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CRN’s 2024 list highlighted ten cloud-infrastructure companies gaining momentum as artificial intelligence increased demand for GPUs, high-performance storage, Kubernetes expertise, cloud-cost control and multi-cloud networking. The companies were not ranked from first to tenth, and “hottest” was an editorial description—not a standardized score or investment recommendation.
This is a historical snapshot of the first half of 2024. Funding, valuations, product availability and company status may have changed since then.
What made a cloud startup “hot” in 2024?
CRN’s selection reflected signals such as major funding rounds, high-profile cloud partnerships, AI-related product launches, differentiated infrastructure technology and relevance to major enterprise problems. It also captured companies positioned to complement or challenge AWS, Microsoft Azure and Google Cloud.
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CRN reported that enterprise cloud-infrastructure spending exceeded $76 billion in the first quarter of 2024, up 21% year over year, citing Synergy Research Group. That is a dated market statistic, not a current measure of cloud spending.
#1 Best Overall
The ten companies operate at very different layers of the stack:
| Company | Primary category | Cloud problem addressed |
|---|---|---|
| CAST AI | Kubernetes optimization | Cost and resource efficiency |
| Celestial AI | AI hardware interconnect | Compute-to-memory bandwidth |
| CoreWeave | Specialized cloud | GPU capacity |
| DuploCloud | Cloud automation | Infrastructure delivery and governance |
| Prosimo | Multi-cloud networking | Connectivity, routing and visibility |
| Pulumi | Infrastructure as code | Developer-oriented infrastructure management |
| Spectro Cloud | Kubernetes management | Cluster fleets across cloud, data center and edge |
| Upbound | Control planes | Self-service infrastructure APIs |
| Vultr | Cloud infrastructure | Accessible compute, storage and GPU capacity |
| WEKA | AI data infrastructure | High-performance data delivery |
CRN’s original list provides the source context for the companies and market framing.
The 10 companies
1. CAST AI
CAST AI provides Kubernetes automation and cloud-optimization software. Its platform analyzes clusters and automates functions such as scaling, provisioning, bin packing and workload placement across AWS, Azure and Google Cloud.
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Best fit: Organizations with significant Kubernetes spending. Potential drawback: It may be unnecessary for small, static or lightly used clusters, or for teams unwilling to delegate infrastructure changes.
2. Celestial AI
Celestial AI develops Photonic Fabric, an optical connectivity technology designed to disaggregate compute and memory. Its target is the hardware layer beneath AI systems: higher bandwidth and memory capacity, lower latency and potentially lower power consumption than conventional interconnect approaches.
CRN reported that Celestial AI raised a $175 million Series C in 2024, led by investors including AMD Ventures and Samsung Catalyst, to support commercialization. This is not a conventional cloud service that an application developer can simply provision.
Best fit: Chipmakers, accelerator developers, server manufacturers, hyperscaler infrastructure teams and data-center architects. Potential drawback: Commercialization and ecosystem adoption are central risks for hardware infrastructure companies.
Rank #2
3. CoreWeave
CoreWeave is a specialized cloud provider focused on GPU infrastructure for artificial intelligence, large language models and other compute-intensive workloads. Its cloud platform is positioned as an alternative or complement to general-purpose hyperscalers.
CRN reported that CoreWeave secured $1.1 billion in new funding in May 2024. It also repeated company claims that some workloads could be up to 35 times faster and 80% less expensive than public-cloud alternatives. Those figures require workload-specific benchmark context and should not be generalized.
Best fit: AI developers, inference providers, research organizations and companies seeking GPU capacity. Evaluate: GPU model and memory, capacity availability, regions, storage throughput, networking, egress, support, compliance and managed-service breadth.
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4. DuploCloud
DuploCloud is a DevOps-automation platform that translates higher-level application requirements into cloud configurations. It aims to make infrastructure as code, security, availability and compliance more accessible to development teams.
The company attracted attention because many organizations need repeatable cloud environments but lack a large platform-engineering team. Abstraction can speed up delivery, but it does not eliminate the need to review networking, security policies, upgrades, disaster recovery and unusual architectures.
Best fit: Startups and mid-market companies building standardized environments. Potential drawback: Mature platform teams may prefer more bespoke control and already have extensive internal automation.
5. Prosimo
Prosimo provides a multi-cloud infrastructure stack covering networking, security, observability, performance and cost management. Its platform is designed to support distributed applications and AI workloads through private connectivity, network policy, application-driven routing and operational visibility.
Its relevance came from the reality that multi-cloud operations are more than a connectivity problem. Routing, policy enforcement, troubleshooting and cost control become harder as applications and data cross cloud boundaries.
Rank #3
Best fit: Large organizations with complex multi-cloud or distributed AI environments. Potential drawback: It adds another policy and management layer, and may be excessive for a simple single-cloud deployment.
6. Pulumi
Pulumi provides infrastructure-as-code tools that let teams manage cloud resources with familiar programming languages. Its offerings also target policy, infrastructure visibility, analytics and multi-cloud platform engineering.
Pulumi’s differentiation is a developer-oriented approach to infrastructure. Reusable abstractions, typed languages, testing and ordinary software-development workflows can appeal to engineering teams managing complex environments.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsBest fit: Organizations that want infrastructure managed through software-engineering practices. Potential drawback: Programming-language flexibility can introduce additional complexity around modules, state, secrets, testing and ownership. Teams with deeply established Terraform workflows should account for migration costs before switching. See the official pricing page for current commercial terms.
7. Spectro Cloud
Spectro Cloud manages the Kubernetes lifecycle across public clouds, data centers and edge locations. Its Palette platform and Palette EdgeAI offering address the challenge of keeping Kubernetes clusters and software stacks consistent across heterogeneous environments.
AI workloads helped raise the importance of this problem because clusters may need specialized components, repeatable upgrades and reliable operation outside a central cloud region.
Best fit: Enterprises managing many clusters across cloud, on-premises, manufacturing, retail, telecom or edge locations. Potential drawback: Edge operations introduce hardware, offline-operation, observability and upgrade challenges.
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8. Upbound
Upbound is associated with Crossplane, an open-source control-plane technology. The approach lets platform teams expose approved infrastructure resources through internal APIs instead of requiring developers to manage every cloud-provider detail directly.
This addresses a central platform-engineering goal: self-service infrastructure with centralized governance. However, adopting Crossplane is not the same as receiving a turnkey internal developer platform. Teams need expertise in Kubernetes controllers, API design, compositions, lifecycle management and provider behavior.
Best fit: Platform teams building standardized infrastructure APIs across providers. Potential drawback: Control-plane architecture requires meaningful design and operational investment. Open source also does not mean zero cost; engineering, support, governance and operations still matter.
9. Vultr
Vultr offers shared and dedicated CPUs, bare metal, block and object storage, networking, Kubernetes and NVIDIA GPU capacity. It also launched Vultr Cloud Inference in March 2024.
CRN reported that Vultr served 1.5 million customers in 185 countries. Those figures should be treated as company- or publication-reported rather than independently audited. Vultr’s appeal was its relatively straightforward infrastructure offering, geographic reach and focus on developers, startups and distributed workloads.
Best fit: Developers, SaaS companies, game studios, agencies and teams seeking alternative cloud regions or simple infrastructure. Evaluate: Region availability, GPU inventory, backups, bandwidth, support, compliance and managed-service depth. See the official pricing page for current rates.
10. WEKA
WEKA provides a high-performance data platform for AI, machine learning and GPU workloads across cloud and on-premises environments. Its premise is that expensive accelerators cannot perform well if data cannot be delivered quickly and consistently.
CRN reported a $140 million Series E in May 2024 and a resulting $1.6 billion valuation. These are dated financing claims and do not establish product maturity, retention, margins or long-term sustainability.
Best fit: Enterprises, research institutions and cloud operators with measurable AI or analytics data-access bottlenecks. Potential drawback: High-performance storage may be excessive for ordinary file workloads, and storage acceleration cannot compensate for weak data preparation, insufficient compute or inefficient models.
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The infrastructure problems behind the list
GPU scarcity and cost
AI training and inference require specialized processors, memory and networking. CoreWeave and Vultr addressed access to GPU infrastructure, while CAST AI focused on making existing cloud resources more efficient.
Data movement
AI systems need rapid access to large datasets. WEKA targeted the data pipeline, while Celestial AI pursued the deeper hardware and interconnect challenge of moving data between compute and memory.
Kubernetes complexity
Kubernetes can standardize application deployment but creates operational demands around scheduling, scaling, upgrades, security and cluster lifecycle. CAST AI, Spectro Cloud and Upbound approach different parts of that problem: optimization, fleet management and infrastructure control planes.
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Prosimo addressed connectivity and policy across distributed environments. Pulumi, DuploCloud and Upbound addressed different forms of infrastructure standardization and automation. These tools may reduce inconsistency, but abstraction can also introduce another control plane, security boundary and vendor dependency.
Which company fits which cloud problem?
| Primary need | Most relevant companies | What to investigate |
|---|---|---|
| Lower Kubernetes costs | CAST AI | Actual utilization, automation permissions and savings methodology |
| GPU cloud capacity | CoreWeave, Vultr | GPU type, availability, storage, networking and egress |
| Infrastructure as code | Pulumi | Language preference, provider support and migration effort |
| Higher-level cloud automation | DuploCloud | Security, compliance, customization and platform ownership |
| Infrastructure APIs | Upbound/Crossplane | Control-plane expertise and lifecycle design |
| Kubernetes fleet management | Spectro Cloud | Cluster scale, edge operation and upgrade workflows |
| Multi-cloud networking | Prosimo | Existing WAN, native cloud networking and security architecture |
| AI data throughput | WEKA | End-to-end pipeline performance, metadata and replication |
| Optical AI interconnect | Celestial AI | Technical partnership and ecosystem readiness |
How to interpret the funding and performance claims
Funding and valuation show investor interest, not proof of reliable production performance, customer retention, sustainable unit economics or product-market fit. The companies on this list also vary widely in maturity: some are software scale-ups, some are infrastructure providers, some are open-source ecosystem companies and some are hardware developers with longer commercialization cycles.
Likewise, “cheaper than the hyperscalers” is not a meaningful comparison unless the analysis normalizes the processor or GPU generation, memory, region, utilization, storage, networking, data transfer, support level and contract term. Vendor-reported claims—such as CAST AI’s more-than-50% savings claim or CoreWeave’s speed and cost comparisons—should be tested against the reader’s own workloads.
Most of these companies operate alongside AWS, Azure or Google Cloud rather than replacing them entirely. A specialized provider may improve one layer of the stack while leaving a customer dependent on hyperscaler services elsewhere.
Bottom line
CRN’s ten-company list captured a key 2024 shift: cloud innovation was moving into specialized layers around the hyperscalers. GPU capacity, AI data delivery, optical interconnects, Kubernetes operations, infrastructure automation and multi-cloud control all became strategic problems.
The list is most useful as a map of that market—not as a league table. The right company depends on whether the actual constraint is compute availability, data throughput, cloud waste, cluster operations, infrastructure standardization or network complexity.
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