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.
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.
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.
There are three big differences among data warehouses and query engines that limit scalability: decoupled storage and compute, dedicated resources, and continuous ingestion.
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.
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.
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.
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.
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