If vLLM already solved LLM serving, why did SGLang appear?
Summary
Most enterprises don’t realize it yet but their AI ambitions are quietly being throttled by outdated infrastructure, runaway costs, and compliance roadblocks. The numbers tell the story: only 13% of CPUs and 20% of memory provisioned in the cloud are actually used. Meanwhile, 40% of companies admit to overprovisioning resources, and 35% say their expensive GPUs sit idle.
That’s millions lost in sunk costs before models even reach production.
This whitepaper explores how AI Acceleration Cloud Systems (“Neoclouds”) like Neysa Velocis address critical challenges in enterprise AI adoption: cost overruns, governance, fragmented toolchains, and performance bottlenecks.
ROI from Gen AI
92% of early adopters report their generative AI investments are paying off. Benefits include efficiency gains (88%), improved customer experience (84%), and faster innovation (84%) (Snowflake, 2025).
Performance Bottlenecks
Over 30% of companies face bandwidth shortages when scaling AI workloads (Flexential, 2024).
Strategic Pillars of Neocloud Adoption
Flexibility
open integration, modular architecture, multi-deployment models.
Predictive Scalability
auto-scaling with ML-based telemetry, optimized GPU usage, cost dashboards.
Control & Transparency
dashboards, granular GPU tracking, governance compliance (GDPR, HIPAA).
Seamless Co-existence
interoperability with AWS, Azure, GCP, and easy exit options (no vendor lock-in).
Implementation Roadmap:
Start with audits and pilots, align stakeholders, run proof-of-concepts, evaluate governance, then scale with phased rollout.
AI is no longer a technology of the future. It is firmly embedded in the present, actively shaping enterprise strategy, operations, and competitive advantage today.
Take the next step
To learn more about the Neysa solutions featured in this story, please contact your Neysa representative
