Postgres wins for time-series at scale
Tiger Data, from the creators of TimescaleDB, brings full Postgres to high-volume time-series. InfluxDB moved to a new engine and dropped Flux support between v2 and v3. TimescaleDB is the same PostgreSQL you already know, at scale.
Built for time-series at scale. Trusted by real teams, including teams that moved off InfluxDB.
Why Not
InfluxDB
InfluxDB is quick to stand up for a metrics pipeline. At scale, teams hit architectural limits that turn into rewrites, workarounds, and a second system to run.
Query language churn → rewrites each major version
InfluxDB moved from InfluxQL to Flux to SQL across versions. Flux is not supported in v3, so v2 queries, tasks, and dashboards built on it have to be rewritten.
Not a relational database → data lives in two systems
InfluxDB 3 added SQL through DataFusion, but it is a time-series and event store, not a general-purpose relational database. Joining time-series with your relational data means crossing a system boundary.
No native continuous aggregates → more moving parts
InfluxDB 3 has no built-in continuous aggregates or materialized views. The documented pattern is a trigger plus a downsampler plugin, which adds complexity and extra storage.
Free tier limits → capped before you scale
InfluxDB 3 Core is single-node with retention and database limits, including a 72-hour query window on recent data. Lifting them means moving to Enterprise or Cloud.
A separate stack → operational overhead
You run and scale a dedicated time-series engine alongside your system-of-record, instead of keeping time-series and relational data in one Postgres.
Where InfluxDB is genuinely strong
High-velocity ingest, unlimited cardinality in v3, a permissive open-source Core license (MIT / Apache 2), and the Telegraf ecosystem. We say so plainly, and weigh it in the full comparison.
Tiger Data is Postgres, purpose-built for time-series and analytics at scale.
Built by the team behind TimescaleDB. Everything Postgres, plus time-series power, without sharding or trade-offs.
PostgreSQL + time-series extensions
Full relational model with joins, foreign keys, and constraints
ACID transactions for reliable writes and reads
SQL for operational queries and complex analytics
Time-series extensions: continuous aggregates, compression, downsampling, gapfill
Everything in one database, one set of tools
Document & nonSQL data model
Document model with tags and fields
No native joins or referential integrity
Limited query language (Flux)
Analytics require workarounds and external tooling
Operational and analytical data often live in separate systems
Tiger Data makes Postgres powerful for time-series with fast, affordable, and familiar database features.
Query language
Full PostgreSQL SQL, the same language across every version
Works with existing drivers, ORMs, and BI tools
Data relationships
New capabilities extend the same Postgres engine
No boundary where your query language stops being supported
Analytics & rollups
Native SQL joins, foreign keys, and constraints
Time-series and relational data in one database
Scale & storage limits
Built-in continuous aggregates with auto-refresh
Native downsampling
AI & vector
Native vector search via pgvector and pgvectorscale
Hybrid search in one query: BM25 keyword search (pg_textsearch) with vector search (pgvectorscale)
Embeddings, time-series, and relational context in one query
Challenge | InfluxDB limitation | Tiger Data advantage |
|---|---|---|
Query language | Primary language changed across versions (InfluxQL → Flux → SQL) Flux is not supported in v3 | Full PostgreSQL SQL, the same language across every version Works with existing drivers, ORMs, and BI tools |
Data relationships | v2 → v3 is a migration, not an in-place upgrade Data exported to line protocol and re-imported | New capabilities extend the same Postgres engine No boundary where your query language stops being supported |
Analytics & rollups | Time-series / event store, not a general-purpose relational database Relational data typically lives in another system | Native SQL joins, foreign keys, and constraints Time-series and relational data in one database |
Scale & storage limits | No native continuous aggregates or materialized views Rollups need a trigger plus a downsampler plugin | Built-in continuous aggregates with auto-refresh Native downsampling |
AI & vector | No native vector search AI via a Python engine and Arrow/Parquet interop with external tools | Native vector search via pgvector and pgvectorscale Hybrid search in one query: BM25 keyword search (pg_textsearch) with vector search (pgvectorscale) Embeddings, time-series, and relational context in one query |
This table covers where Tiger Data leads. InfluxDB has genuine strengths of its own, noted above. Choose based on your workload.
Get started with Tiger Cloud
Compression, joins, and materialized views out of the box
Full SQL compatibility
No sharding required
