Managing data across a fragmented landscape that includes data lakes, data warehouses, and other data sources requires a unified access layer. For many organizations operating within the Amazon Web Services (AWS) ecosystem, Amazon Athena is the query engine of choice for this task. It provides a serverless, pay-per-scan model that allows you to run SQL queries against data stored in Amazon S3 and other sources without managing any underlying infrastructure. Its zero-management setup makes it an ideal starting point for quick data exploration and early analytical projects.
But there’s a problem, and it involves data access.
Many organizations deploy more than one data source. This creates a problem because as the number of data sources grows and the need for multi-cloud or hybrid-cloud access increases. In these scenarios, the limitations of the Amazon Athena model become more apparent. Because of this, you may find that the performance, cost predictability, and connector depth required for enterprise-grade analytics necessitate looking at alternatives. In these situations, moving beyond a basic query service is often the first step toward building a comprehensive open data lakehouse solution.
Let’s walk through it.
TL;DR
* Amazon Athena is an effective query engine for ad hoc queries on Amazon S3 but faces performance and cost challenges at enterprise scale due to its multi-tenant architecture and pay-per-scan pricing.
* Starburst, including both Starburst Galaxy and Starburst Enterprise, offer a high-performance alternative to Amazon Athena with over 50 native connectors, multi-cloud support based on Trino, and advanced optimization features like data ingestion, table maintenance, and performance enhancements.
* Choosing an alternative depends on whether you require low-latency performance, cross-cloud federation, or the foundations of an open data lakehouse solution.
Understanding the Amazon Athena federation model
First, let’s start with the basics. What is Amazon Athena?
Amazon Athena is a serverless, interactive query service that lets you analyze data directly in Amazon S3 using standard SQL. It is built on a foundation of Trino, and offers strong, scalable performance for that reason. For data federation, Amazon Athena uses a feature called Federated Query, which relies on AWS Lambda to connect to approximately 30 different data sources. When you execute a query that involves an external database, such as a PostgreSQL instance or Snowflake, Amazon Athena triggers a Lambda function to fetch the data.
This architecture has pros and cons. It is particularly convenient for occasional, exploratory queries because it allows you to quickly connect to a new source and begin analyzing data without setting up complex data pipelines, and Athena has many users that fall within that use case.
The pricing model is also straightforward, which can be an appeal for certain types of users. For example, Athana charges $5 per terabyte of data scanned from Amazon S3, with a 10MB minimum per query. However, for federated sources, you also incur the costs associated with AWS Lambda execution and any data transfer fees, which can add costs.
While this model works for small-scale or intermittent use, it introduces several variables that can complicate production workloads. The reliance on AWS Lambda means you are subject to cold starts and concurrency limits. The “black box” nature of the engine means you have limited control over how queries are optimized or prioritized, which can lead to inconsistent performance during peak usage times.
The limitations of Amazon Athena for enterprise federation
As your data strategy matures, you may encounter several friction points with Amazon Athena that drive the need for an alternative, and these are quite common complaints about Athena from users. Overall, these challenges generally fall into three categories:
- Performance
- Cost
- Ecosystem flexibility
Let’s look at each of these one by one.
Noisy neighbor and performance caps
First, performance can be an issue for Athena. Because the service operates as a multi-tenant service, your queries share resources with other AWS customers. This can result in the so-called “noisy neighbor” issue where your query latency fluctuates based on the overall load on the AWS region. For critical business reporting or interactive dashboards, this lack of performance consistency is often unacceptable.
Additionally, Amazon Athena lacks advanced performance features such as indexing or sophisticated caching, which limits its ability to handle high-concurrency workloads. In contrast, Starburst Galaxy can be up to 10.2x faster than Amazon Athena on equivalent SQL. This speed difference becomes critical when moving from ad hoc exploration to production-grade data products.
Scan surprises and cost unpredictability
Costs are another typical area of friction. Athena’s pay-per-scan model is a double-edged sword. While it is cost-effective for small datasets, it can lead to significant surprises when users run inefficient queries against large tables. A single poorly written SQL statement that triggers a full table scan on a multi-terabyte dataset can result in an unexpectedly high bill.
In an enterprise environment where many users are querying the data, managing these costs becomes a significant administrative burden. Without the ability to set hard limits on compute resources or isolate workloads, a single department can consume a disproportionate amount of the data budget.
AWS-centricity and limited connectors
Finally, ecosystem flexibility is a major downside for Athena. As an Amazon product, Athena is designed primarily for the AWS ecosystem. While it can connect to some external sources via Lambda, it is not a true multi-cloud federation engine. If your organization has significant data footprints in Google Cloud or Microsoft Azure, querying that data through Amazon Athena is often inefficient or requires moving data into Amazon S3 first.
The library of approximately 30 connectors is smaller than what is available in dedicated federation platforms. This may leave gaps in your data architecture, especially if you rely on modern SaaS applications or specialized NoSQL databases that are not natively supported by the Athena Federated Query framework.
Starburst as a high-performance federation alternative
Starburst exists as the primary alternative to Amazon Athena in the marketplace. Like Athena, it is built on Trino, but also includes a number of proprietary enhancements. It is designed specifically to address the performance and scale requirements of large enterprises. Whether you choose the fully cloud-based Starburst Galaxy or the self-managed Starburst Enterprise for hybrid workloads, the platform provides a more robust approach to data federation than Amazon Athena.
Let’s look at the same three factors we did with Athena: performance, cost, and ecosystem federation.
Performance and query optimization
When comparing performance, the core difference comes down to architecture. Amazon Athena operates as a multi-tenant, serverless service where your queries share compute pools with other AWS customers, frequently causing latency spikes and execution bottlenecks.
Starburst eliminates these performance caps by providing dedicated, single-tenant compute clusters. Instead of routing federated queries through serverless AWS Lambda functions, which introduces cold-start delays and concurrency limits, Starburst uses native connectors that execute directly on cluster nodes. This allows for aggressive query pushdown, executing filters and joins directly inside the source database before transferring data over the network.
For workloads requiring extreme speed, Starburst Galaxy includes Warp Speed. This feature analyzes query patterns in real time, automatically building local SSD caches and smart indexes on hot data without modifying underlying S3 files.
By combining dedicated compute nodes, native connectors, and autonomous indexing, Starburst runs up to 10.2 times faster than Amazon Athena on equivalent SQL workloads. This performance advantage is critical for high-concurrency environments, such as interactive dashboards where sub-second response times are mandatory.
Predictable pricing and workload isolation
Cost management is another area where the architectural difference between Amazon Athena and Starburst becomes clear. Athena uses a pay-per-scan model where you are billed five dollars for every terabyte of data scanned. While this pricing seems low for small tasks, it introduces severe budget volatility in production environments. A single poorly written query or an unoptimized full table scan can trigger unexpected, astronomical charges.
Starburst solves this unpredictability by using a credit-based consumption model tied directly to compute uptime and cluster size rather than raw data scanned. Instead of worrying about how many terabytes an ad hoc analyst query touches, you pay for active compute resources.
To keep costs under control, Starburst provides built-in resource management features like cluster autoscaling and aggressive auto-suspend settings. Clusters automatically scale up to handle sudden query spikes, scale back down as demand cools, and automatically suspend when idle so you never pay for unused compute capacity.
This credit model also enables true workload isolation. In a shared environment like Athena, a resource-heavy report can degrade query speeds for everyone else. With Starburst, platform administrators can provision isolated clusters for specific teams or departments. The data science team can run heavy exploratory jobs on a dedicated cluster without impacting the performance or budget of real-time executive dashboards.
By combining predictable credit pricing, automated cluster scaling, and isolated compute pools, Starburst gives organizations total financial transparency. In fact, Starburst can be up to 65% cheaper than traditional cloud warehouses and query engines for AWS analytical workloads. This cost predictability allows data leaders to grant broad access across the organization without fearing end-of-month invoice surprises.
Native connectors, pushdown, and multi-cloud federation
Ecosystem flexibility is the third area where the difference between Amazon Athena and Starburst is significant. Amazon Athena relies on AWS Lambda functions to execute federated queries against external sources. Routing data requests through Lambda introduces cold-start latency, memory caps, and concurrency limits, making cross-source joins slow and expensive.
Starburst eliminates the Lambda bottleneck by using native high-performance connectors that execute directly on the cluster’s worker nodes. These connectors support advanced query pushdown optimizations, pushing projection, filtering, and aggregation logic down into the underlying source database before any data moves over the network. This minimizes network traffic and frees compute nodes to focus on final result aggregation.
To support complex enterprise architectures, Starburst offers over 50 native connectors covering cloud data warehouses, relational databases, NoSQL systems, and streaming platforms. Analysts can run a single ANSI SQL query that joins live operational data in PostgreSQL, historical tables in Snowflake, and unstructured archives in Amazon S3 without moving a single row into a central repository.
This broad connectivity provides true multi-cloud and hybrid-cloud federation. You can query data across AWS, Microsoft Azure, and Google Cloud Platform from a unified interface without copying files between cloud storage buckets or risking vendor lock-in.
By delivering native connector pushdown, broad enterprise source support, and cross-cloud execution, Starburst serves as a key foundation for an open data lakehouse. Data remains in its optimal, cost-effective storage location while remaining instantly accessible for business intelligence and AI workloads.
Choosing the right path for your data architecture
The decision to move beyond Amazon Athena usually occurs when serverless SQL limitations, unpredictable pay-per-scan costs, and query latency begin impacting core business operations. While Athena serves as an entry-point tool for basic ad hoc checks, enterprise analytics requires an engine built for performance, scale, and multi-cloud flexibility.
When to stay with Amazon Athena
Amazon Athena is sufficient only if your data resides almost entirely inside Amazon S3, your query volume is low or intermittent, and your team has no strict requirements for low-latency query execution. It functions well as a lightweight, zero-management utility for data engineers running quick quality checks or analysts generating occasional, non-critical reports.
Why enterprise workloads require Starburst
For production analytics, interactive business intelligence, and AI workloads, Starburst provides the high-performance foundation that serverless tools cannot match. You should upgrade to Starburst when your organization faces any of the following enterprise requirements:
- Multi-cloud and hybrid federation: You need to execute seamless ANSI SQL queries across AWS, Microsoft Azure, and Google Cloud Platform without expensive data movement or vendor lock-in.
- High query concurrency: You need to support dozens or hundreds of concurrent users accessing the same datasets through business intelligence tools without performance degradation.
- Sub-second performance SLAs: You require sub-second or low-second response times for interactive executive dashboards powered by Warp Speed indexing and local SSD caching.
- Deep native connectivity: You need to join data across complex enterprise environments using over 50 native connectors rather than relying on slow AWS Lambda wrappers.
- Predictable cost management: You want to eliminate pay-per-scan billing surprises by using credit-based pricing, isolated compute clusters, and cluster autoscaling.
By replacing serverless bottlenecks with Starburst, data-driven enterprises eliminate unnecessary ETL pipelines, achieve up to 10.2 times faster query execution, and provide analysts with fast, governed access to data wherever it lives.
Ready to modernize your data federation strategy? Download The Data Engineer’s Guide to Reducing Your Cloud Data Warehouse Spending to discover how combining open table formats with Starburst delivers warehouse-grade query performance at a fraction of the cost.
