MinIO ExaPOD: Credible Architecture, Questions on Methodology
MinIO's exabyte-scale reference architecture makes defensible claims backed by historical benchmarks. We examine what's verifiable, what needs more transparency, and how the commodity approach compares to appliance vendors.
MinIO announced ExaPOD in November 2025, positioning it as “The Reference Architecture for Exascale AI” [1]. The announcement describes a validated hardware configuration achieving 1 EiB usable capacity with 19.2 TB/s aggregate throughput at $4.55-4.60 per TiB-month. Unlike many vendor announcements that traffic in impossible claims, MinIO’s numbers appear mathematically sound. The architecture uses commodity hardware—Supermicro servers, Intel processors, Solidigm NVMe drives—configured for exabyte-scale object storage.
This analysis examines what MinIO claims, verifies the mathematics where possible, and identifies areas where additional transparency would strengthen the credibility of an otherwise solid reference architecture.
The Architecture at a Glance
ExaPOD specifies a concrete bill of materials: 32 racks of 48U each, with 20 servers per rack, totaling 640 servers. Each server contains Solidigm D5-P5336 NVMe drives at 122.88 TB capacity, connected via 400 GbE networking in a 1:1 Clos leaf-spine topology with full bisection bandwidth. The erasure coding configuration uses an 8-stripe layout with 5 data shards and 3 parity shards, yielding 62.5% storage efficiency.
The capacity math checks out. At 57.6 PiB raw capacity per rack across 32 racks, the system provides 1,843 PiB raw. Apply 62.5% erasure coding efficiency and you get approximately 1.15 EiB usable—matching MinIO’s “1 EiB+” claim. This isn’t marketing inflation; it’s straightforward arithmetic anyone can verify.
The throughput claim of 19.2 TB/s also falls within reasonable bounds. With 640 servers each equipped with 400 GbE connectivity, the theoretical network maximum approaches 32 TB/s. MinIO’s claimed 19.2 TB/s represents roughly 60% of theoretical maximum—a plausible figure accounting for protocol overhead, erasure coding computation, and real-world inefficiencies. Storage systems rarely achieve more than 70-80% of theoretical network throughput under production workloads, so 60% suggests either conservative testing or realistic expectations.
What MinIO Gets Right
MinIO has published warp benchmark results for years, establishing a track record of reproducible performance claims. Their S3 benchmark tool is open source, allowing independent verification [2]. Historical benchmark publications have generally aligned with what independent testers observe in production environments. This history of transparency lends credibility to ExaPOD claims that might otherwise seem aspirational.
The commodity hardware approach represents a genuine architectural philosophy, not just a marketing angle. By specifying off-the-shelf components from Supermicro, Intel, and Solidigm, MinIO enables organizations to source hardware independently, negotiate their own pricing, and avoid vendor lock-in. This contrasts with appliance vendors like VAST Data, Pure Storage, or NetApp, who bundle hardware and software into integrated systems with proprietary margins.
The commodity versus appliance trade-off is real. Appliance vendors offer turnkey deployment, integrated support, and simplified procurement—valuable for organizations prioritizing operational simplicity. Commodity approaches offer flexibility, competitive hardware pricing, and the ability to mix components from multiple suppliers—valuable for organizations with engineering capacity and cost sensitivity. Neither approach is universally superior; they serve different organizational needs.
MinIO’s erasure coding configuration of 5+3 is conservative and well-understood. Reed-Solomon codes at this ratio provide tolerance for any 3 simultaneous drive or node failures while maintaining reasonable storage efficiency. The 37.5% overhead (3 parity shards out of 8 total) is higher than aggressive configurations like 8+2 (20% overhead) but provides substantially better durability. For exabyte-scale deployments where rebuild times extend to hours or days, this conservatism makes sense.
Where Transparency Would Help
Benchmark Methodology
The 19.2 TB/s throughput claim lacks published methodology. Key questions remain unanswered in the announcement:
Object sizes matter enormously. S3 throughput varies dramatically with object size. Small objects (4KB-64KB) stress metadata operations and achieve far lower aggregate throughput than large objects (1MB-100MB+) that amortize per-request overhead across more data. A system achieving 19.2 TB/s with 100MB objects might achieve 1 TB/s with 64KB objects. Without knowing the object size distribution used in testing, the throughput number lacks operational context.
Client count affects results. Throughput benchmarks require sufficient client parallelism to saturate storage capacity. A 640-server cluster likely requires hundreds or thousands of concurrent client connections to achieve maximum throughput. The announcement doesn’t specify whether testing used 100 clients, 1,000 clients, or 10,000 clients—information essential for understanding whether the benchmark reflects realistic deployment scenarios.
Read/write mix shapes performance. Object storage systems typically achieve higher read throughput than write throughput due to erasure coding computation overhead on writes. A 19.2 TB/s number could represent 100% reads, 100% writes, or some mixed workload. Each scenario has different operational implications.
Was this tested at scale or extrapolated? Building and testing a 640-server, 1 EiB cluster represents a significant investment. It’s reasonable to ask whether MinIO actually deployed this configuration for benchmarking or extrapolated from smaller cluster tests. Both approaches can be valid, but extrapolation introduces assumptions that should be disclosed.
MinIO publishes warp results regularly on their blog, and benchmark data is reportedly available upon request. Making the ExaPOD-specific methodology publicly available—object sizes, client counts, read/write ratios, and whether testing occurred at full scale—would strengthen an already credible claim.
Pricing Assumptions
The $4.55-4.60 per TiB-month claim includes a crucial caveat: “indicative, not a guarantee.” This honesty is appreciated, but the underlying assumptions deserve examination.
CapEx amortization period significantly affects per-TiB-month calculations. Enterprise storage hardware typically depreciates over 3-5 years. A 3-year amortization produces higher monthly costs than 5-year amortization. The announcement doesn’t specify which period MinIO used, making independent verification difficult.
Power cost assumptions vary dramatically by geography. Data center power ranges from $0.04/kWh in favorable locations to $0.15/kWh or higher in premium markets. MinIO claims 900W per PiB including cooling—a reasonable figure for NVMe-based systems—but the resulting power cost depends entirely on local rates. At 1 EiB (1,024 PiB), total power draw approaches 922 kW. At $0.06/kWh, that’s roughly $40,000/month in power. At $0.12/kWh, it’s $80,000/month. This variance alone could shift the per-TiB-month cost by $0.04-0.08.
MinIO Enterprise licensing costs aren’t publicly disclosed. MinIO operates on a subscription model with per-TiB pricing that varies by contract size and terms. For an exabyte-scale deployment, licensing likely represents a substantial portion of operating costs. Without knowing the licensing component, the $4.55/TiB-month figure can’t be decomposed into hardware, power, and software costs.
Support and maintenance contracts for Supermicro servers, Solidigm drives, and network equipment add operational costs that may or may not be included in MinIO’s calculation. Enterprise deployments typically budget 15-20% of hardware CapEx annually for support contracts.
Rack space and cooling in colocation facilities range from $500-2,000 per rack per month depending on power density and location. Thirty-two racks could add $16,000-64,000 monthly—$0.015-0.06 per TiB-month at exabyte scale.
A complete pricing breakdown showing CapEx components, OpEx assumptions, and licensing costs would allow organizations to plug in their own values and generate realistic TCO projections. The current “indicative” figure provides a starting point but requires significant refinement for procurement decisions.
The Exascale Terminology
MinIO’s use of “exascale” for exabyte-scale storage follows an emerging industry convention, though it differs from the term’s origin in high-performance computing. In HPC contexts, exascale traditionally refers to computational capacity—specifically, systems capable of 10^18 floating-point operations per second (exaFLOPS). The first true exascale supercomputers (Frontier, Aurora) achieved this milestone in 2022-2023.
Storage vendors have adopted “exascale” to describe exabyte-capacity systems, creating potential confusion but following a logical parallel: if exaFLOPS describes computational scale, exabytes describes storage scale. This usage appears in marketing from multiple vendors and seems likely to persist regardless of purist objections.
MinIO’s ExaPOD does deliver exabyte-scale storage capacity by any reasonable definition. Whether the terminology represents useful communication or marketing appropriation depends on audience expectations. For storage professionals, “exabyte-scale” might be clearer; for executives comparing vendor capabilities, “exascale” aligns with broader technology discourse around frontier computing.
Commodity vs. Appliance: The Real Comparison
ExaPOD’s value proposition centers on the commodity hardware model. By publishing a validated reference architecture with specific components, MinIO enables organizations to:
Source hardware competitively. Organizations can obtain quotes from multiple server vendors, negotiate volume pricing, and potentially substitute equivalent components. A Supermicro-specified system could theoretically be built with Dell PowerEdge or HPE ProLiant servers if the specifications match.
Avoid software-hardware bundling. Appliance vendors capture margin on both hardware and software, with hardware margins often exceeding what’s available in the commodity server market. Decoupling these allows organizations to apply competitive pressure to each component.
Maintain flexibility. Commodity deployments can mix hardware generations, integrate with existing infrastructure, and evolve component choices over time. Appliance vendors typically require homogeneous deployments within their ecosystem.
The trade-offs are real. Appliance vendors provide integrated support, pre-validated configurations, and simplified procurement—genuine value for organizations lacking storage engineering expertise. VAST Data’s integrated approach, for example, bundles QLC flash optimization, proprietary data reduction, and purpose-built hardware into a system that requires minimal configuration. Organizations pay premium pricing for reduced operational complexity.
MinIO’s ExaPOD appeals to a different customer profile: organizations with infrastructure engineering capability who prioritize cost optimization and flexibility over turnkey simplicity. Both approaches serve legitimate needs; neither is universally superior.
What We’d Like to See
MinIO has built credibility through years of open-source development, published benchmarks, and transparent architecture documentation. ExaPOD continues this pattern with a concrete, verifiable reference architecture. The following additions would further strengthen an already solid announcement:
Published benchmark methodology specifying object sizes, client counts, read/write ratios, and confirmation of testing at 1 EiB scale versus extrapolation from smaller deployments.
Decomposed pricing showing hardware CapEx, power assumptions ($/kWh), licensing costs, and support contract estimates separately, allowing organizations to adjust for their specific circumstances.
Performance curves showing throughput variation across object sizes (4KB to 100MB+) and client counts, providing operational guidance for workload planning.
Comparison data against previous MinIO reference architectures, demonstrating performance scaling characteristics as cluster size increases.
These additions align with MinIO’s historical transparency and would differentiate ExaPOD from vendor announcements that rely on unverifiable claims.
The Bottom Line
MinIO’s ExaPOD announcement makes defensible claims backed by verifiable mathematics and consistent with the company’s benchmark history. The 1 EiB capacity calculation checks out. The 19.2 TB/s throughput falls within plausible bounds for the specified hardware. The $4.55/TiB-month pricing requires assumption disclosure but isn’t obviously inflated.
This isn’t a takedown. MinIO has earned credibility through transparency, and ExaPOD maintains that standard. The questions raised here—benchmark methodology, pricing decomposition, testing scale—represent requests for the additional detail that would elevate a credible announcement to a fully verifiable reference architecture.
For organizations evaluating exabyte-scale storage, ExaPOD provides a concrete starting point for commodity-based deployments. The architecture decisions are sound. The component choices are reasonable. The economics appear competitive with appliance alternatives, though direct comparison requires pricing transparency from both MinIO and competitors.
Storage vendors publishing reference architectures should meet a higher standard than marketing announcements. MinIO is closer to that standard than most. Full methodology disclosure would complete the picture.
References
[1] MinIO, “Introducing MinIO ExaPOD: The Reference Architecture for Exascale AI,” November 2025. https://blog.min.io/introducing-minio-exapod-the-reference-architecture-for-exascale-ai/
[2] MinIO, “Warp: S3 Benchmark Tool,” GitHub. https://github.com/minio/warp
[3] Solidigm, “D5-P5336 Series Data Sheet.” https://www.solidigm.com/products/data-center/d5/p5336.html
[4] Supermicro, “Storage Server Solutions.” https://www.supermicro.com/en/products/storage
StorageMath examines vendor claims with equal rigor regardless of business model. Unlike vendors claiming 26 nines durability or benchmarks that defy physics, MinIO’s ExaPOD makes mathematically defensible claims consistent with their historical track record. This analysis identifies opportunities for additional transparency, not fundamental problems with the architecture. When vendors make honest claims, they deserve recognition—not just scrutiny.