SQREEM

Name: SQREEM
Brand: SQREEM

The Large Behavioral Model: AI that understands human intent and predicts real-world behaviour.

Est 2010|Valuation $228.4M|Raised $35.7M

LinkedIn 3K|Website|FTEs 117|HQ Downtown Core, Central Region, Singapore, SG

MarketsData Warehouses & Lakehouses

01Competitive Landscape

Narrative analysis

- Situations where SQream may be beneficial: - Workloads that can leverage GPU-parallel SQL to reduce runtime and infrastructure cost relative to CPU-based warehouses. - Offload patterns where the heaviest analytical jobs are executed on an accelerator platform while retaining existing warehouse/lakehouse for other workloads. - Situations where competitors may be preferable: - Organizations that prioritize a broad marketplace ecosystem, extensive governance features, and minimal GPU infrastructure management overhead may prefer other cloud data warehouses. - Teams that are Spark-first and rely on integrated ML tooling and notebook workflows may find a unified platform more operationally convenient.

Market adoption signals

Press coverage and vendor disclosures indicate enterprise customers and a geographic expansion effort following recent funding. Procurement decisions should be informed by direct vendor references and POC results rather than press summaries alone.

	SQream	Snowflake	Databricks
Starting Price	Quote-based; annual subscription per GPU (public list pricing not posted)	Consumption-based credits + storage (pricing varies by edition/region/contract)	Usage-based model with contract options; pricing varies by cloud and contract
Typical Fit	Very large/heavy SQL analytics and data-prep workloads where GPU acceleration may improve runtime/cost	Broad cloud data warehousing with governed data sharing and marketplace integrations	Lakehouse and unified data engineering + ML platform; strong for Spark-centric workflows
Distinguishing Capability	GPU-accelerated SQL engine designed for parallel analytical processing on large datasets (vendor materials and self-published benchmarks)	Mature ecosystem, data governance features, and broad adoption across enterprises	Integrated data engineering and ML tools, notebooks, and managed Spark execution
Considerations / Constraints	Limited public user-review volume; requires GPU infrastructure or cloud GPU instances; quote-based pricing	Consumption and credit model complexity; potential cost if not managed	Requires Spark expertise for many workflows; costs and performance depend on tuning and workload patterns

01Competitive Landscape

Narrative analysis

Situations where SQream may be beneficial:
- Workloads that can leverage GPU-parallel SQL to reduce runtime and infrastructure cost relative to CPU-based warehouses.
- Offload patterns where the heaviest analytical jobs are executed on an accelerator platform while retaining existing warehouse/lakehouse for other workloads.
Situations where competitors may be preferable:
- Organizations that prioritize a broad marketplace ecosystem, extensive governance features, and minimal GPU infrastructure management overhead may prefer other cloud data warehouses.
- Teams that are Spark-first and rely on integrated ML tooling and notebook workflows may find a unified platform more operationally convenient.

Market adoption signals

Press coverage and vendor disclosures indicate enterprise customers and a geographic expansion effort following recent funding. Procurement decisions should be informed by direct vendor references and POC results rather than press summaries alone.

	SQream	Snowflake	Databricks
Starting Price	Quote-based; annual subscription per GPU (public list pricing not posted)	Consumption-based credits + storage (pricing varies by edition/region/contract)	Usage-based model with contract options; pricing varies by cloud and contract
Typical Fit	Very large/heavy SQL analytics and data-prep workloads where GPU acceleration may improve runtime/cost	Broad cloud data warehousing with governed data sharing and marketplace integrations	Lakehouse and unified data engineering + ML platform; strong for Spark-centric workflows
Distinguishing Capability	GPU-accelerated SQL engine designed for parallel analytical processing on large datasets (vendor materials and self-published benchmarks)	Mature ecosystem, data governance features, and broad adoption across enterprises	Integrated data engineering and ML tools, notebooks, and managed Spark execution
Considerations / Constraints	Limited public user-review volume; requires GPU infrastructure or cloud GPU instances; quote-based pricing	Consumption and credit model complexity; potential cost if not managed	Requires Spark expertise for many workflows; costs and performance depend on tuning and workload patterns