Timescale Is Now TigerData

Rebrand rationale and scope

• Commenters note that the company position is: “TimescaleDB” remains the time‑series extension, while the company/cloud become “TigerData/Tiger Cloud,” reflecting broader workloads (many not time-series).
• Some agree this makes sense to avoid being pigeonholed as “just time series,” especially with growing emphasis on vectors / pgvectorscale.
• Others say they only care about the company for time-series, view the broader positioning as unwanted “pivot” energy, and are wary of “agentic era/AI” language.

Name choice and confusion

• Many think “Timescale” was a much stronger, clearer brand; “TigerData” is seen as generic and cheesy.
• There is heavy concern about name collisions and confusion with TigerBeetle, TigerGraph, WiredTiger, Tigris Data, and even non‑DB “Tiger…” brands.
• Some initially assumed an acquisition/merger with TigerBeetle or influence from Tiger‑named investors.
• A few offer alternative name ideas (e.g., keeping “scale,” different animal, taxonomic names) and argue they’d have preserved more brand equity.

Tone and marketing criticism

• Several push back on blog claims that MongoDB, Cassandra, InfluxDB, etc. are “technical dead ends” and that “the Lakehouse has won,” calling this overstated, illogical, or pure marketing.
• Referencing and “gloating” about an old skeptical HN comment is seen by some as immature and bad style, even if framed as historical skepticism.

Comparisons with other databases

• Debate around InfluxDB: some say 1.x/2.x rewrites and language churn burned them; others (including Influx employees) defend 3.x and promise better migration tooling.
• Timescale/TigerData staff argue they’re “Postgres on steroids”: good transactional guarantees, joins, a single SQL database for both OLTP and analytics, and competitive performance in their own RTABench versus ClickHouse.
• Critics note Timescale doesn’t win in ClickHouse’s benchmarks; others say benchmarks differ by workload and point to DuckDB as interesting but single-user.

User experiences and operational concerns

• Several positive long‑term production stories: reliable at scale for industrial metrics, easy continuous aggregates/retention, nice hosted/vector experience.
• Some complain about needing deep expertise to get good performance on older data, tiered storage limitations (especially on Azure), and fear any non‑technical rebrand change in a core dependency.

Culture and branding reactions

• Internal “tiger cubs,” “jungle,” and “Tiger Time” language is widely described as cringe or forced “fun,” though a few see it as harmless cultural flair.