Cardinality tolerance	Moderate in v2; improved in v3	Sensitive; high cardinality quickly uses memory	Handles it as ordinary rows and indexes, still needs design care
Operational skill	Moderate	Low for basic use, higher at scale	Requires PostgreSQL know-how

Read “scaling” claims as rules of thumb. Real limits depend on series count, write rate, query patterns and hardware, so benchmark with your own workload.

5. Query Languages Compared

The same question, “average CPU usage per host over the last hour, in 5 minute buckets”, looks very different in each system.

PromQL (Prometheus):

avg by (instance) (
  rate(node_cpu_seconds_total{mode!="idle"}[5m])
)

Flux (InfluxDB 2.x):

from(bucket: "server_stats")
  |> range(start: -1h)
  |> filter(fn: (r) => r._measurement == "cpu" and r._field == "usage")
  |> aggregateWindow(every: 5m, fn: mean)
  |> group(columns: ["host"])

SQL (TimescaleDB, and InfluxDB 3):

SELECT time_bucket('5 minutes', time) AS bucket,
       host,
       avg(usage) AS avg_usage
FROM cpu
WHERE time > now() - interval '1 hour'
GROUP BY bucket, host
ORDER BY bucket;

Language	Strength	Learning curve
PromQL	Compact, excellent for rates and alert expressions	Unusual at first, quick once learned
Flux	Powerful data scripting and transformation	Steeper; InfluxData has signalled it is in maintenance mode, so check its status before committing
InfluxQL	SQL-like and familiar	Easy, but less capable
SQL	Universal skill, joins, window functions, BI tool support	Lowest for most teams

Language choice affects hiring and maintenance more than most people expect. SQL skills are everywhere; PromQL and Flux skills are specialized.

6. Scaling, Retention and High Availability

InfluxDB. Retention is defined per bucket (v2) and old data is dropped automatically. Clustering and high availability are typically part of the commercial editions, while open source deployments are usually single node with backups.

Prometheus. A single Prometheus server keeps data on local disk and is intentionally simple. For high availability you run two identical servers scraping the same targets. For long retention and global views, the usual answer is remote write to a long-term backend (for example Thanos, Mimir or VictoriaMetrics), which adds moving parts.

TimescaleDB. You inherit PostgreSQL’s mature toolbox: streaming replication, point-in-time recovery, pg_dump, connection pooling and role-based security. Native compression and continuous aggregates (pre-computed rollups) keep large datasets manageable, and retention policies drop old chunks automatically.

Need	Simplest fit
“Keep 15 days of metrics, alert on problems”	Prometheus alone
“Keep years of sensor data with rollups”	TimescaleDB or InfluxDB
“Join metrics with customer or asset tables”	TimescaleDB
“Global view across many clusters”	Prometheus + a long-term backend
“Push data from thousands of devices”	InfluxDB or TimescaleDB

7. Ecosystem and Integrations

Integration	InfluxDB	Prometheus	TimescaleDB
Grafana	Native data source	Native data source	Via the PostgreSQL data source
Telegraf	First-class output	Output plugin for scraping	PostgreSQL output plugin
Kubernetes	Works, less native	De facto standard (service discovery, operators)	Works via operators for PostgreSQL
Exporters (node, MySQL, etc.)	Via Telegraf inputs	Huge exporter library	Via Prometheus adapters or Telegraf
BI and reporting tools	Limited	Limited	Any tool that speaks PostgreSQL
IoT and MQTT pipelines	Common	Uncommon	Common

If your stack is Kubernetes-heavy, Prometheus integration is hard to beat. If your team lives in SQL and BI tools, TimescaleDB fits naturally. If you collect data from many heterogeneous sources and want a purpose-built store with its own UI, InfluxDB is a strong option.

8. Which One Should You Choose?

                      What is your main goal?
                               │
        ┌──────────────────────┼───────────────────────┐
   Monitoring &           General time series      Time series + relational
   alerting for           storage (IoT, events,    data, SQL, BI, joins
   servers / K8s          sensors, metrics)               │
        │                       │                   TIMESCALEDB
   PROMETHEUS              INFLUXDB                (PostgreSQL skills
   (+ Alertmanager,        (push model, UI,         already in team)
    Grafana)               Telegraf ecosystem)

Scenario	Recommendation
Monitor Linux servers, containers and Kubernetes with alerts	Prometheus
Collect IoT or sensor data from remote devices	InfluxDB or TimescaleDB
Store metrics for years and report on them with SQL and BI tools	TimescaleDB
Correlate time series with customers, assets or orders	TimescaleDB
Small team, want a single binary and a UI with minimal fuss	InfluxDB
Already run PostgreSQL and want minimal new technology	TimescaleDB
Need PromQL compatibility for existing dashboards and alerts	Prometheus (or a compatible backend)
Fast-growing metrics with a global view	Prometheus + long-term storage

9. Can You Combine Them?

Yes, and many production stacks do. A common pattern is Prometheus for real-time monitoring and alerting with a long-term store behind it for history. Another is Telegraf feeding both InfluxDB and TimescaleDB during a migration or evaluation. Grafana can query all three in a single dashboard, which makes side-by-side testing straightforward.

A practical evaluation approach:

1. Pick one representative workload (for example, 50 servers or 1,000 devices).
2. Send the same data to two candidates for two weeks.
3. Compare disk usage, query speed, memory use and how easy alerting and reporting feel to your team.

10. Common Selection Mistakes

Mistake	Why it hurts	Better approach
Using Prometheus as a long-term data warehouse	Local storage is not designed for years of history	Add remote write to a long-term backend, or choose another store
Choosing by benchmark charts alone	Results depend on cardinality, batch size and queries	Benchmark with your own data
Ignoring cardinality	Unique IDs in labels or tags cause memory blowups	Keep unbounded values out of labels and tags
Picking a language nobody on the team knows	Slows adoption and hiring	Weigh SQL familiarity heavily
Skipping retention and downsampling	Disks fill and queries slow down	Define retention and rollups from day one
Expecting InfluxDB or Prometheus to handle relational joins	Awkward or impossible	Use TimescaleDB when joins matter
Running any TSDB without backups	Metrics are still business data	Schedule tested backups

11. FAQ

Is InfluxDB better than Prometheus?
They solve different problems. Prometheus is a monitoring system with alerting built around a pull model. InfluxDB is a general time series database that accepts pushed data. For Kubernetes monitoring, Prometheus is the usual choice; for IoT and event data, InfluxDB is often a better fit.

Is TimescaleDB faster than InfluxDB?
It depends on the workload. Published comparisons conflict, and results change with versions and tuning. Test with your own data before deciding.

Can TimescaleDB replace Prometheus?
Not directly. It has no scraping, PromQL or Alertmanager. It can, however, serve as long-term storage for metrics, and it is excellent when you need SQL and joins.

Do I need to know PostgreSQL to use TimescaleDB?
Yes, at least the basics. That is also its advantage: existing PostgreSQL skills, drivers and tools carry over.

Which is easiest to install?
Prometheus is a single binary with a small config file. InfluxDB and TimescaleDB are also straightforward on Linux. See our guides: How to Install InfluxDB on Ubuntu 26.04 and How to Install TimescaleDB on Ubuntu 26.04.

Which is best for IoT?
InfluxDB and TimescaleDB both work well. Choose InfluxDB for a purpose-built time series store with a UI, and TimescaleDB when the device data must be joined with relational information.

12. Conclusion

There is no universal winner. Prometheus is the natural choice for infrastructure and Kubernetes monitoring with alerting. InfluxDB is a flexible, purpose-built store for pushed time series such as IoT and events. TimescaleDB gives you time series performance without leaving PostgreSQL, which is ideal when SQL, joins and reporting matter.

Start from your workload, your team’s skills and your retention needs, run a short pilot with real data, and remember that combining tools is normal.