VAST DataBase Benchmarks: The Numbers We Can't Verify
VAST Data claims 25% faster than Iceberg, 60x faster updates, and 500M messages per second. The numbers are impressive—but without published methodology or independent verification, they remain marketing claims, not engineering data.
VAST Data has published benchmark results claiming substantial performance advantages over established solutions. Their DataBase, a component of the VAST Data Platform, reportedly achieves 25% faster query performance than Apache Iceberg while using 30% less CPU, 20x faster needle-in-a-haystack queries with 1/10th the CPU, and 60x faster updates and deletions compared to object-based solutions [1]. Their Event Broker claims 10x Kafka performance with over 500 million messages per second [2].
These are extraordinary claims. If accurate, they represent significant engineering achievements. The problem is that none of these benchmarks include published methodology, reproducible test configurations, or independent verification. They exist as marketing numbers without the technical documentation that would allow engineers to evaluate them.
The Claims in Detail
VAST’s benchmark claims span several categories, each with impressive multipliers:
Query Performance vs. Iceberg: VAST claims their DataBase completes TPC-DS benchmarks 25% faster than Apache Iceberg while using 30% less CPU. For selective queries, they claim 5-20x performance advantages with up to 90% less CPU utilization. Single-key queries allegedly show 100x acceleration, with multi-key patterns achieving 25x improvements [1].
Data Modification: Updates and deletions reportedly execute 60x faster than object-based solutions. This addresses a known limitation of immutable object storage, where modifications require rewriting entire objects or complex merge-on-read patterns [1].
Event Streaming: The VAST Event Broker claims over 500 million messages per second, described as 10x the performance of legacy Kafka implementations [2].
These multipliers—20x, 60x, 100x—would be remarkable if verified. They would indicate fundamental architectural advantages rather than incremental improvements.
What’s Missing
Every credible benchmark includes methodology documentation that allows independent evaluation. VAST’s published claims lack several critical elements:
Hardware configuration: What servers, storage, and network infrastructure were used? TPC-DS results vary dramatically with hardware. A benchmark on a single-node system differs from a distributed cluster. NVMe performance differs from spinning disk. The absence of hardware specifications makes comparison impossible.
Software versions: Which version of Iceberg was tested? Iceberg performance has improved substantially across releases. Testing against an outdated version produces misleading comparisons. Similarly, which Kafka version does the 500M messages/second claim compare against? Kafka 3.x performs differently than Kafka 2.x.
Test parameters: TPC-DS has multiple scale factors (1GB to 100TB). Which was used? What query set—all 99 queries or a subset? Were results warm cache or cold? What concurrency level? These parameters dramatically affect results.
Object sizes and patterns: The 60x faster updates claim references “object-based solutions” without specifying which ones, what object sizes, or what update patterns. A 4KB object update behaves differently than a 1GB object update.
Client configuration: The 500M messages/second claim requires understanding the client count, message sizes, acknowledgment settings, and partition counts. A single-partition benchmark differs from a multi-partition test. Fire-and-forget messaging differs from synchronous acknowledgment.
Without these details, the numbers cannot be reproduced, verified, or meaningfully compared to alternative solutions.
Why Methodology Matters
Consider VAST’s claim of 25% faster TPC-DS performance than Iceberg. To evaluate this:
An engineer would need to know the scale factor, because TPC-DS at SF1 fits in memory on most servers while SF100 requires distributed query execution. The Iceberg configuration matters—is it using Spark, Trino, or another engine? What file format (Parquet, ORC)? What partition scheme? The VAST configuration matters equally—what consistency level? What caching? What data placement?
These aren’t pedantic details. They determine whether a benchmark reflects production reality or a synthetic best-case scenario optimized for marketing.
Apache Iceberg publishes extensive documentation on performance characteristics [3]. The community maintains benchmark suites with reproducible configurations. When vendors like Databricks or Snowflake claim performance advantages, they typically publish detailed methodology or submit to independent benchmark organizations.
VAST’s approach—publishing multipliers without methodology—follows a pattern common in storage marketing but inconsistent with engineering credibility.
The Pattern of Unverified Claims
This isn’t VAST’s first appearance on StorageMath. Previous analyses examined VAST’s erasure coding claims, marketing positioning, and parallel file system assertions. A consistent pattern emerges: impressive numbers without the technical documentation that would allow verification.
The company has achieved real commercial success—reportedly reaching $2 billion in cumulative software revenue faster than any data infrastructure company in history, with a Net Promoter Score of 84 [4]. Commercial success, however, doesn’t validate technical claims. Customers may choose VAST for legitimate reasons (operational simplicity, sales relationships, risk tolerance) that don’t require benchmark claims to be accurate.
Independent analysts have noted this gap. DataPro observes that “data engineers should approach such claims with healthy skepticism until independent verification becomes available” [4]. TheCUBE Research notes that “VAST has not (yet) achieved a Databricks-style lakehouse, a Snowflake-grade cloud database, nor a hyperscaler data platform” and that the company’s database “remains primarily an index optimized for metadata and vectors rather than a full ANSI-SQL engine” [5].
What Verification Would Look Like
Credible benchmark claims follow established patterns:
Industry-standard benchmarks like TPC-DS, TPC-H, SPECstorage, or STAC-M3 have defined rules, auditing procedures, and published results. Weka, for example, publishes SPECstorage results that can be independently verified on spec.org [6]. Pure Storage submits to STAC-M3 auditing [7]. These results include full configuration disclosure and independent verification.
Published methodology allows reproduction. MinIO publishes their warp benchmark tool as open source, enabling anyone to validate claims independently. When MinIO claims specific throughput numbers, engineers can run the same tests on their own hardware.
Third-party testing from independent labs provides credibility that vendor-published numbers lack. The Storage Performance Council, SPEC, and STAC maintain benchmark integrity through defined processes and disclosure requirements.
VAST could address credibility concerns by submitting to any of these verification mechanisms. Until they do, their benchmark claims remain in a category distinct from verified engineering data.
The 500 Million Messages Question
The Event Broker claim deserves particular scrutiny. Claiming 500 million messages per second—10x Kafka performance—is extraordinary.
Apache Kafka, in well-tuned configurations, achieves millions of messages per second per broker. LinkedIn’s production deployment, which originated Kafka, handles trillions of messages daily across their cluster [8]. Confluent publishes benchmark results showing single-cluster throughput in the millions of messages per second range [9].
A 10x improvement over Kafka would require fundamental architectural advantages. It’s not impossible—Kafka’s design involves trade-offs that alternative architectures might avoid. But claims of this magnitude require correspondingly detailed proof.
What message size was used? A 100-byte message benchmark differs from a 10KB message benchmark. What durability guarantees? Fire-and-forget messaging achieves higher throughput than synchronous replication. What cluster size? How many partitions? What consumer configuration?
The claim that VAST’s Event Broker processes “over 500 million messages per second” appears in marketing materials without the technical context necessary for evaluation. This pattern—big numbers without methodology—characterizes VAST’s benchmark communication strategy.
Credit Where Due
VAST has built a functional storage platform that customers deploy in production. The architectural concept—unified storage with global namespace and metadata indexing—addresses real problems in enterprise data management. The company’s growth suggests they’re solving customer problems, regardless of whether specific benchmark claims hold up to scrutiny.
The criticism here isn’t that VAST’s technology doesn’t work. It’s that their benchmark claims can’t be verified with available information. This represents a communication problem, not necessarily a technology problem.
A company confident in their performance advantages would benefit from publishing detailed methodology. Independent verification would convert marketing claims into engineering credibility. The absence of such verification raises questions about whether the published numbers reflect realistic production scenarios or optimized synthetic benchmarks.
What Customers Should Ask
Organizations evaluating VAST should request:
Detailed benchmark methodology including hardware specifications, software versions, test parameters, and configuration files. Ask whether the tests were conducted at scale relevant to your deployment or extrapolated from smaller configurations.
Customer references with similar workloads who can speak to actual production performance. Marketing benchmarks and production reality often differ substantially.
Independent verification from third-party labs or published results in audited benchmark programs. If VAST’s performance advantages are real, independent testing should confirm them.
Comparative testing in your own environment. Proof-of-concept deployments with your actual workloads provide more relevant data than vendor benchmarks regardless of methodology quality.
The questions aren’t adversarial—they’re the due diligence appropriate for infrastructure decisions with multi-year implications.
The Industry Context
VAST isn’t unique in publishing unverified benchmark claims. Storage marketing historically emphasizes impressive numbers over reproducible methodology. The industry would benefit from norms requiring benchmark claims to include sufficient detail for independent verification.
Some vendors meet higher standards. Weka’s SPECstorage submissions include full configuration disclosure. MinIO’s open-source benchmark tools enable independent validation. Pure Storage participates in STAC-M3 auditing. These practices demonstrate that verified benchmark claims are achievable.
VAST’s choice to publish marketing multipliers rather than verified benchmarks represents a positioning decision. It prioritizes impressive numbers over engineering credibility. For some buyers, the numbers suffice. For technical evaluators, the absence of methodology undermines confidence.
The Bottom Line
VAST Data claims 25% faster than Iceberg, 60x faster updates, 100x single-key acceleration, and 500 million messages per second. These would be significant achievements if verified.
They are not verified.
The claims appear in marketing materials without hardware configurations, software versions, test parameters, or methodology documentation. They have not been submitted to independent benchmark organizations. They cannot be reproduced by third parties with available information.
This doesn’t prove the claims are false. VAST may indeed deliver these performance levels under specific conditions. But without methodology disclosure, the claims exist in a category distinct from engineering data—they’re marketing numbers, useful for sales conversations but insufficient for technical evaluation.
Engineers making infrastructure decisions need more than multipliers. They need reproducible methodology, independent verification, and realistic production benchmarks. Until VAST provides these, their performance claims remain exactly what they appear to be: impressive numbers without proof.
References
[1] VAST Data, “The Next Generation Database for the AI Era.” https://vastdata.com/blog/the-next-generation-database-for-the-ai-era
[2] VAST Data, “Event Broker Product Overview.” https://vastdata.com/platform/event-broker
[3] Apache Iceberg Documentation, “Performance Tuning.” https://iceberg.apache.org/docs/latest/performance/
[4] DataPro News, “VAST Data: Revolutionary AI OS or Silicon Valley Hyperbole?” https://www.datapro.news/p/vast-data-revolutionary-ai-os-or-silicon-valley-hyperbole
[5] theCUBE Research, “Unpacking VAST Data’s Ambition.” https://thecuberesearch.com/special-breaking-analysis-unpacking-vast-datas-ambition-to-become-the-operating-system-for-the-thinking-machine/
[6] SPEC, “SPECstorage Solution 2020 Results.” https://www.spec.org/storage2020/results/
[7] Pure Storage, “STAC-M3 Benchmark Testing Results.” https://blog.purestorage.com/news-events/pure-storage-stac-m3-benchmark-testing-results-quantitative-trading/
[8] LinkedIn Engineering, “Kafka at LinkedIn.” https://engineering.linkedin.com/kafka
[9] Confluent, “Kafka Performance Benchmarks.” https://www.confluent.io/blog/kafka-fastest-messaging-system/
StorageMath applies equal scrutiny to every vendor. VAST Data has built a successful business, but their benchmark claims lack the methodology disclosure that would allow independent verification. Impressive numbers without proof aren’t engineering data—they’re marketing.