“The next great cloud isn’t one cloud—it’s all clouds, stitched together by intention.” – Anonymous CTO at an AI Infra Summit
Neoclouds and the AI Renaissance: Stitching the Future of Cloud Strategy
Once upon a time, the cloud was a monolith—Amazon, Microsoft, and Google carved up the market like empires of old. Enterprises picked a side, swore fealty, and architected their workloads accordingly. But as artificial intelligence and machine learning began reshaping the very fabric of how businesses operate, the rigidity of the “one cloud to rule them all” model started to crack. Enter the neoclouds—specialized, nimble, AI-optimized cloud providers that aren’t trying to be everything for everyone, just the best at something specific.
A New Chapter in Cloud Evolution
The term “neocloud” may not have appeared in your last AWS Summit keynote, but it’s increasingly used in strategy sessions, analyst briefings, and boardrooms with a forward-looking CTO at the table. Neoclouds are emergent players or cloud services that sit adjacent to hyperscalers, offering hyper-specialized infrastructure optimized for AI, GPU-intensive compute, data sharing, fine-tuning, or MLOps orchestration.
The evolution of cloud went something like this:
- Gen 1 (Early 2000s): VMs and IaaS. AWS EC2, S3.
- Gen 2 (2010s): Platform services, managed DBs, Kubernetes, global scale.
- Gen 3 (Now): AI-native infrastructure, multi-cloud abstractions, data-first platforms, and neocloud integration.
The Rise of the AI-Native Cloud
As LLMs and GenAI workloads exploded post-2022, hyperscalers weren’t the only ones racing to offer GPU availability. Players like CoreWeave, Lambda Labs, Paperspace, and Together AI began carving out niches. Their secret sauce? Extreme focus. No baggage. And most importantly—availability of compute when the hyperscalers had waitlists.
These neoclouds offered:
- Lower-cost, GPU-focused compute (e.g., NVIDIA A100s, H100s)
- Egress-free networking for training workloads
- ML-first developer experience (preinstalled environments, containerized jobs, usage-based billing)
- High-bandwidth storage optimized for model I/O
Thought Leaders & Advocates
The neocloud narrative has been shaped and championed by builders and investors at the intersection of AI and infrastructure:
- Jonathan Frankle (MosaicML, now Databricks): Advocated for open-source LLM training and showed why you don’t need a hyperscaler to train state-of-the-art models.
- Chris Lattner (Modular AI): Emphasizes efficient, portable compute layers that can span clouds.
- Arun Chandrasekaran (Gartner): Tracks emerging cloud infrastructure trends and coined frameworks around AI-native infrastructure design.
- Sarah Guo (Conviction): A prominent voice in early-stage AI investing, often touting infrastructure innovation as the missing layer between today’s AI hype and tomorrow’s adoption.
What “Good” Looks Like
Mistral AI, a European open-weight LLM company, is a standout example. They trained their models on a blend of hyperscaler and neocloud infrastructure, minimizing costs and vendor lock-in. By leaning on GPU-optimized neoclouds during peak times, they accelerated time to market without breaking the bank.
Runway ML, known for video generation, uses CoreWeave’s infrastructure to serve inference-heavy workloads. Their real-time needs (low latency, high throughput) were a perfect match for a neocloud with data center footprints tuned for media and rendering.
In both cases, teams avoided reinventing the wheel on infra. They plugged into a neocloud that already spoke their language: AI-native, not enterprise-Java-native.
What “Bad” Looks Like
We’ve also seen companies bet on neoclouds for the wrong workloads—standard web apps, simple APIs, or long-tail microservices. That’s like trying to do your taxes on a supercomputer: expensive and unnecessary. These missteps often stem from:
- Overengineering for the “AI strategy” buzzword
- Chasing temporary GPU availability without cost modeling
- Ignoring interoperability or vendor lock-in from niche providers
And then there’s security. Some neoclouds lack the hardened maturity of AWS or GCP in terms of compliance, logging, IAM, and global resilience. Without compensating controls, startups have opened themselves to breach risks or observability blind spots.
How to Think About Neoclouds in Your Cloud Strategy
Neoclouds aren’t a replacement—they’re a force multiplier. Used wisely, they unlock new performance frontiers. Used poorly, they become another shadow IT rabbit hole. The key is intentional design:
1. Workload Fit
- Use neoclouds for:
- AI/ML training jobs
- GPU inference (LLMs, diffusion, video)
- Synthetic data generation
- Rendering, simulation, vector search
- Don’t use them for:
- CRUD apps
- Business logic-heavy systems
- Compliance-heavy workloads unless verified
2. Cloud Tapestry
Think of your cloud strategy as a woven fabric, not a monolith:
- Hyperscaler: Core services, data gravity, managed databases, global scale
- Neocloud: Specialized compute, burst capacity, cost-optimized training
- SaaS Infrastructure: Vector DBs, model endpoints, feature stores
Use infrastructure-as-code and policy engines (OPA, HashiCorp Sentinel) to enforce consistency across this tapestry.
3. Toolchain Compatibility
- Ensure your orchestrators (e.g., Ray, Kubernetes, Slurm, Flyte) can run multi-cloud
- Use abstraction layers (like Modal, Replicate, or BentoML) for portable inference
4. FinOps and Monitoring
- Track GPU hour costs, not just compute hours
- Include cost-to-performance metrics (e.g., $/token generated or $/epoch trained)
- Avoid surprises in bandwidth and storage latency
Wrapping up…
In 2020, your competitive edge was moving to the cloud. In 2023, it was adopting AI. In 2025, it will be how strategically you use cloud for AI.The modern cloud strategist doesn’t just pick a cloud—they compose one. And neoclouds? They’re the new threads in that fabric—lighter, stronger, and more specialized than ever before.
