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Brief
F

Firebolt

Delivers sub-second query performance on massive datasets at significantly lower costs than traditional cloud data warehouses through purpose-built indexing and caching technology.

Tel Aviv, IsraelFounded 2019

Last updated May 11, 2026 by ATDb automated enrichment

Industry
Data Infrastructure/Analytics
Business Model
SaaS
Target Market
Enterprise
Employee Count
51-200
Funding
$164M
API Available
Yes
Market Position

High-performance cloud data warehouse provider serving data-intensive industries including AdTech

Overview

Firebolt is a cloud-native data warehouse platform designed to deliver sub-second query performance on massive datasets. Founded by a team of database veterans, the company built its architecture from the ground up to address the performance and cost challenges of traditional cloud data warehouses. Firebolt's technology uses a decoupled storage and compute architecture with advanced indexing and caching mechanisms to achieve exceptional speed at a fraction of the cost of legacy solutions. While not exclusively an AdTech company, Firebolt has significant presence in the advertising technology sector, where its high-performance analytics capabilities are particularly valuable for processing large volumes of ad impression data, user behavior analytics, and real-time bidding data. The platform serves data-intensive industries including AdTech, gaming, fintech, and e-commerce, where fast query performance on large datasets is critical for business operations and decision-making. Firebolt competes in the modern cloud data warehouse market against established players like Snowflake, Google BigQuery, and Amazon Redshift, positioning itself as the performance-optimized alternative that can handle extreme-scale analytics workloads more efficiently and cost-effectively.

Products & Features

Firebolt Data Warehouse

Cloud-native SQL data warehouse optimized for extreme performance on large-scale analytics workloads

Sparse Index

Proprietary indexing technology that enables fast query performance without loading entire datasets into memory

Decoupled Storage and Compute

Architecture that allows independent scaling of storage and compute resources for cost optimization

Key Features
Sub-second query performanceAdvanced indexing and cachingDecoupled storage and compute architectureSQL compatibilityAutomatic optimizationReal-time data ingestionColumnar storage formatVectorized query execution
Use Cases
Real-time ad analytics and reportingUser behavior analysis at scaleCampaign performance optimizationFraud detection and preventionCustomer data platform analyticsGaming telemetry analysisE-commerce analyticsLog analytics
Customer Segments
AdTech platformsGaming companiesE-commerce platformsFintech companiesSaaS companies with large data volumesData-driven enterprises
Connections

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