VAST Data's '29x Data Reduction' Claims: The Storage Industry's Most Brazen Lies

VAST Data's co-founder claims customers see 8x to 29x capacity advantages versus HDFS. We expose the fraudulent math, the deliberate straw man, and why VAST has become the storage industry's most prolific source of misleading claims.

January 15, 2026 · 14 min read

VAST Data has a lying problem.

Their co-founder Jeff Denworth recently posted on LinkedIn claiming extraordinary data reduction ratios from customers migrating from “triplicated data lakes” to VAST DataBase. The numbers are impressive: 8x, 12.8x, and an eye-popping 29x capacity advantage. These claims are not merely misleading—they are deliberately constructed falsehoods designed to deceive potential customers.

This is not our first encounter with VAST’s creative relationship with truth. We’ve documented their unverifiable 99.9991% uptime claims, their dubious 10x Kafka performance assertions, and now this. A pattern emerges: VAST Data has positioned itself as the storage industry’s foremost charlatan, systematically publishing claims that collapse under the slightest mathematical scrutiny.

Let’s expose exactly how this fraud works.

The Claims

Jeff’s post presents data reduction stories from VAST’s “data lake modernization efforts.” Here’s what he claims:

An electric vehicle company achieved 2.8:1 data reduction ratio (DRR), which when multiplied by 2.91x “replication efficiency” yields 8x capacity utilization improvement. An insurance company hit 4.4:1 DRR, translating to 12.8x capacity advantage. A chip design company achieved an overall DRR of 29:1.

The formula is simple: take your data reduction ratio, multiply by 2.91, and voilà—massive savings that justify VAST’s premium pricing.

There’s just one problem: this math is a lie.

The 2.91x Straw Man: Lying Through Obsolete Comparisons

The 2.91x “replication efficiency” multiplier compares VAST’s erasure coding to HDFS 3x replication. Let’s verify this calculation.

VAST uses wide erasure coding stripes, typically 150+4 in large deployments. This means 150 data chunks plus 4 parity chunks, yielding 4/154 = 2.6% overhead. Total capacity required: 1.026x the logical data size. Meanwhile, HDFS 3x replication stores three copies of every block, requiring 3x the logical data size.

The ratio: 3.0 ÷ 1.026 = 2.92x

So yes, the 2.91x number is mathematically correct against a baseline that hasn’t been relevant for nearly a decade. Here’s what Jeff knows but deliberately omits: HDFS has supported erasure coding since December 2017.

Apache Hadoop 3.0 introduced native erasure coding support. The default RS(6,3) scheme—six data blocks plus three parity blocks—has been available for over eight years. This gives 50% overhead (1.5x capacity), not 200% overhead (3x capacity).

The honest comparison:

Jeff is comparing VAST’s best-case scenario against a configuration that no competent data engineer has deployed for new workloads since 2018. This is not ignorance—Jeff Denworth is a storage industry veteran who absolutely knows that HDFS erasure coding exists. He is choosing to lie by omission.

It’s like a car manufacturer bragging about better fuel economy than a 1970s muscle car while pretending that Teslas don’t exist. Except it’s worse, because VAST knows their customers are technical decision-makers who might actually believe this nonsense.

The real “replication efficiency” advantage of VAST over a modern HDFS deployment with erasure coding is approximately 1.4x, not 2.91x. This single honest substitution cuts every claimed advantage roughly in half. But half the lie wouldn’t generate the same LinkedIn engagement, would it?

The Multiplication Fallacy: Claiming Credit for Universal Physics

The deeper deception is presenting the formula DRR × Replication Efficiency as if both factors are VAST-unique benefits. They are not.

Data reduction—compression and deduplication—works on any storage platform. You can compress data on HDFS. You can compress data on NetApp. You can compress data on Dell PowerScale, Pure Storage, or a pile of USB drives. Compression algorithms don’t care what storage system runs them.

When Jeff claims an electric vehicle company achieved 2.8:1 DRR on VAST, the implicit suggestion is that this compression ratio is somehow a VAST capability. It isn’t. The same data compressed with the same algorithm on HDFS would achieve the same 2.8:1 ratio.

The honest comparison would be:

HDFS with RS(6,3) EC + Zstd compression vs VAST with 150+4 EC + compression

In this apples-to-apples comparison, the capacity advantage shrinks from the claimed 8x to something like 1.4x × (marginal dedupe advantage). For most workloads, that’s 1.5-2x total—a meaningful but far less impressive number than 8x.

VAST’s marketing trick is to compare:

• VAST with all optimizations enabled
• Against legacy HDFS with no optimizations

Then present the delta as if VAST invented compression. This is intellectual fraud dressed up as technical marketing.

The Math Errors That Always Favor VAST: Coincidence or Fabrication?

Jeff’s post contains arithmetic errors. Every single error inflates VAST’s claimed advantage. Not one error understates their case. The probability of this happening by chance decreases with each conveniently favorable “mistake.”

The telco example: Jeff claims 1.3:1 DRR multiplied by the replication factor yields “4.28:1 capacity advantage.” Let’s check: 1.3 × 2.91 = 3.78. That’s not 4.28. The actual math shows 3.78x, but Jeff reports 4.28x—a 13% inflation.

The credit card example: Jeff claims 2.7:1 DRR yields “8.1:1 capacity boost.” The math: 2.7 × 2.91 = 7.86. Jeff reports 8.1x—a 3% inflation.

These are not rounding errors. Rounding errors go both directions. When every single arithmetic “mistake” in a post increases the vendor’s claimed advantage, you’re not looking at carelessness—you’re looking at deliberate inflation.

A co-founder of a company valued at $30 billion, posting about numerical advantages, doesn’t accidentally get the arithmetic wrong in ways that always favor his company. Jeff either can’t do basic multiplication (unlikely for someone who’s spent decades in enterprise storage), or he’s padding the numbers knowing most readers won’t check his math.

The 29:1 Absurdity: A Number So Ridiculous It Insults Your Intelligence

The chip design company claim deserves special scrutiny. A 29:1 data reduction ratio across an enterprise dataset is extraordinary. For context:

Typical compression ratios by data type:

• Already-compressed formats (Parquet, ORC, JPEG): 1.0-1.3x
• Text and logs: 3-5x
• Database tables: 2-4x
• Scientific simulation data: 2-6x
• Uncompressed media: 2-10x

To achieve 29:1 across an entire dataset requires one of three scenarios.

The first possibility is that the source data was pathologically inefficient—completely uncompressed, with massive duplication, stored on a system with no optimization whatsoever. If true, this reflects poorly on the customer’s previous architecture, not well on VAST. Any modern storage system would show dramatic improvement over such a baseline.

The second possibility is aggressive global deduplication finding cross-project, cross-team, or cross-time duplicates. This can yield high ratios, but comes with significant caveats: dedupe is computationally expensive, can impact performance, and the ratios are highly workload-dependent. A chip design company likely has many versions of similar designs—great for dedupe, but not representative of typical workloads.

The third possibility is cherry-picking. Perhaps 29:1 applies to a specific subset of data—archived old revisions, backup copies, or a particular project with unusual characteristics. Presenting this as the “overall DRR” without context is misleading.

Without knowing the baseline configuration, dedupe scope, data composition, and measurement methodology, 29:1 is just a fabrication. It tells us nothing useful about what a typical customer should expect—which is precisely the point. The number exists to impress, not to inform. It’s designed to be repeated in sales calls and LinkedIn comments without ever being scrutinized.

The ORC Caveat Reveals the Truth

Buried in Jeff’s post is an admission that undermines the entire narrative. The wireless telco example notes: “their data is precompressed on ORC file-format” and achieved only 1.3:1 DRR.

This is the critical detail. Modern data lakes don’t store uncompressed data. Apache Parquet and ORC—the dominant formats for analytical workloads—include built-in compression. When data is already compressed, VAST’s additional compression provides minimal benefit.

The massive DRR numbers (4.4:1, 2.8:1) only apply to customers migrating from legacy systems with uncompressed or poorly-compressed data. For any organization running a modern data stack—which is most organizations building new data lakes in 2025—the realistic DRR is closer to 1.3:1.

Combined with the realistic 1.4x erasure coding advantage over HDFS EC, the actual capacity benefit for a modern workload is approximately: 1.3 × 1.4 = 1.82x

Not 8x. Not 12.8x. Not 29x. Less than 2x.

The Pattern of Lies: VAST Data’s Systematic Dishonesty

This isn’t an isolated incident. VAST Data has established a consistent pattern of publishing extraordinary claims that cannot be verified and fall apart under scrutiny. We’ve previously analyzed their 99.9991% uptime claim—4.7 minutes of downtime per year, a number that requires believing VAST has better reliability than AWS, Google, and Microsoft combined despite being a fraction of their size and operational maturity. We’ve examined their 10x Kafka performance claims, which conveniently lack the reproducible benchmarks that would let anyone verify them.

VAST has perfected a formula for manufacturing impressive statistics:

1. Pick an impressive number: 99.9991%, 10x, 29x—bigger is better, plausibility is optional
2. Compare against the worst possible baseline: Legacy HDFS without EC, untuned Kafka, configurations from 2010
3. Omit methodology: Never publish enough detail for anyone to reproduce or verify
4. Use anonymous customers: “[chip design company]” conveniently cannot be contacted for confirmation
5. Rely on uncritical amplification: Blocks and Files and other outlets publish VAST’s numbers as facts, never asking obvious questions

This is not aggressive marketing. This is systematic deception. VAST Data has become the storage industry’s most prolific manufacturer of unverifiable claims, and they face zero consequences because the tech press is too lazy or too compromised to challenge them.

The Damning Contrast: Vendors Who Actually Prove Their Claims

While VAST publishes unverifiable LinkedIn posts with made-up multipliers, their competitors submit to independently audited benchmarks. This contrast exposes VAST’s methodology-free marketing for what it is: deliberate evasion of scrutiny.

WEKA submitted to SPECstorage Solution 2020 in January 2025, achieving #1 rankings across all five workloads. The results are independently audited by SPEC and published with full configuration disclosure—hardware specifications, software versions, test parameters, and raw data. Anyone can review the methodology. Anyone can compare configurations. Anyone can verify the claims. We analyzed this in detail in our WEKA SPECstorage coverage.

Hammerspace submitted to MLPerf Storage v2.0 in August 2025, demonstrating their Tier 0 architecture supporting 140 simulated H100 GPUs with 94.7% utilization and a result 3.7x more efficient than the next competitor. The methodology is public. The results are MLCommons-validated. Curtis Anderson, Hammerspace’s field CTO and MLPerf Storage working group co-chair, helps define the very standards that ensure benchmark integrity.

DDN submitted their AI400X3 to MLPerf Storage v2.0, achieving 120+ GB/s throughput supporting 640 simulated H100 GPUs. Their results are MLCommons-validated with full transparency. DDN publishes real numbers verified by independent third parties.

The MLPerf Storage v2.0 submission list includes over 200 results from 26 organizations: Alluxio, Argonne National Lab, DDN, Hammerspace, HPE, IBM, Micron, Nutanix, Oracle, Samsung, WEKA, and others. These vendors compete on a level playing field with standardized methodology and independent verification.

Conspicuously absent from both SPECstorage and MLPerf Storage: VAST Data.

Let that sink in. The vendor claiming 29x data reduction, 99.9991% uptime, and 10x Kafka performance has never submitted to a major independently audited storage benchmark. WEKA proves their claims through SPEC. Hammerspace proves their claims through MLCommons. DDN proves their claims through MLPerf. VAST proves nothing—they just post on LinkedIn and wait for Blocks and Files to amplify it.

The infrastructure for transparent benchmarking exists. SPEC has been auditing storage benchmarks for years. MLCommons has created the industry-standard AI storage benchmark. Participation is voluntary—and VAST has voluntarily chosen not to participate. They’ve decided that unverifiable marketing claims serve their interests better than transparent competition.

This is not a vendor that lacks the resources to participate in audited benchmarks. VAST Data is valued at $30 billion with over 1,000 employees. They could submit to SPECstorage tomorrow. They could participate in MLPerf Storage v3.0. They choose not to, because audited benchmarks would require them to prove claims that cannot be proven.

When WEKA, Hammerspace, and DDN submit to independent verification while VAST hides behind LinkedIn posts, you’re seeing the difference between engineering confidence and marketing cowardice.

Why This Matters: VAST’s Lies Hurt Everyone

When a vendor valued at potentially $30 billion builds that valuation on misleading math, the damage extends far beyond one company’s ethics.

Customers get screwed. An organization expecting 8x capacity advantage will budget accordingly, plan their data center footprint accordingly, and make staffing decisions accordingly. When reality delivers 1.5-2x, someone has to explain the cost overruns to the CFO. Someone has to explain why the promised efficiency didn’t materialize. The engineers who made the recommendation—based on trusting a vendor’s claims—take the blame for VAST’s lies.

Honest competitors get punished. NetApp, Dell, Pure Storage, and others face a choice: match VAST’s hyperbole or lose deals to inflated claims. When VAST claims 29x and a competitor honestly states 2-3x, the honest vendor looks incompetent. This creates a race to the bottom where lying becomes the rational strategy. VAST’s dishonesty makes the entire industry worse.

Tech journalism becomes useless. When Chris Mellor at Blocks and Files publishes VAST’s numbers as facts rather than unverified assertions, he’s not doing journalism—he’s doing free marketing. When analysts include VAST in “leader” quadrants without verifying their claims, they’re selling credibility they haven’t earned. The entire ecosystem of storage industry coverage becomes corrupted when vendors learn that lies get amplified and truth-telling gets ignored.

Engineers waste thousands of hours. Every storage professional evaluating systems must now assume vendor claims are false until proven otherwise. Instead of comparing features and architecture, they’re doing forensic accounting on marketing materials. This is time stolen from actual engineering work, all because VAST decided that honesty was optional.

What Honest Marketing Would Look Like

VAST could make credible claims by publishing:

Baseline configurations: Specify exactly what the source system was running. HDFS version, replication factor, compression settings, file formats. Without this, any comparison is meaningless.

Measurement methodology: How is DRR calculated? Logical bytes in versus physical bytes stored? What about metadata overhead? Snapshot space? Filesystem overhead?

Dedupe scope and impact: Is deduplication inline or post-process? What’s the dedupe domain? What’s the performance impact? What workloads benefit most and least?

Named customers with permission: Anonymous testimonials are unfalsifiable. Named customers who can confirm the numbers add credibility.

Realistic ranges, not outliers: “Typical customers see 1.5-3x total capacity advantage; best cases reach 5-8x for specific workloads” is honest. “29x” without context is not.

Reproducible benchmarks: Publish configurations, test scripts, and raw data so claims can be independently verified.

Until VAST provides this transparency, their data reduction claims should be treated as marketing assertions, not engineering facts.

Conclusion: VAST Data is Lying to You

Let’s be direct about what we’ve documented here.

VAST Data’s co-founder published claims that he knows are false. The 2.91x comparison against deprecated HDFS configurations is a deliberate straw man. The multiplication formula presents universal capabilities as VAST-unique benefits. The arithmetic errors that always favor VAST are not accidents. The 29:1 outlier is presented without context precisely because context would expose it as meaningless.

This is not aggressive marketing. This is not “vendor optimism.” This is a pattern of deliberate falsehoods from a company that has made misleading claims its core marketing strategy.

VAST Data has decided that the storage industry’s lack of accountability makes lying profitable. They’ve calculated—correctly, so far—that tech journalists won’t challenge them, analysts won’t scrutinize them, and customers won’t do the math. They’ve built a $30 billion valuation substantially on claims that do not survive basic verification.

Every time VAST publishes an unverifiable claim and faces no consequences, they learn that honesty is for suckers. Every time Blocks and Files or another outlet amplifies VAST’s numbers without scrutiny, they enable the next round of lies. Every time a customer signs a contract based on 8x or 29x capacity advantages that will never materialize, VAST profits from their deception.

This has to stop.

Storage decisions involve millions of dollars and years of operational commitment. Engineers evaluating VAST should demand the methodology behind every claim. Sales engineers should be pressed on exactly which configurations are being compared. Contracts should include performance guarantees based on the promised metrics—and penalties when reality falls short.

If VAST’s claims are true, they should welcome the scrutiny. If they refuse to provide verification, you have your answer.

The storage industry deserves vendors who tell the truth. VAST Data has demonstrated, repeatedly, that they are not among them.

References:

• Apache Hadoop documentation: HDFS Erasure Coding (available since Hadoop 3.0, December 2017)
• VAST Data technical documentation: Locally Decodable Erasure Codes
• StorageMath: “VAST Data’s 99.9991% Uptime and 10x Kafka Claims”
• StorageMath: “Weka’s SPECstorage Records: How Benchmark Transparency Should Work”
• SPEC: SPECstorage Solution 2020 Results (https://www.spec.org/storage2020/results/)
• MLCommons: MLPerf Storage v2.0 Benchmark Results (August 2025)
• Hammerspace: MLPerf Storage v2.0 Benchmark Results Technical Brief
• DDN: AI400X3 MLPerf Storage Benchmark Results