ONGOING INDEPENDENT COVERAGE

INITIATING COVERAGE - NEYSA, GPU Cloud Orchestration

Neysa rents powerful GPUs and managed AI platforms, claiming major cost savings over hyperscalers. It targets enterprises that need steady, secure, and predictable compute power.


What They Do, In Plain Words

  • The Core Problem: Running AI requires powerful GPUs, which are expensive and slow to procure.


  • The Solution: Neysa rents out these GPUs, plus the necessary tools to actually use them.


  • The Platform (Velocis): Their bundled offering includes:



    • GPU-as-a-Service


    • An AI platform layer


    • Inference endpoints for open-source models


    • Live monitoring across clusters


    • MLOps and a marketplace


    • A security layer called Aegis LLM Shield


  • The Real Value: The useful part isn't just access to the chips—it’s the managed software layer sitting right above them.


What They Claim

  • Top-Tier Hardware: Bare-metal or virtual access to L4, L40S, H100, and MI300X chips, backed by high-bandwidth networking. Learn about Velocis


  • Major Cost Savings: 40–60% lower unit economics compared to global hyperscalers.


  • Flexible Commitments: Terms ranging from purely on-demand to 36-month locked commitments.


  • Precise Billing: Fractional and per-minute billing tailored to exact usage.


  • Developer-Ready: Comes with pre-configured stacks, including PyTorch and Hugging Face.


  • Data Sovereignty: Data stays inside the country—a critical factor for regulated and enterprise buyers.


Who Should Consider Them

  • Target Industries: Banks, insurers, manufacturers, research bodies, and AI-native product companies.


  • Company Stage: Teams that have moved past initial experiments and now require steady, predictable compute.


  • Named Use Cases: Predictive maintenance, fraud detection, and claims automation. Read Use Cases


  • For Startups: An invitation-only, 21-month partner programme for funded startups, featuring discounts that scale as milestones are achieved.


5 Questions to Ask Before Shortlisting

  1. Availability: Is the specific chip you need available on the exact date you need it, rather than just "in general"?


  2. True Cost: When you run your own benchmark before signing a long commitment, does their cost advantage actually hold up for your specific job?


  3. Scalability: If you outgrow your current plan, what does the next price step look like?


  4. Lock-in: How hard is it to move your models and data out later?


  5. Support: Who answers the phone at 2:00 AM when a critical training run dies?


Our View

  • Platform > Cost: The cost-saving story is easy to pitch but hard to verify. The platform story—the ecosystem they’ve built around the hardware—is the harder thing to copy.


  • How to Judge Them: Evaluate Neysa on the managed layer above the chips and whether they can guarantee capacity when your demand spikes.


  • The Alternative: If your AI needs are limited to occasional, unpredictable bursts, a traditional hyperscaler may still be the simpler option.