Cloudflare flipped a switch on October 1, 2026, and quietly reshaped the serverless analytics market. The product formerly known as the Cloudflare Data Platform now ships as Cloudflare Basin, a generally available stack that bundles ingestion pipelines, an Apache Iceberg catalog, and SQL query execution directly on top of R2 object storage, as Cloudflare detailed in its launch post. The headline number making the rounds is $2.50 per terabyte of data scanned by Basin SQL, roughly half of what AWS Athena charges for the same job. That comparison is useful, but it is also incomplete, because Athena and Basin are not solving quite the same problem, and Google BigQuery adds a third model entirely: a managed data warehouse that now also speaks Iceberg natively.
This piece pulls apart the pricing, the architecture, and the practical tradeoffs between Cloudflare Basin, AWS Athena (paired with AWS Glue Data Catalog), and Google BigQuery’s Lakehouse for Apache Iceberg. All three now let you run SQL over data stored as open Iceberg tables, which means the real decision is no longer “which engine is fastest” but “which billing model and ecosystem fit your workload.” We modeled five realistic workloads against each platform’s published list pricing, checked the Iceberg interoperability claims against official documentation, and laid out a migration path for teams currently locked into Athena or BigQuery costs that keep climbing.
The stakes here go beyond one vendor’s launch-day marketing. Apache Iceberg has become the format every major data platform now assumes you are already using, or will be soon, which means the real competitive battlefield has shifted from “whose storage format wins” to “whose compute meter is cheapest to point at the same open tables.” That shift is exactly what makes a three-way comparison worth running in detail rather than taking any single vendor’s press release at face value.
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What is Cloudflare Basin, and why did it launch now?
Basin is not a new object store. It sits on top of R2, Cloudflare’s existing egress-free storage service, and adds three things R2 never had on its own: a managed ingestion layer called Pipelines, an Iceberg table catalog (renamed from R2 Data Catalog to Basin Catalog), and a SQL query engine billed by the gigabyte scanned. Cloudflare’s own developer documentation describes the move plainly: “Cloudflare Data Platform is now Basin, bringing generally available ingestion, Iceberg table management, and SQL analytics together on R2,” according to the Basin pricing documentation published alongside the GA announcement.
The timing matters. Kubernetes 1.34 reaches upstream end of life later this month, cloud egress costs have been under scrutiny for years, and every major vendor is racing to support Apache Iceberg as the lingua franca of lakehouse table formats. Cloudflare’s pitch is that Basin Catalog exposes a standard Iceberg REST Catalog interface, the same protocol that tools like PyIceberg, DuckDB, Snowflake, and Apache Spark already know how to talk to. That is a meaningfully different strategy than building a closed, proprietary warehouse. If the REST Catalog claim holds up in production, Basin tables should be queryable from outside Cloudflare’s network, which is the opposite of the vendor lock-in that has defined cloud data warehousing for the past decade.
It is worth being honest about what is not yet known. Cloudflare’s own press materials, confirmed in the company’s official GA press release, describe the intended use case: ingest streaming or batch data, maintain it as Iceberg tables automatically, and run SQL against it without managing infrastructure. What the release does not include is a list of named production customers, published query-volume numbers, or independent third-party benchmarks. Basin launched eight days before this article, so treat any adoption claim you see elsewhere with the same skepticism you would apply to any week-old product.
The ingestion layer deserves a closer look, since it is the piece that did not exist in any form on R2 before Basin shipped. Pipelines, Basin’s managed ingestion service, accepts streaming or batch writes and is reported to handle up to 3 gigabytes per second per stream before automatically converting incoming data into Iceberg table format. Ingestion itself carries no direct charge, but moving that data onward through Basin’s delivery layer does: Cloudflare’s published rate card lists $0.03 per gigabyte for JSON delivery into R2 and $0.06 per gigabyte for Parquet or Iceberg-format delivery, separate from the SQL query charge covered later in this article. For teams that previously stitched together a homegrown combination of Kafka, a batch job, and a cron-triggered compaction script just to keep an Iceberg table tidy, collapsing that into one managed pipeline is arguably a bigger deal than the query-price headline, even if it generates fewer attention-grabbing comparisons.
AWS Athena and Glue: the incumbent serverless query stack
Athena has been AWS’s answer to “run SQL over S3 without a cluster” since 2016, and it remains the default choice for teams already inside the AWS ecosystem. The pricing model is simple on paper: $5 per terabyte of data scanned, rounded up to the nearest megabyte, with a 10 MB minimum charge per query, according to AWS’s current Athena pricing page. Teams running federated queries against external data sources pay the same per-terabyte rate across every source the query touches, unless they opt into Athena Provisioned Capacity, which switches billing to $0.30 per DPU-hour for predictable, high-volume workloads.
What catches teams off guard is that the $5-per-terabyte figure is only the compute charge. Athena also bills standard Amazon S3 storage, request, and data-transfer rates on top, and if you use the AWS Glue Data Catalog to track table schemas, that is a separate line item. Glue’s catalog pricing is generous at small scale: the first million metadata objects stored are free, and the first million catalog accesses per month cost nothing. Beyond that, AWS charges $1 per 100,000 additional objects per month and $1 per million additional accesses, per the AWS Glue pricing page. For a team running a handful of tables with modest query traffic, catalog costs round to zero. For a company cataloging millions of partitions across hundreds of tables, that line item adds up fast.
Athena’s biggest strength is maturity. It has been production-hardened for nearly a decade, integrates natively with every AWS analytics and BI tool, and now supports Apache Iceberg tables directly, including time travel and schema evolution. The tradeoff is that none of that comes free of AWS’s broader pricing gravity: S3 egress to anywhere outside AWS still runs roughly $0.09 per gigabyte for the first 10 terabytes each month, a fee that Cloudflare has spent years positioning R2 and now Basin against.
Athena Provisioned Capacity is worth a closer look for any team already running enough query volume to feel the pay-per-scan model add up. At $0.30 per DPU-hour, a sustained workload that would otherwise burn through thousands of dollars a month in scan charges can often be reserved at a flatter, more predictable rate, the same logic that pushes heavy BigQuery users toward Editions capacity pricing instead of on-demand billing. The crossover point depends entirely on how consistently your queries run through the day. A team querying in short, unpredictable bursts rarely benefits from provisioned capacity, while a team running near-continuous dashboards or scheduled batch jobs almost always does. Neither Athena nor BigQuery publishes a universal breakeven number, because it depends on DPU or slot utilization, which is specific to each account’s query patterns.
Google BigQuery and Lakehouse for Apache Iceberg: the warehouse-native approach
BigQuery takes a third path. Rather than bolting a query engine onto raw object storage, it is a fully managed data warehouse that has spent the last year extending itself to also read and write open Iceberg tables, under a product line Google now calls Lakehouse for Apache Iceberg, previously branded BigLake. On-demand query pricing is $6.25 per tebibyte of data processed, with the first 1 tebibyte of query data per month free for every billing account, confirmed on Google Cloud’s official BigQuery pricing page. Storage follows a two-tier logical model: active storage (tables modified within the last 90 days) costs $0.000031507 per gibibyte-hour, which works out to roughly $23 per terabyte-month, while long-term storage that has not been touched in 90 days automatically drops to about $16 per terabyte-month. The first 10 gibibytes of storage are free each month.
The Iceberg story is the newest and most relevant wrinkle for this comparison. Google’s current lakehouse documentation describes BigLake Iceberg tables as storing their underlying data in Cloud Storage rather than BigQuery’s proprietary format, meaning there is no separate BigQuery-specific storage fee for that data, just standard Cloud Storage rates. Google has also rolled out a preview feature called Lakehouse runtime catalog federation, which lets BigQuery and Google-managed Spark discover and query tables across external catalogs, including AWS Glue, Databricks Unity Catalog, and Snowflake Horizon Catalog, according to Google’s lakehouse product documentation. That is Google explicitly building bridges to the exact ecosystems Athena and Basin also plug into, which tells you where the whole industry is headed: fewer walled gardens, more shared Iceberg tables queried by whichever engine happens to be cheapest for the job at hand.
BigQuery’s practical advantage is ecosystem depth. Looker, Data Studio, Vertex AI, and a decade of third-party BI integrations all assume BigQuery as a first-class citizen. Its disadvantage, for teams evaluating total cost, is that the warehouse model bundles compute and governance features that a pure pay-per-scan engine does not need to carry.
For teams with steadier workloads, BigQuery Editions offers a capacity-based alternative to on-demand billing, priced in slot-hours rather than per-terabyte-processed, with Standard, Enterprise, and Enterprise Plus tiers that trade flexibility for lower effective cost at scale. The Storage Write API’s change-data replication feature, announced earlier in 2026, lets teams stream inserts, updates, and deletes from an operational database directly into an Iceberg table in near real time, which removes the need for the kind of complex merge-based extract-transform-load pipelines that used to be standard practice for keeping a lakehouse table in sync with a production database. That single feature closes a real gap between BigQuery’s warehouse-native roots and the Iceberg-native workflows Basin and Athena both assume from day one.
Full specs comparison: Basin vs Athena vs BigQuery
| Attribute | Cloudflare Basin | AWS Athena + Glue | Google BigQuery (Lakehouse for Iceberg) |
|---|---|---|---|
| GA / current status | General availability October 1, 2026 | GA since 2016, Iceberg support added later | BigLake Iceberg support GA, catalog federation in preview |
| Underlying storage | Cloudflare R2 | Amazon S3 | Google Cloud Storage |
| Native table format | Apache Iceberg (via Basin Catalog) | Apache Iceberg, Hive, plus legacy formats | Apache Iceberg, BigQuery native storage |
| Catalog interface | Iceberg REST Catalog (per third-party integration reporting) | AWS Glue Data Catalog | BigQuery metastore, Iceberg REST Catalog tables |
| Query price per TB scanned | $2.50 | $5.00 | $6.25 per tebibyte (first 1 TiB/month free) |
| Minimum query charge | Not published as a flat minimum; billed per GB scanned | 10 MB minimum per query | Rounded to query bytes processed |
| Standard object storage price | $0.015/GB-month (R2 Standard) | ~$0.023/GB-month (S3 Standard, first 50 TB) | ~$0.023/GB-month active, ~$0.016/GB-month long-term |
| Egress fees | Free, including cross-cloud movement | ~$0.09/GB for first 10 TB/month | Standard Cloud Storage egress applies |
| Catalog metadata pricing | First 1M operations/month free, then $9.00/million operations | First 1M objects free, then $1/100K objects/month | Bundled into BigQuery storage/compute billing |
| Ingestion pricing | Free via Pipelines; delivery fees apply by format | Standard S3 PUT/ingestion costs | Streaming inserts and Storage Write API billed separately |
| Reserved / capacity pricing option | Not published at GA | Provisioned Capacity, $0.30/DPU-hour | Capacity pricing via BigQuery Editions (slot-hours) |
| Cross-engine compatibility | PyIceberg, DuckDB, Snowflake, Apache Spark (per Cloudflare) | Spark, Trino, Presto, Redshift Spectrum, and the broader AWS analytics stack | Spark, Trino, and catalog-federated AWS Glue / Unity Catalog / Snowflake Horizon tables (preview) |
| Best documented track record | Eight days of public GA history as of this article | Nearly a decade of production use | Years of production use plus a year of active Iceberg investment |
Pricing breakdown: query costs, storage costs, and egress fees
Stack the three pricing models side by side and the pattern becomes obvious: Basin undercuts both incumbents on the two line items that scale fastest as data volume grows, the per-terabyte query charge and the per-terabyte-month storage charge. Athena and BigQuery compete with each other more closely than either competes with Basin, at least on the sticker price of those two components.
| Cost component | Cloudflare Basin | AWS Athena | Google BigQuery |
|---|---|---|---|
| Query / scan cost | $2.50 per TB scanned | $5.00 per TB scanned | $6.25 per TiB processed (1 TiB/month free) |
| Storage cost (standard tier) | ~$15 per TB-month | ~$23 per TB-month | ~$23 per TB-month (active) |
| Storage cost (cold/long-term tier) | ~$10 per TB-month (R2 Infrequent Access) | Separate S3 IA/Glacier tiers required | ~$16 per TB-month (automatic after 90 days idle) |
| Egress to another cloud or the public internet | $0 | ~$90 per TB (first 10 TB/month) | Standard Cloud Storage egress rates apply |
| Catalog overage (beyond free tier) | $9.00 per million operations | $10 per million objects (per 100K increments) | Bundled, no separate catalog line item |
| Data delivery / ingestion format fee | $0.03/GB (JSON), $0.06/GB (Parquet/Iceberg) | Standard S3 PUT and transfer pricing | Storage Write API streaming rates apply |
Two caveats keep this table honest. First, Basin’s storage figures are really R2’s figures, since Basin does not charge a separate markup on top of the object storage it runs on, according to Cloudflare’s published pricing documentation. Second, none of these numbers include request-level charges, which differ enough by access pattern that a workload doing millions of small reads could shift the comparison meaningfully. Treat this table as a starting point for your own calculation, not a final invoice.
The hidden cost problem: why egress fees quietly decide the winner
Query price and storage price get all the attention in headline comparisons, but egress is where multi-cloud and multi-tool analytics budgets actually blow up. Every time a BI tool outside the cloud provider’s own network pulls a dataset, every time a data science team exports a training set to a different platform, every time a disaster-recovery job copies data to a second region, that traffic hits a per-gigabyte meter. AWS’s standard internet egress starts at roughly $0.09 per gigabyte for the first 10 terabytes each month. At even modest data science team scale, that is not a rounding error.
Basin inherits R2’s zero-egress policy, which Cloudflare has marketed aggressively since R2 launched, and which now extends to moving or querying data across clouds, platforms, or regions, per the Basin pricing documentation. That is the single biggest structural advantage Basin has over both Athena and BigQuery for teams whose analytics stack spans more than one vendor, which, in 2026, describes most mid-size and large engineering organizations. BigQuery’s egress situation is more nuanced. Data that never leaves Google Cloud, including cross-service reads within BigQuery and Cloud Storage, avoids the fee, but data pulled to an external BI tool, a different cloud, or an on-premises system hits standard Cloud Storage egress pricing.
The practical takeaway: if your organization runs a single-cloud stack end to end, egress barely factors into the decision. If you are querying data from a third-party dashboard, replicating to a second provider for resilience, or running a genuinely multi-cloud analytics layer, egress fees can flip the “cheapest platform” conclusion entirely, regardless of what the query and storage line items say.
Apache Iceberg compatibility: how interoperable is each platform, really?
All three platforms now claim Iceberg support, but “support” means different things depending on the vendor. Basin Catalog, formerly R2 Data Catalog, is described in Cloudflare’s own changelog as managing and automatically maintaining Apache Iceberg tables so they stay fast, cost-efficient, and accessible to any compatible query engine, per the October 1 Basin changelog entry. Third-party technical reporting on the launch adds that Basin Catalog exposes a standard Iceberg REST Catalog interface, the same protocol PyIceberg, DuckDB, Snowflake, and Apache Spark already implement. If that holds up under real production load, it means a team could write data through Basin and query it from a completely different engine without a migration step, which is the entire promise of open table formats.
AWS Athena’s Iceberg support is the most battle-tested of the three, since AWS Glue Data Catalog has supported Iceberg tables for years and the broader AWS analytics stack, including Redshift Spectrum, EMR, and Spark on EMR, all read the same catalog. Google’s approach has moved fastest in the last twelve months: BigQuery now supports Iceberg REST Catalog tables directly, and the newer Lakehouse runtime catalog federation feature, currently in preview, lets BigQuery and Google-managed Spark discover and analyze tables sitting in AWS Glue, Databricks Unity Catalog, and Snowflake Horizon Catalog without copying the data first. That is Google explicitly reaching into competitors’ catalogs, which is a notable concession that no single vendor’s lock-in strategy survives Iceberg’s rise intact.
What none of the three vendors has published is a controlled, independent benchmark showing query latency or throughput when, say, Athena reads a Basin-managed Iceberg table over its REST Catalog, or BigQuery federates into a Basin catalog. These integrations are new enough that cross-platform performance numbers simply do not exist in public sources yet. Anyone citing a specific millisecond figure for cross-catalog Iceberg queries right now is extrapolating, not reporting.
Benchmark data: what the public record actually shows
Three independent sources anchor the pricing comparisons in this article. AWS’s own Athena pricing page confirms the $5-per-terabyte-scanned rate and the 10 MB per-query minimum. Data-engineering analysis published by OWOX confirms the same Athena figure, checked directly against the live pricing page in October 2026, and adds that heavy, steady workloads typically do better switching to Athena’s provisioned-capacity model rather than staying on pay-per-scan. Google’s own BigQuery cost-optimization whitepaper states the on-demand rate as “Starting at $6.25 per TB” with “First 1TB per month free (US multi-region),” matching the figure on Google’s live pricing page.
For Basin, the $2.50-per-terabyte-scanned figure comes directly from Cloudflare’s published Basin pricing documentation, which lists the rate as $0.0025 per gigabyte of SQL data scanned. That figure is explicitly for SQL query execution, not for storing data, a distinction that got blurred in some early coverage of the launch that implied Basin was a flat $2.50-per-terabyte object storage competitor to S3. It is not. Basin’s storage still runs through R2 at R2’s separate published rate. Once you separate query cost from storage cost cleanly, as the tables above do, the three-way comparison holds up: Basin is cheaper to query and cheaper to store data in than either Athena or BigQuery, at published list-price rates, as of this article’s publication date.
Five real-world cost scenarios
Numbers mean more with context. Below are five modeled workloads, built from published list pricing for each platform. These are illustrative estimates, not quotes from any vendor’s billing calculator, and they exclude request-level, API, and regional pricing variation that a real invoice would include.
| Workload | Monthly scan / storage | Basin (modeled) | Athena (modeled) | BigQuery (modeled) |
|---|---|---|---|---|
| Mobile gaming telemetry pipeline | 20 TB scanned, 50 TB stored | ~$800/mo | ~$1,250/mo | ~$1,269/mo |
| E-commerce clickstream analytics with BI export | 100 TB scanned, 200 TB stored, 10 TB egress | ~$3,250/mo | ~$6,000/mo | ~$6,120/mo |
| IoT sensor fleet time-series analysis | 150 TB scanned, 80 TB stored | ~$1,575/mo | ~$2,590/mo | ~$2,771/mo |
| Ad-tech bid log analysis at scale | 500 TB scanned, 300 TB stored | ~$5,750/mo | ~$9,400/mo | ~$10,020/mo |
| Fintech transaction audit archive (cold, rarely queried) | 2 TB scanned, 500 TB stored on cold tier | ~$5,005/mo | ~$11,510/mo (standard S3 tier) | ~$8,006/mo (long-term storage discount) |
The pattern holds across every scenario: Basin’s combination of a lower per-terabyte scan rate and free egress compounds quickly once data volume and cross-platform movement both grow. The gap narrows for the cold-archive scenario once BigQuery’s automatic long-term storage discount kicks in, which is the one case where Google’s warehouse pricing pulls ahead of Athena’s flat S3 Standard rate, a reminder that the cheapest platform depends heavily on how your data actually gets accessed, not just how much of it you have.
How to calculate your own monthly bill: a worked example
Rather than trusting any vendor comparison, including this one, it helps to know the actual arithmetic so you can run your own numbers against your own query logs. Take the e-commerce clickstream scenario from the table above: 100 TB scanned per month, 200 TB stored, and 10 TB of monthly egress to a third-party BI tool.
On Cloudflare Basin, the math is: 100 TB scanned x $2.50/TB = $250 in query charges. Storage at 200 TB x $15/TB-month = $3,000. Egress is free regardless of destination, so that line item is $0. Total: approximately $3,250 per month, before any catalog-overage or data-delivery charges, which stay negligible at this scale.
On AWS Athena, the same workload breaks down as: 100 TB scanned x $5.00/TB = $500 in query charges. Storage at 200 TB x roughly $23/TB-month (S3 Standard, first 50 TB tier blended with the lower rate above that) = approximately $4,600. Egress for 10 TB at roughly $0.09/GB works out to around $900, since the per-account 100 GB free allowance barely dents a 10 TB transfer. Total: approximately $6,000 per month, before Glue Data Catalog charges, which again stay close to zero unless the table count runs into the hundreds of thousands.
On Google BigQuery, the on-demand math is: 100 TiB scanned minus the 1 TiB free allowance leaves 99 TiB billable, at $6.25/TiB, for approximately $619 in query charges. Storage at 200 TiB x roughly $23/TiB-month (assuming the data stays active, meaning it is modified within a rolling 90-day window) comes to approximately $4,600. Egress for 10 TB of data leaving Google Cloud runs at a broadly comparable rate to AWS’s, landing around $900 depending on destination and region. Total: approximately $6,120 per month.
Line up all three and the ranking from this article’s modeled table holds: Basin comes in roughly 46% below Athena and 47% below BigQuery for this specific workload, driven almost entirely by the egress line item disappearing and the storage rate running about a third lower. Change the egress volume to zero, and the gap shrinks to the pure query-and-storage difference, which is still meaningful but far less dramatic. That is the single most important variable to isolate when you run this calculation against your own account: how much data actually leaves the platform each month, because that number swings the comparison more than any other line item in the entire bill.
Use case recommendations: which platform fits your workload
- Already running on Cloudflare’s edge or using R2 for storage: Basin is the natural extension, since it adds query and catalog capability on top of storage you may already pay for, with no egress penalty for pulling results into other tools.
- Deeply embedded in the AWS analytics stack: Athena remains the pragmatic choice if your pipelines already depend on Glue, Redshift Spectrum, or EMR, since switching engines mid-stack costs more in engineering time than it saves in query fees at most data volumes.
- Running BI dashboards and data science workflows through Looker, Vertex AI, or other Google-native tools: BigQuery’s ecosystem depth outweighs the per-terabyte price gap for teams that lean on its built-in ML and visualization integrations daily.
- Operating a genuinely multi-cloud analytics layer: Basin’s free egress and open Iceberg REST Catalog make it the cheapest place to park a shared table that Athena, Spark, Snowflake, and BigQuery all need to read without each of them paying a transfer toll.
- Cost-sensitive startups scanning large, bursty datasets: the combination of Basin’s $2.50-per-terabyte scan rate and no minimum-query floor tends to favor small, frequent queries over Athena’s 10 MB-minimum billing model.
- Enterprises with steady, predictable, very high query volume: both Athena Provisioned Capacity and BigQuery Editions capacity pricing can undercut any pay-per-scan model, including Basin’s, once query volume is consistent enough to justify reserving dedicated compute.
Pros and cons of each platform
Cloudflare Basin
- Pros: lowest published query and storage rates of the three, free cross-cloud egress, native Iceberg REST Catalog, built on an already-popular storage layer in R2.
- Cons: eight days of public production history as of this article, no published capacity/reserved pricing tier yet, no independent benchmarks confirming performance at scale, thinner BI and visualization ecosystem than AWS or Google.
AWS Athena + Glue
- Pros: nearly a decade of production maturity, deep integration across the entire AWS analytics and BI stack, mature Iceberg support, provisioned capacity option for predictable billing.
- Cons: highest per-terabyte scan price of the three, separate S3 storage and Glue catalog charges stack on top of query cost, standard AWS internet egress applies to any data leaving the network.
Google BigQuery
- Pros: unmatched BI and ML ecosystem integration, automatic long-term storage discount for cold data, fastest-moving Iceberg catalog federation roadmap of the three vendors, generous 1 TiB/month free query allowance.
- Cons: highest headline per-terabyte-processed query price, catalog federation with external systems is still in preview, warehouse-first architecture carries more overhead than a pure pay-per-scan engine for lightweight workloads.
Migration guide: moving an existing Athena or BigQuery workload to Basin
Because all three platforms increasingly speak Apache Iceberg, migration is less about rewriting pipelines and more about repointing catalogs and validating compatibility. The steps below outline a conservative path for a team currently running Iceberg tables on Glue or BigLake who wants to evaluate Basin without a risky cutover.
- Inventory your existing Iceberg tables and confirm which ones are already in open Iceberg format versus a proprietary warehouse-native format that would need conversion first.
- Stand up a Basin project and connect R2 as the target storage layer, following Cloudflare’s Basin setup documentation.
- Use a tool that already speaks the Iceberg REST Catalog protocol, such as PyIceberg or Spark, to copy a single non-critical table into Basin Catalog as a test case.
- Point a read-only query, run through DuckDB or Spark, at the migrated table through Basin’s REST Catalog endpoint and confirm the results match the source system exactly.
- Benchmark query latency and scan cost on the test table for at least a week of real traffic before drawing conclusions, since a single query is not a representative sample.
- Set up Basin Pipelines to ingest new data in parallel with your existing pipeline, running both systems side by side rather than cutting over immediately.
- Validate that downstream BI tools or notebooks can connect to Basin’s query endpoint with the same credentials and access model your team already uses.
- Compare the actual invoiced cost from the parallel-run period against your Athena or BigQuery bill for the same data volume and query pattern, not against the published list-price estimate alone.
- Once cost and correctness are both confirmed, migrate ingestion for the remaining tables in batches, prioritizing the highest-volume, highest-egress tables first since those see the largest savings.
- Decommission the parallel pipeline only after a full billing cycle has passed on Basin alone, to catch any seasonal or end-of-month query pattern that a shorter test window would miss.
A sample query against an Iceberg table through a REST Catalog endpoint, using DuckDB as the client, looks like this once the catalog connection is configured:
INSTALL iceberg;
LOAD iceberg;
ATTACH 'basin_catalog' AS basin (
TYPE ICEBERG,
ENDPOINT 'https://catalog.basin.example/v1',
WAREHOUSE 'analytics'
);
SELECT
event_date,
COUNT(*) AS event_count,
SUM(revenue_cents) / 100.0 AS revenue_usd
FROM basin.events.transactions
WHERE event_date >= DATE '2026-09-01'
GROUP BY event_date
ORDER BY event_date;The exact endpoint and warehouse names will differ per account, but the pattern, attaching an Iceberg REST Catalog and querying tables inside it, stays consistent whether the catalog lives in Basin, Glue, or a BigQuery-federated external source.
Security, governance, and catalog federation
Governance is where the three platforms diverge the most, and it is an area where published, verifiable detail is thinnest for the newest entrant. AWS Glue Data Catalog has a mature permissions model through AWS Lake Formation, letting teams set column-level and row-level access controls across every service that reads the catalog. Google’s BigQuery carries its own IAM-integrated access controls plus the newer catalog federation layer, which currently operates in preview and is explicitly designed to let BigQuery discover tables in AWS Glue, Databricks Unity Catalog, and Snowflake Horizon Catalog without duplicating the data.
Basin’s governance model is the least documented in public sources at the time of this article’s publication, which is a fair reflection of a product that is one week into general availability rather than a criticism of its design. Teams evaluating Basin for anything beyond a proof of concept should treat access control, audit logging, and compliance certification as open questions to verify directly against Cloudflare’s current documentation before committing production data, rather than assuming parity with AWS or Google’s decade-plus of compliance tooling.
Catalog federation is the feature to watch across all three vendors over the next year, because it is the mechanism that determines whether “open” table formats actually deliver on the promise of avoiding lock-in. Google’s preview-stage federation into AWS Glue, Databricks Unity Catalog, and Snowflake Horizon Catalog is the most ambitious public commitment so far, since it means a BigQuery user could theoretically query a table that lives entirely inside AWS’s catalog without copying a single byte. Whether Cloudflare extends Basin Catalog with equivalent federation into Glue or Unity Catalog, rather than just exposing its own tables outward through the Iceberg REST Catalog standard, will say a lot about whether Basin is positioning itself as a full warehouse competitor or as a cheaper storage-and-compute layer that plugs into whichever catalog a customer already uses elsewhere.
The verdict: which serverless analytics platform wins in 2026
On pure published pricing, Cloudflare Basin wins. Half the query cost of Athena, roughly a third cheaper storage than either competitor, and zero egress fees add up to the lowest modeled total cost in four of the five workload scenarios in this article, and a close second in the fifth. For any team building new analytics infrastructure from scratch, especially one that already touches Cloudflare’s network or needs to share Iceberg tables across more than one cloud, Basin is worth a serious pilot.
But price is not the only variable that matters, and a one-week-old GA product carries risk that a cost spreadsheet does not capture. AWS Athena remains the safer default for teams already standardized on AWS, with a track record and ecosystem depth Basin cannot match yet. Google BigQuery remains the strongest choice for organizations whose real bottleneck is BI and machine learning integration rather than raw query cost, and its aggressive push into Iceberg catalog federation suggests Google intends to compete on openness as well as price going forward. The honest conclusion: Basin’s economics are compelling enough to justify a pilot project today, but the decision to move production workloads should wait for a few more months of public operating history, independent benchmarks, and clarity on governance tooling before betting a company’s core analytics pipeline on the newest player in a three-way race that is still very much open.
Frequently asked questions
Is Cloudflare Basin a replacement for AWS S3 or Google Cloud Storage?
No. Basin is a query and catalog layer built on top of Cloudflare R2, which is the actual object storage service. Comparing Basin directly to S3 as a storage product, rather than comparing it to Athena as a query engine, is a common but incorrect framing that circulated after the GA launch.
Does Cloudflare Basin really cost 50% less than AWS Athena?
On the query-scan line item specifically, yes: $2.50 per terabyte scanned on Basin versus $5.00 per terabyte scanned on Athena, per each vendor’s published pricing. That does not automatically mean a 50% reduction in total bill, since storage, catalog, and egress charges all factor in separately and move the final number up or down depending on the workload.
Can AWS Athena or BigQuery query a table stored in Cloudflare Basin?
In principle, yes, since Basin Catalog is reported to expose a standard Iceberg REST Catalog interface that compatible engines can connect to. In practice, no public, independently verified benchmark currently confirms production-grade cross-platform query performance between Basin and either Athena or BigQuery, so teams should test this directly before relying on it.
What is the AWS Glue Data Catalog free tier?
The first one million metadata objects stored and the first one million catalog accesses per month are free. Beyond that, AWS charges $1 per 100,000 additional objects per month and $1 per million additional accesses, according to AWS’s published Glue pricing page.
Why did BigQuery rename BigLake to Lakehouse for Apache Iceberg?
Google’s current product documentation lists Lakehouse for Apache Iceberg as the current name for the product line previously called BigLake, reflecting Google’s shift toward positioning the offering specifically around Iceberg interoperability rather than a broader, less specific lakehouse brand.
Is Cloudflare Basin’s free egress really unconditional?
Cloudflare’s published pricing documentation states that Basin does not charge egress fees for moving or querying customer data across clouds, platforms, or regions. As with any new product, teams with unusually high-volume or unusual access patterns should confirm current terms directly with Cloudflare before assuming the policy applies without limits to every use case.
Should a startup choose Cloudflare Basin over Athena or BigQuery today?
For a greenfield project, especially one already using Cloudflare’s network or R2 storage, Basin is worth piloting given its lower published query and storage costs. For a startup with an existing, working Athena or BigQuery pipeline, the migration effort should be weighed against the modeled savings for its specific data volume, since smaller workloads may not see enough absolute dollar savings to justify the engineering time.
Does switching to Basin lock a team into Cloudflare the way Athena locks teams into AWS?
Less so, at least on paper. Because Basin Catalog is built on the open Iceberg REST Catalog standard rather than a proprietary format, tables created in Basin should, in principle, be portable to any other Iceberg-compatible engine without a rewrite. That portability claim has not yet been stress-tested by the broader developer community given how recently Basin reached general availability.
