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This is a historical 2022 snapshot—not a current ranking of cloud startups in 2026. The list also used “cloud computing” broadly: most of these companies built software on top of, around or adjacent to cloud infrastructure rather than competing directly with Amazon Web Services, Microsoft Azure or Google Cloud.
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The list at a glance
| Company | Category | Primary problem | Likely buyer | 2022 momentum signal |
|---|---|---|---|---|
| Amperity | Customer data platform | Fragmented customer records | Marketing, customer data or analytics leader | Profile Accelerator and AWS collaboration |
| Aporia | ML observability | Model drift, bias and unexplained performance | Data science or ML-platform leader | $25 million Series A |
| Filebase | Decentralized object storage | Complexity of using decentralized storage networks | Infrastructure, storage or Web3 team | Cloud-like interface over decentralized networks |
| Imply | Real-time analytics database | Slow or cumbersome interactive analytics | Data-platform or application team | $100 million Series D and reported $1.1 billion valuation |
| Iterative | Open-source MLOps | Reproducibility and collaboration in ML workflows | ML engineering or platform team | DVC, CML and commercial Studio platform |
| Kong | API management and service connectivity | Connecting services across hybrid and multicloud environments | Platform, DevOps or network engineering | Kong Gateway, Kuma and Konnect Cloud |
| Privacera | Data governance and security | Controlling access to sensitive data across platforms | Security, governance, privacy or data leader | 2022 platform releases and Apache Ranger heritage |
| Solo.io | Cloud-native application networking | Complexity in Kubernetes and multicluster systems | Platform engineering or cloud infrastructure | Gloo Mesh integration with Cilium |
| Tetrate | Enterprise service mesh | Consistent connectivity and policy across hybrid clouds | Platform, network or security engineering | Tetrate Service Bridge and managed Tetrate Cloud |
| Wasabi Technologies | Hot object storage | Unpredictable storage and data-transfer economics | IT, backup, media or infrastructure team | 2022 financing and reported customer growth |
CRN’s list reflected the cloud market’s expansion beyond virtual machines and basic infrastructure. In 2022, major opportunities were emerging around multicloud networking, Kubernetes, AI operations, data growth, analytics latency, governance and storage pricing. Gartner’s forecasts cited by CRN projected worldwide public-cloud end-user spending above $495 billion in 2022, up from $411 billion in 2021, with nearly $600 billion forecast for 2023. Those were historical forecasts, not current spending figures.
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1. Amperity: unifying customer data
Amperity addressed a familiar enterprise problem: customer information is scattered across marketing, commerce, service, loyalty and other systems. Its customer data platform used cloud data infrastructure and AI-oriented capabilities to unify records, create customer profiles, build segments and activate insights.
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CRN highlighted Amperity Profile Accelerator and the company’s strategic collaboration with AWS. The important distinction is that Amperity was a cloud-delivered data and marketing platform—not core cloud infrastructure. Its likely economic buyers were marketing, digital, customer-data and analytics leaders.
Amperity’s current pricing page describes usage-based “Amps” pricing with Standard and Enterprise editions, but does not publish dollar prices. It says all plans include the complete platform while support and service levels differ. That sales-led model is more suitable for organizations with substantial first-party data and sophisticated identity-resolution or activation needs than for a small business with a modest customer database. See Amperity’s pricing information.
2. Aporia: monitoring machine-learning systems
Aporia represented the shift from building machine-learning models to operating them responsibly in production. Models can drift as user behavior and source data change; they can also produce biased, unexplained or unreliable results. Aporia’s category was ML observability and monitoring for models running in cloud or hybrid environments.
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Aporia announced a $25 million Series A led by Tiger Global in early 2022, bringing reported total funding to $30 million after its seed round. The funding showed investor interest in ML operations, but it did not by itself establish market leadership, product quality or customer outcomes. Aporia’s funding announcement describes the company’s positioning.
The buyer was typically a head of data science, AI engineering or ML platform engineering. Aporia was not primarily a model-training framework; it addressed the operational visibility needed after models were deployed.
3. Iterative: open-source MLOps for developer workflows
Iterative focused on making data and model work more reproducible and collaborative. Its ecosystem included the open-source DVC and CML projects, as well as commercial products such as Studio. The approach brought familiar software-development practices—version control, automation and repeatable workflows—to datasets, experiments and models.
Iterative integrated with AWS, Azure and Google Cloud, but “cloud-native” did not mean every component was fully managed SaaS. Teams adopting open-source tooling may reduce lock-in and retain flexibility, while taking on more responsibility for deployment, security, operations and support.
Rank #2
Iterative was therefore a different type of ML company from Aporia. Aporia concentrated on observing production models; Iterative concentrated more on the workflow for developing, versioning and collaborating around data and models. Iterative and DVC provide the relevant product and project context.
4. Imply: real-time analytics applications
Imply was founded by creators of Apache Druid, a database designed for fast, interactive analytics. The company targeted applications where conventional warehouses and dashboards could be too slow or cumbersome—for example, user-facing analytics requiring high concurrency and rapid response.
Its 2022 product Polaris was a managed cloud database service intended to simplify infrastructure management for analytics applications. In May 2022, Imply announced a $100 million Series D led by Thoma Bravo at a reported $1.1 billion valuation, bringing reported total funding to $215 million. The announcement came from Thoma Bravo.
Imply did not represent a universal replacement for a transactional database, warehouse or lakehouse. Its stronger fit was real-time, interactive and embedded analytics. A company whose primary workload was batch reporting might gain less from a specialized real-time database than from extending its existing data platform.
Cloud networking and application connectivity
Kong, Solo.io and Tetrate addressed related but distinct layers of cloud-native networking. An API gateway generally handles north-south traffic between external clients and services, including authentication, rate limiting and API policy. A service mesh generally handles east-west traffic between services, adding service identity, telemetry and traffic controls. The categories overlap, but they are not interchangeable.
5. Kong: API management across distributed systems
Kong helped organizations connect APIs and microservices across clouds, Kubernetes clusters, data centers, serverless systems and legacy applications. CRN identified Kong Gateway, the Kuma service mesh and Konnect Cloud.
Its open-source core paired with enterprise and managed offerings. That combination appealed to organizations wanting portability or community-driven technology while still seeking commercial governance and support. The likely buyer was a platform, DevOps, cloud infrastructure or network-engineering team.
Rank #3
The trade-off is that an API gateway or service mesh creates another policy and operational layer. It can solve real authentication, routing, traffic-management and connectivity problems, but adopting it merely because an architecture is described as “cloud-native” can increase configuration, debugging and performance overhead. Kong’s site describes its current product family.
6. Solo.io: making service mesh more consumable
Solo.io focused on API infrastructure, service mesh and application networking for Kubernetes, microservices and multicluster environments. Its products included Gloo Edge, Gloo Mesh and Gloo Cloud. CRN highlighted the integration of Cilium into Gloo Mesh.
The company represented an attempt to make service-mesh capabilities easier for enterprises to consume, particularly where teams needed consistent traffic management, security, observability and connectivity across clusters.
Service mesh still requires platform-engineering capacity. It may be excessive for a small application, a team with only a few services or an organization that has not yet established reliable identity, load balancing and telemetry. A historical funding reference can also be misleading: Solo.io’s own account describes a $3.25 million seed round in September 2015, not a 2022 financing event. The company’s post gives that date.
7. Tetrate: enterprise service mesh
Tetrate targeted enterprises moving from monoliths to hybrid or multicloud architectures. Its Tetrate Service Bridge was an Istio-based platform for consistent connectivity, traffic control, security and observability. CRN also described Tetrate Cloud as a managed Istio-based service-mesh offering.
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For a small or low-complexity architecture, a managed cloud load balancer, API gateway and existing telemetry tools may be simpler. Tetrate and the Istio project provide useful points of comparison.
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8. Filebase: a cloud-like layer for decentralized storage
Filebase addressed developers and enterprises interested in decentralized or Web3 storage networks but unwilling to manage their underlying complexity directly. Its layer-2 object-storage platform provided a cloud-like interface across multiple decentralized storage networks, with potential uses including backup, content delivery, file management and NAS configurations.
Its differentiation was therefore an abstraction layer, not simply a claim of cheaper storage. Buyers would need to examine latency, throughput, durability guarantees, recovery procedures, network availability, geographic redundancy, data residency, encryption, key management and support.
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“Decentralized” does not automatically mean more secure, cheaper or more reliable than hyperscaler object storage. Filebase was most relevant to teams with a clear reason to use decentralized networks and the technical ability to evaluate their operational and legal risks.
9. Wasabi Technologies: predictable hot object storage
Wasabi competed more directly with mainstream cloud infrastructure, although only in object storage. Its proposition was simpler capacity-based pricing with no egress or API-request charges under its stated model. The goal was to make storage costs easier to predict for backup, media, archive-adjacent and general object-storage workloads.
Wasabi announced a $250 million financing package in September 2022, consisting of $125 million in Series D equity and an expanded $125 million debt facility, at a reported valuation above $1.1 billion. In December, it announced an additional $15 million, bringing Series D equity to $140 million and reported total funding above $500 million.
Wasabi also reported more than 40,000 customers, availability in over 100 countries and 13 storage regions in September 2022. Those were company-reported figures, not an independent market-share audit.
A flat storage price is not automatically the lowest total cost. A comparison should include capacity, retrieval and download volume, API requests, minimum-retention rules, replication, backup-software integration, region availability, support, migration costs, compliance requirements and the cost of moving data to another cloud.
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As a later commercial signal, Wasabi’s pricing page currently advertises pay-as-you-go storage starting at $7.99 per TB per month, with no egress or API-request fees under the listed model. That is a 2026 pricing signal, not the price available in 2022. Check the current terms directly before comparing it with Amazon S3, Google Cloud Storage or Azure Blob Storage.
10. Privacera: governing data across clouds
Privacera focused on data security, access control and governance. Enterprises increasingly needed to make data available for analytics while restricting sensitive information, enforcing policy and meeting regulatory obligations across AWS, Azure, Google Cloud, Databricks, Snowflake and on-premises environments.
CRN cited PrivaceraCloud 4.3 and Privacera Platform 6.3, released in May 2022. The company was founded by creators of Apache Ranger and positioned its platform around centralized policy management.
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Governance tools are difficult to implement when data ownership, classification, identity and policy-enforcement responsibilities are unclear. A product cannot substitute for those organizational foundations. Relevant alternatives include Immuta, Collibra, OneTrust and the open-source Apache Ranger, each with different operational and functional trade-offs.
How to interpret “hottest”
CRN’s selection is best read as a companies-to-watch feature, not a purchasing scorecard. A serious evaluation should separate several kinds of evidence:
- Investor momentum: funding and valuation can indicate attention, but not product-market fit, profitability or customer satisfaction.
- Product momentum: launches and platform expansions show activity, but not necessarily adoption.
- Customer traction: named deployments, usage and retention are more useful than a funding headline, although they also require context.
- Ecosystem influence: hyperscaler partnerships and open-source adoption can improve distribution and credibility.
- Technical maturity: reliability, security, integrations, support and operational burden matter more than positioning language.
- Business durability: buyers should consider the vendor’s ability to support pricing, roadmap, service levels and ownership over time.
Also note that “startup” did not necessarily mean early-stage. Imply’s reported Series D valued it at $1.1 billion, while Wasabi later reported a valuation above $1.1 billion. In this context, startup was ecosystem terminology, not a claim that every company was small or newly founded.
What the 10 companies had in common
They were not all building another generic public cloud. They were targeting complexity created by cloud adoption:
- Amperity addressed fragmented customer data.
- Aporia and Iterative addressed different stages of the machine-learning lifecycle.
- Imply addressed latency and concurrency in interactive analytics.
- Kong, Solo.io and Tetrate addressed distributed application connectivity.
- Privacera addressed governance across data environments.
- Filebase and Wasabi addressed different models for object storage.
That is why the list can appear inconsistent if the companies are compared as direct competitors. The more useful comparison is by layer: Amperity sits at the data and application layer; Aporia and Iterative near AI and ML operations; Imply in analytics; Filebase and Wasabi in storage; Kong, Solo.io and Tetrate in application networking; and Privacera in data governance and security.
The original list remains valuable as a dated record of what the cloud market considered strategically important in the first half of 2022. It should not be treated as evidence that all 10 companies remained equally successful, independent or commercially unchanged by 2026. Determining what happened after the snapshot requires a separate current-status review.
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