01
Make AI training move faster.
Reduce loading delays, checkpoint time and idle GPU cycles so your researchers can run more experiments in the same day.
AI data + memory infrastructure
Your GPUs aren’t slow. They’re waiting.
You bought AI compute to train models, run inference and ship what comes next. You didn’t buy it to watch expensive GPUs wait for data, memory or another pipeline to finish.
Start with the work you need to move faster. Then find the infrastructure path that fits it.
Start here
What are you trying to make happen?
You don’t need to diagnose the product first. Choose the job. Then follow the path that matches it.
02
Serve more inference without adding more GPUs.
Reduce memory pressure, repeated compute and context bottlenecks so the GPUs you already own can support more useful work.
Your path
NeuralMesh + Augmented Memory Grid
Inference Economics Review
Improve inference economics →
Inference Economics Review
03
Turn enterprise data into AI-ready context.
Stop building permanent integration projects around every new AI workload. Keep data current, usable and permission-aware.
04
Build an AI factory without stitching the stack together yourself.
Start with the workload, then connect compute, networking, data, memory and operations around one production foundation.
05
Get more revenue from every GPU in your AI cloud.
Improve customer density, workload performance and infrastructure economics without treating every constraint with more hardware.
06
Keep production infrastructure boring.
Find operational risk before your researchers do. Reduce surprises, resolve problems faster and keep technical ownership clear.
Your path
Customer Success + Designated Engineering
Production Reliability Review
Reduce production risk →
Production Reliability Review
You’re not buying storage. You’re buying back the time infrastructure steals from the work.
The hidden bill
Waiting rarely appears as one line item.
Idle compute
GPUs sit below target utilization while the rest of the stack catches up.
Slower research
Researchers schedule their work around loading, checkpoints and infrastructure limitations.
Repeated work
Context is recreated. Data is copied. Pipelines run again. Compute gets spent without producing a new answer.
More infrastructure
The default answer becomes buying more hardware instead of removing the bottleneck holding the hardware back.
What changes
When the infrastructure keeps up, the work changes.
The useful measure isn’t how impressive the infrastructure looks. It’s what your people and compute can do once it stops slowing them down.
40×
FASTER TRAINING STARTUP
Published Luma AI result for training startup performance.
10×
FASTER CHECKPOINTING
Published Cohere result for checkpoint performance.
6.5×
INPUT-TOKEN THROUGHPUT
Published workload result for inference throughput at scale.
80%
LOWER STORAGE COSTS
Published Physical Intelligence customer result.
One foundation
Different jobs. Same thing underneath them.
Keep the work moving.
Training + fine-tuning
NeuralMesh
A high-performance data foundation for GPU-intensive AI workloads.
Long-context inference
Augmented Memory Grid
Expand effective memory capacity and reduce repeated context work.
Enterprise AI
NeuralMesh AIDP
Prepare changing enterprise data for AI while preserving access controls.
Integrated deployment
WEKApod
An engineered deployment option for large-scale AI infrastructure.
Production operations
Customer Success + Designated Engineering
Add technical ownership, monitoring and deep engineering access after the environment becomes production.
Fit
If cheap capacity is all you need, this probably isn’t for you.
You should probably talk to WEKA if…
- GPU utilization changes your economics.
- Training speed changes research velocity.
- Long context is creating memory pressure.
- Data pipelines are slowing production AI.
- Your AI cloud needs more useful work per GPU.
- Another infrastructure silo creates more work than it removes.
You may not need WEKA if…
- Your workloads rarely wait on data.
- Your GPUs are not a meaningful cost center.
- Generic capacity is more important than workload performance.
- Your AI workloads are small enough that existing infrastructure keeps up.
- You have no operational or economic reason to remove the bottleneck.
Your next step
Start with the bottleneck, not the sales pitch.
Tell us what you’re trying to make faster and where the work starts slowing down.
Start with the workload. Then decide whether WEKA belongs in it.
The goal isn’t faster storage. The goal is what happens when your team stops waiting for it.