Firebolt Cloud:
The Analytical Database for Engineers

Millisecond response times over TB-scale datasets. Thousands of SQL queries per second. Predictable costs. Built for data and AI applications where traditional warehouses and lakehouses fail.

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Trusted by Engineering Teams Building at Scale

Why data teams choose Firebolt

Millisecond performance

Sparse indexes prune data before scan. Aggregating indexes pre-compute results. Query optimizer adapts to your data. Millisecond response times at TB scale.

Workload isolation with elastic compute

Run dashboards, batch jobs, and AI workloads on separate engines. No resource contention. No query queuing. Engines scale from 1 to 128 nodes and suspend when idle.

Standard Postgres SQL

Standard Postgres syntax. No proprietary dialect. RBAC, schemas, views, CTEs, window functions all work as expected. Define infrastructure in SQL. Version control your entire platform.

Native Apache Iceberg integration

Read and write Iceberg tables with time travel and schema evolution. Works with AWS Glue, Unity Catalog, and Snowflake Open Catalog. Your data stays in S3 in open formats. No migration. No lock-in

True multi-tenancy

Account-level isolation for each customer. Dedicated databases, engines, and RBAC per tenant. Independent compute scaling. Unified billing with per-account visibility.

ACID guarantees at scale

ACID transactions with snapshot isolation. No partial writes or dirty reads, even during node failures. Transactional DDL: schema changes are atomic and never block queries

Built for AI workloads

Sustain high query throughput for AI agents and LLMs. Thousands of simultaneous queries with millisecond latency. No rate limits. No query queue.

Broad ecosystem integration

Works with your existing data and technology stack. SDKs for Python, Java, Go, Node, and .NET. Integrate with Airflow,Confluent/Kafka, Tableau, Looker, Power BI, and more.

Fast by default.
Tunable when needed.

Primary indexes and subquery caching deliver millisecond latency automatically. For extreme performance requirements, fine-tune with advanced indexing and optimizer controls.

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Sparse primary indexes: Prune irrelevant data partitions before scan.

Aggregating indexes: Pre-compute SUM, COUNT, AVG, MIN, MAX. Queries hit pre-aggregated results instead of raw data

Subquery caching: Intermediate results cached automatically. Repeated subqueries reuse cached data.

Cost-based query optimizer: Rewrites execution plans based on table statistics, index availability, and query patterns. Adapts without manual hints.

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See everything.
Control what matters.

See exactly where compute and time are spent. Execution plans show per-operator timing, scan counts, cache hit rates, and memory usage. Set spend caps and scale-to-zero policies per engine.

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Query execution plans: Operator-level execution time breakdown. Identify expensive joins, aggregations, or scans before production.

Engine resource tracking: CPU, memory, and I/O per engine. Track resource consumption. Allocate costs to teams or customers.

Optimizer hints: Override join order, force index usage, or control aggregation when needed. Use only for edge cases.

Right-sized engines: Scale 1 to 128 nodes per engine. Set spend caps. Auto-suspend when idle. Pay only for active compute.

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Point Firebolt at your Iceberg tables and start querying.

Firebolt reads and writes Apache Iceberg tables natively. Your data stays in S3 in open formats. No proprietary storage. No data duplication. No lock-in.

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Read/write Iceberg tables: Time travel, schema evolution, partition pruning. Query data written by Spark, Trino, or Flink without copying.

Works with your existing catalog: AWS Glue, Unity Catalog, Snowflake Open Catalog. Point Firebolt at the catalog. No data movement.

Incremental adoption: Start with high-value tables. Run performance benchmarks on real queries, then scale up. Existing tools keep working on the same data.