# Firebolt vs BigQuery (2025) (/comparison/firebolt-vs-bigquery)
## Architecture [#architecture]
The biggest difference among cloud data warehouses are whether they separate storage and compute, how much they isolate data and compute, and what clouds they can run on.
| Feature | Firebolt | BigQuery |
| ------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| Separation of storage and compute | Yes, separation of storage and metadata as well as compute from compute with full workload isolation. | Fully serverless with complete separation of compute (Dremel) and storage (Colossus), powered by Jupiter network and Borg orchestration |
| Supported cloud infrastructure | AWS (GCP coming soon) & anywhere (Firebolt Core) | Google Cloud only |
| Isolated tenancy – option for dedicated resources | • Multi-tenant metadata layer
• Isolated tenancy for compute & storage per client | • Multi-tenant pooled resources
• VPC Service Controls provide enhanced security and connectivity isolation to customer VPCs
• Cross-region disaster recovery for enterprise workloads |
| Control vs abstraction of compute | Uses engine abstraction:
• Each engine has configurable cluster size (1-128 nodes) for horizontal scaling.
• Configurable compute family (compute vs storage optimized) and type (XS, S, M, L, XL) for vertical scaling
• Number of clusters for concurrency (auto)scaling.
Provides full workload isolation across engines. | Fully serverless with no control over compute resources – BigQuery automatically allocates computing resources as needed with intelligent workload management and dynamic slot allocation |
| Self-hosted and hybrid deployment options | • Firebolt Core: Forever free, self-hosted edition with full query engine capabilities
• Same performance and features as managed service
• Deploy anywhere: local laptop, cloud, datacenter, Kubernetes
• Production-grade distributed architecture
• No usage restrictions except building competing SaaS | No self-hosted options – fully managed service only |
| ACID Compliance and Transactions | • Full ACID compliance with snapshot isolation
• Multi-statement transactions supported
• Strong consistency across all operations
• Supports concurrent reads and writes
• Transactional integrity for data applications | Limited ACID support – eventual consistency model with some transactional capabilities |
**Firebolt** is built on a natively decoupled storage & compute architecture, on AWS only. Data has to be copied outside of your VPC into the Firebolt, where both your compute and data run in a dedicated and isolated tenant. A "Firebolt Engine" can be granularly configured across # of nodes and different CPU/RAM/SSD combinations
**BigQuery** was one of the first decoupled storage and compute architectures. It is a unique piece of engineering and not a typical data warehouse in part because it started as an on-demand serverless query engine. It runs in multi-tenancy with shared resources, allocated as "slots" which represent a virtual CPU that executes SQL. BigQuery determines how many slots a query requires, without the ability of the user to control it. BigQuery can be priced on a $/TB scanned basis or through slot reservations. A slot in BigQuery is logically equivalent to 0.5 vCPU and 0.5GB of RAM. There are multiple models to allocate slots in BigQuery.
## Scalability [#scalability]
There are three big differences among data warehouses and query engines that limit scalability: decoupled storage and compute, dedicated resources, and continuous ingestion.
| Feature | Firebolt | BigQuery |
| --------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Elasticity – Scaling for larger data volumes and faster queries | Granular cluster resize with node types, number of nodes and number of clusters. Zero downtime. | Fully automated serverless scaling – BigQuery automatically determines resource allocation and scales to petabytes without user intervention. Can dynamically burst beyond baseline slot allocations for performance optimization |
| Elasticity – Scaling for higher concurrency | A single engine can handle hundreds of concurrent queries. Engines auto-scale the number of clusters up and down base on resource usage thresholds. Idle engines scale down to zero billing. | Dynamic concurrency management with query queueing supporting up to 1,000 interactive queries and 20,000 batch queries per project per region. Automatic fair scheduling and slot distribution across workloads |
**Firebolt** can handle the largest data volumes and concurrency on a single comparable cluster size, thanks to its superior hardware efficiency. Thanks to its decoupled storage & compute architecture it scales very well to large data volumes. However, resizing an engine size isn't instant and requires orchestration if avoiding downtime is necessary. A single Firebolt engine can support hundreds of concurrent queries, avoiding the need to scale out for most use cases. Scaling horizontally for even higher concurrency is manual.
**BigQuery** scales very well to large data volumes, and automatically assigns more compute resources when needed behind the scenes, in the form of "slots". BigQuery works either in an "on-demand pricing model", where slot assignment is completely in the hands of BigQuery and the state of the shared resource pool, or in "flat-rate pricing model" where slots are reserved in advance. With reserved slots there is more control over compute resources, thus making scaling more predictable. Concurrency is limited to 100 users by default.
## Performance [#performance]
Performance is the biggest challenge with most data warehouses today.
While decoupled storage and compute architectures improved scalability and simplified administration, for most data warehouses it introduced two bottlenecks; storage, and compute. Most modern cloud data warehouses fetch entire partitions over the network instead of just fetching the specific data needed for each query. While many invest in caching, most do not invest heavily in query optimization. Most vendors also have not improved continuous ingestion or semi-structured data analytics performance, both of which are needed for operational and customer-facing use cases.
| Feature | Firebolt | BigQuery |
| ------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Indexes | • Sparse primary indexes
• Aggregating indexes
• Join indexes
• Optimizer driven index usage | Search indexes (GA) for efficient text search optimization on STRING, JSON, and array columns. Support for LOG\_ANALYZER, NO\_OP\_ANALYZER, and PATTERN\_ANALYZER with column-level granularity for improved query performance and cost efficiency |
| Compute tuning | SQL defined engines. Control number of nodes, node family and type per cluster, with one or more clusters per engine. Multiple engines isolate workloads. | Serverless architecture with automatic resource optimization – no manual tuning required. Intelligent workload management with AI-powered resource allocation and dynamic slot distribution |
| Storage format | Columnar, sorted & compressed & sparsely indexed storage (F3 – Firebolt File Format) with native Apache Iceberg support | Columnar & compressed storage (Capacitor format) with support for open table formats including Apache Iceberg, Delta Lake, and Hudi. Intelligent tiering with automatic long-term storage cost reduction after 90 days |
| Table-level partition & pruning techniques | • User-defined table-level partitions are optional.
• Data is automatically sorted, compressed and indexed into F3 format.
• Pruning at indexed data-range level. | • Automatic table organization with intelligent micro-partitioning • Clustering keys for data organization • Automatic partition pruning optimization • Supports time-based and custom partitioning strategies |
| Result cache | Yes, results and sub-results cache with transactional spoiling. | Yes, with cross-user result caching and intelligent cache management for up to 24 hours |
| Warm cache (SSD) | Yes, at indexed data-range level granularity | BI Engine provides in-memory caching and acceleration for frequently accessed data and dashboards |
| Support for semi-structured data & JSON functions within SQL | Yes, including Lambda expressions and native nested array structures | Yes, including advanced JSON functions, Lambda expressions, and native support for nested and repeated fields |
| Vector Search and AI Capabilities | • Native vector search capabilities and embeddings
• MCP Server for AI driven analytics
• Natural Language to SQL
• SQL based Inference | • BigQuery ML for in-database machine learning • Vertex AI integration • Natural language querying with Gemini AI • Limited vector search capabilities |
| Query Optimizations | • Primary indexes, aggregating indexes, join indexes, sparse indexes
• Sub-plan result caching
• F3 storage format optimization
• Automatic query optimizer with aggressive pruning
• Late column materialization
• Query analysis tools based on execution telemetry | • Advanced query optimizer with Dremel engine • Search indexes with column-level granularity • Materialized views with smart refresh and automatic query rewriting • BI Engine in-memory acceleration • Gemini AI-powered query optimization and natural language querying • Cross-user result caching • Automatic partitioning and clustering optimization • Cost-based optimization with intelligent workload management |
**Firebolt** is the fastest when it comes to query performance when compared to cloud data warehouses and services like Athena. Its unique approach to storage and indexing results in highly aggressive data pruning that scans dramatically less data compared to other technologies. While other technologies scan partitions or micro-partitions, Firebolt works with indexed data ranges, that are significantly smaller. In addition, Firebolt lets user accelerate queries further with multiple index types (Aggregating index, Join index), and using its decoupled storage & compute architecture workloads can be easily isolated to guarantee consistent performance.
**BigQuery** lines up in benchmarks in the same ballpark as other cloud data warehouses but does come in consistently last in most queries. Beyond implementing according to best practices, there is little you can do to accelerate BigQuery performance, as it determines the amount of resources (slots) the query needs for you. BigQuery can be used together with "BigQuery BI Engine" for lower latency analytics. However, BI Engine is limited in terms of scale because it runs in memory. Its maximum capacity is 100GB.
## Use cases [#use-cases]
There are a host of different analytics use cases that can be supported by a data warehouse. Look at your legacy technologies and their workloads, as well as the new possible use cases, and figure out which ones you will need to support in the next few years.
| Feature | Firebolt | BigQuery |
| ---------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Low-latency dashboards | • 120ms query latency at 4000 QPS (FireScale benchmark 2025)
• Sub-second performance at TB+ scale with proper indexing
• Built for AI-driven analytics, dashboards, and real-time analytic applications | • Sub-second to seconds response times at TB+ scale with BI Engine acceleration • Search indexes and materialized views provide significant performance improvements for dashboard queries • Intelligent caching reduces query costs for repeated dashboard access • Dynamic concurrency management supports high user loads |
| Enterprise BI | • Growing ecosystem with focus on modern BI tools
• Strong SQL compliance with PostgreSQL
• Wire level compatibility drives expansion to PostgreSQL BI and ETL ecosystem | • Mature and comprehensive Enterprise DW feature set with native Google Cloud ecosystem integration • Strong integration with Looker, Looker Studio, and major BI tools • Gemini AI integration for natural language insights • Cross-cloud analytics capabilities • Advanced governance with Dataplex Universal Catalog |
| Data Apps and AI Applications (Customer-facing low-latency high concurrency) | • 120ms latency at 4000+ QPS proven performance at TB+ scale
• Supports hundreds to thousands of concurrent queries on single engine
• Price-performance leader (8x better than Snowflake, 18x vs Redshift) • Purpose-built for AI agents and data-intensive applications
• Native vector search and embeddings | • Dynamic concurrency supporting 1,000+ interactive queries with intelligent queuing and fair scheduling • Sub-second response times with BI Engine acceleration and search indexes • Serverless architecture eliminates infrastructure management overhead • Advanced caching and materialized views optimize repeated queries • AI-powered optimization for customer-facing applications • BigQuery ML for AI workloads |
| Ad hoc | • Excellent performance out-of-the-box with engine optimized for star and snowflake joins and aggregations
• Self learning query plan optimizer
• Full workload isolation prevents ad-hoc complexity from affecting real-time workloads
• Aggregating indexes are automatically used by optimizer | • Excellent for ad-hoc analytics with serverless architecture requiring zero infrastructure management • Intelligent query optimization handles unpredictable workloads automatically • Gemini AI integration enables natural language querying for business users • Advanced JSON support and schema inference enable flexible data exploration • Cross-cloud analytics capabilities for federated queries |
**Firebolt** stands out by being the fastest cloud data warehouse when compared to Snowflake, Redshift, BigQuery and Athena. It's great for delivering sub-second analytics at scale, while remaining hardware efficient and high concurrency friendly. This makes it a great choice for operational use cases and customer-facing data apps. Given that it is not as feature-rich and integration rich as the more mature data warehouses makes it a lesser fit for a general-purpose Enterprise data warehouse. It is also not the best fit for ad-hoc use cases, because of the need to predefine indexing at the table level.
**BigQuery** is a mature general-purpose data warehouse, which lends itself well to internal BI & reporting. The fact that it's serverless in nature and tightly integrated with GCP, makes it very convenient for Ad-Hoc analytics and ML use cases on GCP. On the other hand, because BigQuery makes resource allocation decisions for you, it is not always the best fit for operational use cases and Data Apps where performance needs to be consistent and predictable.