Iobeam

Name: Iobeam
Brand: Iobeam

Iobeam is a legacy domain now representing Tiger Data's TimescaleDB and Tiger Cloud, a PostgreSQL-compatible time-series database extension and managed cloud service that provides hypertables, continuous aggregates, compression and tiering to store and analyze high-ingest time-series/event data via SQL.

Est 2014|Valuation $1B|15 Ref Customers

LinkedIn 39|Website|FTEs 1|HQ New York, New York, US

MarketsCloud Databases

01Competitive Landscape

Narrative analysis

- **Timescale/Tiger Data vs InfluxDB** - Timescale emphasizes SQL/Postgres ecosystem compatibility and handling of high-cardinality workloads. - InfluxDB Cloud uses an explicit usage-dimensioned pricing model (data in/out, query count, storage) which may be easier or harder to forecast depending on workload. - **Timescale/Tiger Data vs ClickHouse** - ClickHouse is commonly chosen for analytics/OLAP at scale; Timescale is often chosen when teams require Postgres semantics and the ability to combine relational and time-series data. - **When competitors are chosen instead** - If an organization needs ultra-fast OLAP and accepts a non-Postgres operational model: ClickHouse may be preferred. - If the telemetry pipeline is centered on a specific telemetry ecosystem and ingestion/query economics are prioritized: InfluxDB may be preferred.

	Tiger Data / TimescaleDB (via iobeam domain redirect)	InfluxDB Cloud	ClickHouse Cloud
Starting Price	Tiger Cloud compute starts at a low tens of dollars/month (Performance tier); storage billed per-GB-month	Free tier available; usage-based pricing (ingest, queries, storage, egress)	Trial period available; plan-based tiers (Basic/Scale/Enterprise), usage varies
Most suitable for	Teams that require PostgreSQL compatibility combined with time-series features and SQL-based relational joins	Time-series-heavy telemetry stacks, particularly where Influx tooling is preferred	High-performance OLAP/analytics workloads requiring fast aggregations over large datasets
Key Strength	PostgreSQL compatibility; hypertables, continuous aggregates, and compression policies	Purpose-built time-series model and ingestion tooling	Columnar analytics engine optimized for large-scale aggregations
Key Weakness	Operational complexity; licensing/feature clarity can be confusing; resource usage may be high on small deployments	Query and usage pricing can be complex to forecast for high-frequency dashboards/workloads	Different operational and query model compared to Postgres; not drop-in for Postgres-centric app development

01Competitive Landscape

Narrative analysis

Timescale/Tiger Data vs InfluxDB
- Timescale emphasizes SQL/Postgres ecosystem compatibility and handling of high-cardinality workloads.
- InfluxDB Cloud uses an explicit usage-dimensioned pricing model (data in/out, query count, storage) which may be easier or harder to forecast depending on workload.
Timescale/Tiger Data vs ClickHouse
- ClickHouse is commonly chosen for analytics/OLAP at scale; Timescale is often chosen when teams require Postgres semantics and the ability to combine relational and time-series data.
When competitors are chosen instead
- If an organization needs ultra-fast OLAP and accepts a non-Postgres operational model: ClickHouse may be preferred.
- If the telemetry pipeline is centered on a specific telemetry ecosystem and ingestion/query economics are prioritized: InfluxDB may be preferred.

	Tiger Data / TimescaleDB (via iobeam domain redirect)	InfluxDB Cloud	ClickHouse Cloud
Starting Price	Tiger Cloud compute starts at a low tens of dollars/month (Performance tier); storage billed per-GB-month	Free tier available; usage-based pricing (ingest, queries, storage, egress)	Trial period available; plan-based tiers (Basic/Scale/Enterprise), usage varies
Most suitable for	Teams that require PostgreSQL compatibility combined with time-series features and SQL-based relational joins	Time-series-heavy telemetry stacks, particularly where Influx tooling is preferred	High-performance OLAP/analytics workloads requiring fast aggregations over large datasets
Key Strength	PostgreSQL compatibility; hypertables, continuous aggregates, and compression policies	Purpose-built time-series model and ingestion tooling	Columnar analytics engine optimized for large-scale aggregations
Key Weakness	Operational complexity; licensing/feature clarity can be confusing; resource usage may be high on small deployments	Query and usage pricing can be complex to forecast for high-frequency dashboards/workloads	Different operational and query model compared to Postgres; not drop-in for Postgres-centric app development