Pure Storage FlashBlade + LandingAI: Accelerate Visual Intelligence

This blog on the power of FlashBlade and LandingLens from LandingAI was co-authored with Steve Ackley from LandingAI.

Let’s be real: Visual AI isn’t just an R&D playground anymore. It’s how factories spot defects before they cost millions, how insurers speed up claims, and how biotech firms push the boundaries of discovery. The models? They’re ready. GPUs? Plenty of power. But here’s the catch: Most projects hit a wall because storage can’t keep up.

Here’s the reality:

• The latest DGX or HGX can chew through 1-5TB/s for training, and real-time inference can range from 500GB/s to 1TB/s.
• Scaling a cluster of GPUs realistically requires the NAS system to scale in performance linearly to keep those clusters of GPUs busy.
• Teams end up copying the same images over and over just to run experiments.
• Hybrid setups—where some data is in the cloud, some on-prem—create headaches with security and ops.

That’s where this Pure Storage + LandingAI setup comes in. LandingAI simplifies the model-building and deployment process (via LandingLens and microservices). Pure Storage handles the data firehose with FlashBlade//S™—an NVMe all-flash array that can hit up to 450GB/s and sub-millisecond latency. FlashBlade//EXA™ can hit read throughput over 10TB/s. Together, you get a clean reference architecture: raw images in, insights out—no rewiring, no messy data copies.

(And no, FlashBlade//S isn’t your average SSD—it’s more like 100 NVMe SSDs behind a 450GB/s fabric. Think 10 times faster than desktop SSDs or USB drives.)

What We Actually Built

Figure 1: 

LandingLens Architecture

Figure 2: 

How It Works (without the Jargon)

Provision: We automate the Azure side with Terraform; the on-prem Pure Storage nodes are provisioned manually for now.

Package: Landing AI CLI and our customizer script generate site-specific Helm charts, injecting DB hosts, ingress rules, and image tags.

Launch: We apply the charts service by service with Helm upgrade—install, adding pull secrets for each namespace. A guided install usually takes a couple of days with our engineers on call.

Bottom line: Any authorized engineer can spin up the whole stack in minutes, test locally or in the cloud, and go to production without rework.

What’s Cool about This

No staging or copying data: LandingLens works directly on FlashBlade//S. No need for rsync jobs or cache wrangling.

Same speed, no matter the data set size: Whether you’re dealing with 100GB or 1PB, performance stays flat thanks to the parallel fabric of FlashBlade^®.

Built-in security and resilience: FlashBlade offers always-on encryption and immutable snapshots, so ransomware has no chance.

Accuracy: There’s a higher level of accuracy compared to open source and a smaller number of samples for fine-tuning highly specialized vision models.

Time to train/value: Because of the architectural advantages, the stack delivers the fastest time to train vision models compared to open source offerings.

Performance: The solution provides the best performance/rack unit, performance/W/GB metrics, and best mixed workload performance (concurrent read/writes) for multimodal AI workloads.

Cost: The solution offers the best overall TCO with guaranteed performance through Pure Storage^® Evergreen//One™.

Overall Value Prop: Pure Storage FlashBlade + LandingLens delivers a turnkey validated full-stack pod for medical imaging, biotech, and manufacturing, providing the fastest time to train/value for the end customer for computer vision use cases.

What This Means for You

Now let’s take a closer look at some use cases for this stack.

Manufacturing and Electronics

Put an end to “spot-check and pray.” With FlashBlade feeding your GPUs at up to 600Gb/s—and even faster if you step up to FlashBlade//EXA—LandingLens flags missing components, off-angle parts, solder bridges, and cosmetic dings while the line is still moving. Because the models learn from a fraction of the labelled images most open source stacks require, you get higher yield and airtight traceability without the six-month data-labelling slog.

Application of LandingLens in manufacturing.

Textiles and Packaging

Whether you’re weaving carbon-fiber fabric or printing shrink-wrap labels, every roll and every print pass streams straight to GPU memory. The same pipeline that spots stains and weave defects can read barcodes or lot codes on the fly, so you catch color shifts, misprints, or sealant gaps before a single pallet leaves the dock—no overnight rsync, no cache games, just real-time eyes on every millimeter.

Biotech and Life Sciences

Whole-slide images and fluorescent microscopy stacks move at the speed of discovery, not the speed of file transfers. Fine-tune on-prem, behind your firewall, with always-on encryption and immutable snapshots that line up with FDA 21 CFR Part 11 and HIPAA. Result: faster assay cycles, zero data sovereignty headaches.

Application of LandingLens in life sciences and biotech.

Finance and Insurance

LandingLens turns document backlogs into straight-through workflows. In our feasibility build, the stack ingested high-resolution ID photos, multi-page claim packets, and mortgage bundles directly from FlashBlade, pushed them through vision and document-AI services, and handed structured results back to core systems—all on the same iron, with zero data shuffling to the cloud. Early pilots show you can shave days off KYC and claims cycles while keeping every byte inside your own compliance perimeter.

Industrial Equipment Monitoring

Keep a constant eye on conveyors, presses, and robotic arms. The moment alignment drifts or a belt starts to fray, the vision model fires a maintenance ticket. Because FlashBlade scales linearly—one chassis at a time—you can keep adding cameras (or entire production cells) without ever re-architecting storage or watching latency creep past a millisecond.

Watch a video demonstration of LandingLens

What’s Next

Run your own pilot: We’re offering early customers a chance to test their workloads on our joint lab setup.

Benchmark results coming: We’ll publish real-world latency, throughput, and cost numbers once the pilots are done.

DIY blueprint: We’re packaging up the Terraform + Helm configs so your DevOps team can replicate the setup in under an hour.

Ready to Build? Bring Us Your Data

If you’ve got a massive image library or document pile slowing down your AI plans, let’s put it to the test. Bring us your data set—we’ll run it live and show you what this stack can do.

Try FlashBlade

No hardware, no setup, no cost—no problem. Experience the self-service capabilities of FlashBlade.

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