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## 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 | Druid |
|---|---|---|
| Separation of storage and compute | Yes, separation of storage and metadata as well as compute from compute with full workload isolation. | No |
| Supported cloud infrastructure | AWS (GCP coming soon) & anywhere (Firebolt Core) | Can be installed anywhere |
| Isolated tenancy – option for dedicated resources | • Multi-tenant metadata layer • Isolated tenancy for compute & storage per client | Single tenant |
| 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. | • Complex configuration of compute tier with multiple role-specific nodes • Configurable node count • Configurable compute types (virtual machines or kubernetes) |
| 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 | Self-managed deployment required |
| 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 with eventual consistency |
Firebolt is built on a natively decoupled compute & storage 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.
Druid is an OLAP engine designed to provide fast real time analytics. Druid adopts a clustered architecture with servers that host various role specific processes. These processes address real time and batch ingestion, indexing, querying of historical and real time data. Apache Druid can be deployed as a virtual machine or a Kubernetes based cluster. Druid does not support a decoupled compute & storage architecture. Deep storage in the form of object storage is used to replicate data to.
## 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 | Druid |
|---|---|---|
| Elasticity – Scaling for larger data volumes and faster queries | Granular cluster resize with node types, number of nodes and number of clusters. Zero downtime. | Scale-up of nodes requires careful planning and downtime. Addition of new nodes for scale-out is possible |
| 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. | Supports 100s to 100,000s queries per second (1000+ QPS) with proper configuration and scaling |
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.
Druid provides the ability to handle fast ingest and high concurrency. Custom sizing and cluster tuning are required to balance the compute, memory, storage needs of each process within Druid and to provide high concurrency. Druid clusters can be grown by adding nodes with automatic rebalancing of storage segments assigned to nodes. Self hosted Druid on Kubernetes is an option that users leverage to simplify scaling. Additionally, Cloud based managed Druid offerings are being rolled out. However, these managed offerings are limited in scale and scaling is not granular.
## 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 | Druid |
|---|---|---|
| Indexes | • Sparse primary indexes • Aggregating indexes • Join indexes • Optimizer driven index usage | Compressed bitmap indexes for data access and roll-ups to manage aggregations |
| 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. | On-premises, self-managed hardware. Druid requires infrastructure management and leverages commonly available instance types |
| Storage format | Columnar, sorted & compressed & sparsely indexed storage (F3 – Firebolt File Format) with native Apache Iceberg support | Columnar storage format with time-based sorting |
| 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. | Restrictive time-based partitioning. Can partition based on other secondary columns |
| Result cache | Yes, results and sub-results cache with transactional spoiling. | Ability to support caching on broker (set to off by default) |
| Warm cache (SSD) | Yes, at indexed data-range level granularity | Yes, at much larger segment level granularity |
| Support for semi-structured data & JSON functions within SQL | Yes, including Lambda expressions and native nested array structures | Recommend flattening JSON or translate to array prior to loading. No support for JSON parsing at query runtime |
| Vector Search and AI Capabilities | • Native vector search capabilities and embeddings • MCP Server for AI driven analytics • Natural Language to SQL • SQL based Inference | No native AI or 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 | • Compressed bitmap indexes • Roll-up aggregations • Time-based optimization • Query optimization requires manual tuning |
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 users 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.
Druid provides high performance through columnar storage format, parallel processing, bitmap indexes and roll-ups. Druid, however, recommends a denormalized data model for performance needs. Join operations in Druid are a relatively new feature with various limitations, especially if there is a need to join large datasets.
## 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 | Druid |
|---|---|---|
| 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 load times optimized for time-series and real-time analytics • Built for high-concurrency interactive dashboards • Requires denormalized data model |
| 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 | • Limited integrations with traditional Enterprise BI tools • Strong for real-time operational dashboards • Requires specialized visualization tools |
| 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 | • Built for high concurrency (1000+ QPS) with distributed architecture • Sub-second response times for time-series data • Optimized for real-time operational applications • No AI capabilities |
| 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 | • Not optimized for ad-hoc queries • Requires predefined roll-ups and data modeling • Limited flexibility for exploratory analysis |
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.
Druid is designed as an OLAP engine to provide fast access to aggregations that are run against large volumes of data. Druid is typically used for customer facing analytics and streaming data processing. Druid is used as an add-on with other data warehousing products that are efficient at scaling, joining, and filtering large volumes of data. It is not a suitable option for data warehouse replacement.