ON THIS PAGE
## 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 | Athena |
|---|---|---|
| Separation of storage and compute | Yes, separation of storage and metadata as well as compute from compute with full workload isolation. | Yes, serverless with optional provisioned capacity. Workloads can be isolated through Workgroups and Capacity Reservations |
| Supported cloud infrastructure | AWS (GCP coming soon) & anywhere (Firebolt Core) | AWS only |
| Isolated tenancy – option for dedicated resources | • Multi-tenant metadata layer • Isolated tenancy for compute & storage per client | • Multi-tenant pooled resources by default • Dedicated compute resources available via Provisioned Capacity • VPC endpoint connections supported |
| 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. | • Serverless by default with no infrastructure control • Optional Provisioned Capacity allows dedicated DPU allocation (minimum 24 DPUs) • Two pricing models: on-demand ($5/TB scanned) or provisioned ($0.30/DPU-hour) |
| 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 – serverless 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 | No ACID compliance – eventual consistency model |
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.
Athena is serverless and built on a decoupled storage and compute architecture that queries data directly in S3, without the need to ingest/copy the data. It runs in multi-tenancy with shared resources. Users do not have control over the compute resources Athena chooses to allocate per query from the shared resource pool. For folks requiring additional or dedicated resources, they can reserve dedicated processing capacity in the form of Data Processing Units (DPU), with each DPU providing 4 vCPU and 16 GB RAM. RPU allocation ranges from 24 - 1000 per region.
## 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 | Athena |
|---|---|---|
| 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 abstracted on-demand scaling • Provisioned Capacity allows manual scaling of DPUs for predictable performance • Capacity reservations can be adjusted with minimum 1-hour billing periods |
| 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. | • Default limit of 25 concurrent DML queries and 20 DDL queries (adjustable via service quotas) • Provisioned Capacity enables higher concurrency with dedicated DPUs • Query queuing available when capacity is exceeded |
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.
Athena is a shared multi-tenant resource, with no guarantees on the amount or availability of the resources allocated for your queries. From a data volume perspective, it can scale to large volumes, but large data volumes can suffer from very long run times and frequent time outs. Query concurrency is maxed at 20. If scalability is a top priority, Athena is probably not the best choice.
## 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 | Athena |
|---|---|---|
| Indexes | • Sparse primary indexes • Aggregating indexes • Join indexes • Optimizer driven index usage | No traditional indexes – relies on partition pruning and data organization in S3. Uses columnar formats and compression for optimization |
| 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. | • No compute tuning in on-demand mode • Provisioned Capacity allows DPU allocation control (4 vCPU and 16GB RAM per DPU) • Minimum 24 DPUs with scaling in 4-DPU increments |
| Storage format | Columnar, sorted & compressed & sparsely indexed storage (F3 – Firebolt File Format) with native Apache Iceberg support | Supports multiple formats: Parquet, ORC, Avro, JSON, CSV, TSV on S3. Native support for open table formats including Apache Iceberg, Apache Hudi, and Delta Lake |
| 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. | • User-defined table-level partitions with Hive-style partitioning • Pruning at partition level • Partition projection for advanced performance optimization • Supports open table formats with built-in partitioning |
| Result cache | Yes, results and sub-results cache with transactional spoiling. | Query result caching for up to 30 days with configurable retention. Results reuse supported across workgroups |
| Warm cache (SSD) | Yes, at indexed data-range level granularity | No local caching – queries data directly from S3. Relies on S3's performance characteristics and intelligent tiering |
| Support for semi-structured data & JSON functions within SQL | Yes, including Lambda expressions and native nested array structures | Yes, comprehensive JSON support including Lambda expressions, array functions, and native nested data handling |
| 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 | • Cost-based optimizer (CBO) in Athena engine v3 • Query result caching (up to 30 days) • Partition projection for advanced optimization • CTAS for precomputed queries • Join reordering and aggregation pushdown • Automatic parallel query execution • Support for columnar formats (Parquet, ORC) • Integration with AWS Glue Data Catalog |
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.
Athena (and Presto) are designed to query data where it is, sacrificing storage-compute optimizations. This makes it very convenient for easy and immediate querying but at the expense of performance. This typically puts Athena behind cloud data warehouses in terms of performance. But Athena still does relatively well in performance benchmarks, especially when external storage is managed by experts. While it supports partitions, there is no support for indexing, and together with the fact that resources are pooled from a shared multi-tenant service, low-latency and consistent performance are not Athena's sweet spot. A cloud data warehouse be more performant better than Athena in most 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 | Athena |
|---|---|---|
| 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 | • Seconds to minutes response times for interactive dashboards • Performance varies based on data partitioning, file formats, and query optimization • Provisioned Capacity can improve consistency for dashboard workloads • Best suited for analytical dashboards rather than sub-second operational dashboards |
| 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 | • Good integration with AWS ecosystem BI tools (QuickSight, etc.) • Standard SQL compatibility enables most BI tool connections • Cost-effective for variable workloads and ad-hoc analytics • JDBC/ODBC drivers support enterprise BI tools • Limited advanced BI features compared to dedicated data warehouses |
| 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 | • Default concurrency limits (25 DML/20 DDL queries) may require service quota increases • Provisioned Capacity enables higher concurrency with dedicated resources • Seconds-level response times typical • Cost-effective for customer-facing analytics with proper optimization • Best suited for analytical rather than operational workloads • No native 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 | • Purpose-built for ad-hoc analytics on data lakes • Serverless with zero infrastructure management • Direct querying of S3 data without ETL • Cost-effective pay-per-query model ideal for exploratory analysis • Strong support for multiple data formats and federated queries • Apache Spark integration for advanced analytics |
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.
Athena is a great choice for Ad-Hoc analytics. You can keep the data where it is, and start querying without worrying about hardware or pretty much anything else, given that Athena is serverless and takes care of everything behind the scenes. However, it is not a great fit when you need consistent and fast query performance, and/or high concurrency. This is why it is typically not the best choice for operational and customer-facing applications. It can be also easily and flexibly used for batch processing, which is often leveraged for ML use cases.