Evaluation Criteria

  • Performance: IOPS, throughput, and latency for your specific workload mix
  • Scalability: Maximum capacity, performance scaling, and upgrade path
  • Data services: Snapshots, replication, encryption, deduplication, compression
  • Management: GUI usability, API/automation support, monitoring integration
  • Support: Response time SLAs, proactive monitoring, hardware replacement
  • TCO: Hardware, software licensing, support, power, cooling, and operational costs over 5 years

All-Flash Array Evaluation

Key all-flash array evaluation criteria: performance at your workload mix (not just peak IOPS), data reduction ratios for your data types, non-disruptive upgrade path, and support quality. Request a proof of concept with your actual workloads before committing.

Test performance with all data services enabled — deduplication and compression can reduce performance by 10–30% on some systems. Verify that advertised performance is achievable with your data types and access patterns.

AI Storage Evaluation

AI storage evaluation focuses on throughput, not IOPS. Key criteria: aggregate read throughput (GB/s), scalability (can throughput scale with cluster size?), and S3 API compatibility for dataset management. Test with actual AI training workloads — synthetic benchmarks do not represent AI access patterns.

Software-Defined Storage Evaluation

SDS evaluation must account for operational complexity — SDS requires more expertise to deploy and manage than purpose-built storage arrays. Key criteria: performance on commodity hardware, operational tooling quality, vendor support for the SDS platform, and total cost including operational labor.

Vendor Comparison

VendorStrengthsConsiderations
Pure StorageBest all-flash performance; Evergreen subscription; excellent supportPremium pricing; limited to all-flash
NetAppBroadest portfolio; strong data management; ONTAP ecosystemComplex licensing; higher operational overhead
Dell PowerStoreGood price/performance; broad portfolio; strong enterprise relationshipsComplex product line; variable support quality
WEKABest AI storage throughput; cloud-native; NVMe-basedNewer vendor; higher cost; requires expertise
Vast DataUniversal storage platform; excellent scalability; strong AI capabilitiesNewer vendor; limited track record at scale

Common Mistakes

  • Evaluating on vendor benchmarks: Test with your actual workloads in your environment
  • Ignoring data reduction variability: AI data achieves minimal data reduction — plan capacity accordingly
  • Underestimating software costs: Storage software licenses can add 30–50% to hardware costs
  • Not planning for AI workloads: Traditional enterprise storage criteria are insufficient for AI
  • Selecting on price alone: The cheapest storage is rarely the best value over a 5-year lifecycle