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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 | ClickHouse |
|---|---|---|
| Separation of storage and compute | Yes, separation of storage and metadata as well as compute from compute with full workload isolation. | Yes – SharedMergeTree engine in ClickHouse Cloud enables full separation of storage and compute, with compute-compute separation through Warehouses feature (introduced 2025) allowing multiple isolated compute services sharing the same data |
| Supported cloud infrastructure | AWS (GCP coming soon) & anywhere (Firebolt Core) | AWS, GCP, Azure, cloud service and on-premises |
| Isolated tenancy – option for dedicated resources | • Multi-tenant metadata layer • Isolated tenancy for compute & storage per client | • Multi-tenant metadata layer • Isolated tenancy for compute & storage per client in cloud |
| 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. | Configurable cluster size and compute types in ClickHouse Cloud with granular control over nodes (1-128 nodes) and node characteristics. Warehouses feature enables multiple isolated read-only compute environments. |
| 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 deployments available with full control over infrastructure |
| 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 compliance with MergeTree engine family. |
Firebolt is built around three architectural bets that compound: disaggregated storage and compute with sub-second cold start, a vectorized query engine with SIMD-optimized kernels, and sparse indexes that make full scans almost obsolete at scale. When you combine partition pruning with sparse indexes, you're often touching <1% of the data for typical analytical queries. Disaggregated storage isn't just a cost story, it fundamentally changes what's possible for multi-cluster concurrency without the data movement overhead that kills P99 latency in shuffle-heavy systems.
ClickHouse is a column-oriented OLAP database built around a shared-nothing architecture where each node owns its storage and compute, scaling horizontally through explicit sharding and replication. The MergeTree engine family is the core primitive - sorted, sparse-indexed storage with variants (ReplacingMergeTree, AggregatingMergeTree) handling different update and aggregation patterns. Query execution is vectorized with LLVM JIT compilation for hot paths. The tight coupling of storage and compute that makes single-node performance exceptional becomes operationally non-trivial at very large scale.
## 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 | ClickHouse |
|---|---|---|
| Elasticity – Scaling for larger data volumes and faster queries | Granular cluster resize with node types, number of nodes and number of clusters. Zero downtime. | Automatic horizontal and vertical scaling in ClickHouse Cloud with SharedMergeTree architecture. Manual scaling for self-managed deployments with cluster rebalancing capabilities |
| 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 high concurrency with proper resource allocation and configuration. Vertical auto-scaling and horizontal manual scaling. Additional warehouses can idle to zero billing. Primary service always on in multi-warehouse configurations. |
Firebolt separates storage and compute entirely. Data lives in object storage, engines are stateless and spin up in seconds. Multiple engine clusters can read and write from the same storage simultaneously, isolating workloads without contention or data movement. Scaling out is just provisioning; no rebalancing, no shuffle overhead, no distributed consensus. Uses simple SQL commands to scale vertically, horizontally and in cluster count with zero down-time.
ClickHouse Cloud runs on SharedMergeTree, a closed-source engine where data lives in object storage and compute nodes are fully stateless. No sharding needed; you scale by adding compute nodes against shared storage, with vertical autoscaling and scale-to-zero built in. Compute-compute separation lets multiple isolated node groups share the same data without extra copies, useful for isolating reads from writes. The main caveats: metadata coordination through ClickHouse Keeper introduces concurrency limits.
## 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 | ClickHouse |
|---|---|---|
| Indexes | • Sparse primary indexes • Aggregating indexes • Join indexes • Optimizer driven index usage | • Primary indexes • Skipping indexes (minmax, set, bloom filters, ngrambf_v1, tokenbf_v1) • MergeTree indexes • Incremental Materialized views |
| 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. | Configurable compute resources in cloud offering |
| Storage format | Columnar, sorted & compressed & sparsely indexed storage (F3 – Firebolt File Format) with native Apache Iceberg support | Columnar, supports sorted, compressed, encoded & sparsely indexed files with native Apache Iceberg support. |
| 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. | Partitioning by date/time and custom partitions with MergeTree indexes. |
| Result cache | Yes, results and sub-results cache with transactional spoiling. | Yes, results cache with TTL and query condition cache. |
| Warm cache (SSD) | Yes, at indexed data-range level granularity | Yes, at indexed data-range level granularity |
| Support for semi-structured data & JSON functions within SQL | Yes, including Lambda expressions and native nested array structures | Yes, including Lambda expressions and native JSON data type (GA in v25.3) |
| Vector Search and AI Capabilities | • Native vector search capabilities and embeddings • MCP Server for AI driven analytics • Natural Language to SQL • SQL based Inference | • Native vector search capabilities and embeddings • MCP Server for AI driven analytics • Natural Language to SQL • SQL based Inference |
| 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 | • Primary indexes (ORDER BY) • Data skipping indexes (minmax, set, bloom filters, ngrambf_v1, tokenbf_v1) • Materialized views • Projections • PREWHERE optimization • Query analysis tools • Automatic global join reordering (v25.9) • Enhanced JSON query optimization • Streaming secondary indices |
Firebolt's performance advantage starts with how it reads data. While most data warehouses fetch entire partitions over the network, Firebolt works with indexed data ranges that are dramatically smaller, resulting in aggressive pruning that scans a fraction of what other systems touch. Multiple index types (aggregating indexes, join indexes) let users push this further for specific query patterns. Decoupled storage and compute means workloads can be isolated to guarantee consistent latency. A heavy analytical job doesn't degrade concurrent dashboard queries. A single engine handles hundreds of concurrent queries without needing to scale out, which makes it particularly well-suited for operational and customer-facing analytics where sub-second response times are non-negotiable.
ClickHouse's performance story is built on its columnar storage, compression, and indexing capabilities, which make it a consistent benchmark leader for raw query execution speed. The MergeTree engine family and sparse primary indexes are highly effective at minimizing I/O for queries that align well with the sort key. Where it gets complicated is that this performance is not automatic, it requires significant engineering investment to tune table engines, indexes, and merge strategies for each workload. The lack of a cost-based query optimizer means query performance is sensitive to how SQL is written, and standard BI tooling that generates arbitrary SQL will often underperform. For engineering-managed workloads where the query patterns are known and controlled, ClickHouse is extremely fast.
## 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 | ClickHouse |
|---|---|---|
| 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 at TB+ scale with proper indexing • ClickHouse Cloud reduces engineering overhead with managed service • Proven low-latency performance (120ms at 2500 QPS in benchmarks) • Purpose-built for low-latency OLAP and real-time analytics |
| 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 | • Growing ecosystem with 50+ integrations including major BI tools • Native MySQL protocol support enables broad BI tool compatibility • Strong SQL compliance with PostgreSQL compatibility • Best suited for modern analytical workloads and engineering-managed use cases |
| 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 | • Sub-second response times at TB+ scale • Supports 1000 concurrent users per replica • Strong price-performance on customer-facing applications • Native vector search and embeddings |
| 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 | • Good for ad-hoc queries with ClickHouse Cloud’s separated storage/compute architecture • Join optimizations enable more query complexity • Strong sampling capabilities (TABLESAMPLE) for exploratory analysis • Resource management through user quotas prevents query interference • Materialized views offer performance improvements for common aggregation patterns, ad-hoc users specify directly in SQL |
Firebolt is purpose-built for operational and customer-facing analytics where consistent, sub-second query latency is a hard requirement at scale. The combination of aggressive data pruning, multiple index types, and workload isolation makes it the strongest fit for data apps and AI applications serving end users directly, scenarios where P99 latency matters as much as P50, and where concurrent workloads need to be isolated to guarantee SLA consistency. It's less suited for ad-hoc analytics or general-purpose enterprise BI, where ecosystem maturity matter more than raw performance.
ClickHouse is best suited for engineering-managed, high-throughput analytical workloads where query patterns are known in advance and teams have the expertise to tune schema design accordingly. It has a strong track record in observability, telemetry, event analytics, and time-series use cases - scenarios where data volumes are massive, ingestion rates are high, and the queries are well-understood. The tradeoff is operational overhead: getting the most out of ClickHouse requires deliberate investment in sort keys, projections, and materialized views. For teams willing to make that investment, it delivers exceptional raw performance. For teams expecting a more managed, self-optimizing experience, that overhead becomes a liability.