Engineering Control Plane for AI Infrastructure
XARIV helps engineering teams reduce the time, cost, and uncertainty of building AI systems — from first workload sketch to procurement-ready architecture review.
No account required · Free to start
Sound familiar?
“My AI feature is slow, expensive, and I don't know why.”
Latency spikes, GPU bills, and no clear bottleneck.
“Leadership wants 10M users next quarter. Can we?”
Capacity questions with no model beyond spreadsheets.
“We have twelve tools and no single source of truth.”
Hugging Face, vLLM, Datadog, Excel — nothing connects the decision.
One platform · Five capabilities
Lens, Pulse, Atlas, Oracle, and Forge are modules inside one engineering control plane — not five unrelated products.
Complete in under 10 minutes
Define a workload, benchmark real traffic, see a unified report, get recommendations, and export for your team. Calculators are optional step zero for quick ballpark checks.
Today, every step of the AI infrastructure lifecycle uses a different tool. XARIV owns the decision workflow between them.
Today — fragmented
Nobody owns the entire engineering decision workflow.
With XARIV — one platform
Enterprise lifecycle
Product, ML, platform, SRE, finance, and leadership — one platform, multiple personas, same source of truth.
Platform in action
Global e-commerce platform. Planning a 32B chat model rollout on H100s with a 500ms p99 SLO — team estimated 48 GPUs from spreadsheet math. Lens predicted a memory-bandwidth bottleneck at 28 GPUs; Pulse replayed ShareGPT traffic and confirmed p99 at 31 GPUs with tensor parallelism. Provisioned 31 GPUs instead of 48 — 35% capex reduction with SLO met on day one.
“We stopped guessing GPU counts. XARIV Lens surfaced the memory-bandwidth constraint our spreadsheet model missed — and Pulse validated the fix before we cut a PO.”
First-principles engineering analysis behind the platform models.
Hundreds of models, ten thousand GPUs, a nine-figure compute bill — and almost every design decision turns out to be one dial: how much do you pay, in money or complexity, to protect latency?
Hundreds of models, thousands of GPUs, a multi-million-dollar compute bill — and yet the scheduler dispatches about once a second. The hard part isn't throughput. It's trusting the number.
It looks read-heavy. It isn't. A per-user RAG at 220M-user scale is write-dominated, the LLM bill dwarfs the infrastructure, and one partitioning decision quietly solves four problems at once.
Start with a workload definition. Complete the workflow in under 10 minutes. Export when your team is ready to decide.