Timescale Cloud earned its users honestly: it took PostgreSQL — the database you already know — and made time-series workloads genuinely fast on it. Hypertables that partition themselves, continuous aggregates that keep dashboards cheap, columnar compression that routinely shrinks storage by an order of magnitude. If you're evaluating alternatives, start by admitting that's a strong package.
But people do look elsewhere, for a few recurring reasons. The company rebranded to TigerData in 2025 (Timescale Cloud became Tiger Cloud), and rebrands make teams re-evaluate. Some workloads outgrow the row-store-plus-compression model and want a true columnar engine. And plenty of teams discover the opposite: their "time-series problem" is a few million rows a month, and a specialist database was never necessary.
So the honest first question isn't "which alternative" — it's "which bucket are you in?"
Three buckets
- 1You genuinely need a time-series/analytics engine. Billions of rows, high-cardinality metrics, sub-second aggregations over huge ranges. Look at ClickHouse, InfluxDB, QuestDB.
- 2You want to stay on Postgres with time-series extensions. You like SQL, joins, and your existing tooling. Look at Aiven or self-managing TimescaleDB — with a licensing caveat below.
- 3Your time-series needs are modest and Postgres alone is enough. Partitioning and BRIN indexes carry further than most people expect. Any good managed Postgres works — this is where PandaStack sits.
Note on bucket 2: the full TimescaleDB feature set (compression, continuous aggregates) is under the Timescale License, which is why you won't find it on most managed Postgres providers — AWS RDS doesn't offer the extension at all. The Apache-2 edition, which some providers do ship, is hypertables without the headline features. Check which edition you're actually getting before you migrate.
1. ClickHouse Cloud — when aggregation speed is the whole point
ClickHouse is a columnar OLAP database, and for pure analytical scans it's in a different weight class than anything Postgres-based. Aggregating billions of rows across months of data is its home turf, and its compression is excellent because columnar storage is compression.
What it does well: raw analytical throughput, materialized views for pre-aggregation, mature managed offering with usage-based pricing and a free trial tier, huge ecosystem momentum.
What to watch: it is not Postgres. No real UPDATE/DELETE semantics in the way you're used to, eventual consistency in mutations, joins want care. It's a warehouse-shaped tool — pair it with an OLTP database rather than replacing one. Migrating from Timescale means rewriting schema and some queries, not just changing a connection string.
Docs: https://clickhouse.com/docs
2. InfluxDB — the metrics-native choice
InfluxDB has been synonymous with metrics for a decade, and the 3.x generation rebuilt the engine on Apache Arrow and Parquet with SQL support — a big step toward the mainstream after years of Flux, its own query language.
What it does well: ingestion-heavy metrics workloads, retention policies as a first-class concept, tight ecosystem with Telegraf collectors and Grafana, serverless cloud option with usage-based pricing.
What to watch: the 1.x → 2.x → 3.x transitions were rough on operators, and the query-language story (InfluxQL, Flux, now SQL) has changed more than once. If your team wants boring continuity, weigh that history. It's also purpose-built — relational workloads don't belong here.
Docs: https://docs.influxdata.com/
3. QuestDB — SQL time-series with serious ingestion speed
QuestDB is an open-source time-series database with a SQL interface, columnar storage, and a focus on very high ingestion rates — it's popular in financial and sensor-data workloads. It speaks the InfluxDB line protocol for ingestion and supports Postgres wire protocol for queries, which makes trialing it unusually low-friction.
What it does well: ingestion performance, SAMPLE BY time-bucket queries that read naturally, open-source core you can self-host, managed enterprise offering if you don't want to.
What to watch: smaller ecosystem and community than the giants above; Postgres wire compatibility covers queries, not the full extension/tooling universe. It's a specialist — the right kind of specialist if your workload matches.
Site: https://questdb.com/
4. Aiven for PostgreSQL — managed Postgres with the Apache-2 Timescale extension
Aiven runs managed open-source databases across AWS, GCP, and Azure, and its PostgreSQL service can enable the Apache-2 edition of the TimescaleDB extension. If your Timescale usage is mostly "hypertables and time_bucket" rather than compression and continuous aggregates, this can be a near drop-in — with multi-cloud portability as a bonus.
What it does well: genuinely multi-cloud, solid operational tooling (forks, read replicas, point-in-time recovery), one vendor for Postgres plus Kafka/Redis/ClickHouse if your stack sprawls.
What to watch: the Apache-2 edition limitation is the whole ballgame — audit which Timescale features you actually use before assuming parity. Pricing is tiered per plan; check https://aiven.io/pricing for current numbers.
5. PandaStack managed PostgreSQL — when Postgres alone is enough
Full disclosure: PandaStack is our platform, and it is not a time-series specialist. I'll tell you exactly where it fits.
A lot of "we need Timescale" workloads are an IoT side project, an events table, or per-customer metrics measured in millions of rows, not billions. Plain PostgreSQL handles that with declarative partitioning and BRIN indexes — no extension required. What those teams actually feel is the *operational* pain: provisioning, backups, and wiring credentials into the app.
That's the part PandaStack automates. Managed PostgreSQL (14.x and 16.x — MySQL, MongoDB, and Redis too), orchestrated by KubeBlocks on Kubernetes, with daily scheduled backups plus manual ones. The distinctive bit: databases attach to your apps, and DATABASE_URL is injected automatically — provision, attach, deploy, no credential copying. Your app itself deploys from a Git push on the same platform, with live build logs.
What it does well: the app-plus-database golden path; flat plans (Free $0, Pro $15/mo, Premium $25/mo) instead of usage math; backup retention of 7/15/30 days by plan.
What to watch: no TimescaleDB extension, so this is bucket 3 only — if you need compression ratios and continuous aggregates over billions of rows, use a specialist above. Free-tier databases get a small storage volume, suited to dev and hobby workloads rather than heavy production.
Quick comparison
| Option | Model | Postgres-compatible | Best for |
|---|---|---|---|
| ClickHouse Cloud | Columnar OLAP | No | Massive analytical scans |
| InfluxDB Cloud | Purpose-built TSDB | No | Metrics pipelines, Telegraf/Grafana shops |
| QuestDB | Time-series, SQL | Wire protocol (queries) | High-rate ingestion, finance/sensors |
| Aiven for PostgreSQL | Managed Postgres + Apache-2 Timescale | Yes | Near drop-in, multi-cloud |
| PandaStack | Managed Postgres, app-attached | Yes (it *is* Postgres) | Modest time-series inside a product |
How I'd decide
Measure before you migrate. If your biggest table is under ~100M rows and your dashboards aggregate over days rather than years, you're probably in bucket 3 and a specialist database is complexity you don't need. If you're compressing terabytes and leaning on continuous aggregates daily, buckets 1 and 2 are where the real evaluation happens — and Timescale/Tiger itself may still win it, which is a fine outcome.
If bucket 3 sounds like you — an app and a Postgres database that should just wire themselves together — that's the case PandaStack was built for, and you can try it at https://pandastack.io.