Silk secured a $45 million growth capital facility led by Avenue Capital Group to expand its agentic AI data platform, targeting engineering growth, specialized cloud providers, and deeper hyperscaler enterprise adoption.
Silk closed a $45 million growth capital facility, positioning itself as a purpose built data platform for agentic AI workloads. The financing was led by Avenue Capital Group’s Growth Lending Strategy, with participation from Sequoia Capital, S Capital VC, Vintage Investment Partners, Ibex Investors, West Coast Equity Partners, and others.
This is a growth capital facility (debt oriented financing) rather than a pure equity round. Avenue Capital, which manages more than $9 billion in assets and focuses on flexible capital for high growth venture backed technology companies, underwrote Silk’s recurring enterprise revenue base and its positioning in an inflection point market. Tony Pandjiris, Senior Portfolio Manager of Avenue’s Growth Lending Strategy, highlighted the company’s traction with global enterprise customers and cloud providers, along with the measurable economics of the platform.
Proceeds are earmarked for three priorities:
- Expanding engineering investment in the Silk data platform.
- Scaling go to market coverage of neocloud, sovereign, and inference cloud providers.
- Deepening the enterprise footprint across Amazon Web Services, Microsoft Azure, and Google Cloud.
CEO and founder Dani Golan framed the raise around a fundamental market shift: “The shift from training centric infrastructure toward autonomous agent based production is happening now. Organizations are spending enormous sums on compute only to wait on the data layer beneath it. This capital lets us build ahead of agentic demand and bring AI to the data.” He further noted that neocloud, sovereign, and inference clouds have become the new center of gravity for AI infrastructure, and that Avenue’s approach matches the speed required in this forming category.
Agentic AI workloads are described as the dominant driver of AI infrastructure demand. Supporting data cited includes more than half of all traffic across Cloudflare’s global network now generated by machines rather than humans, and daily AI agent requests growing more than 1,700% year over year. These agents operate at machine pace and produce bursty access patterns that general purpose cloud data infrastructure was not designed to absorb. The resulting problems (unpredictable latency, contention between agents and production users, and unsustainable cost curves) create a clear bottleneck at the data layer.
Silk’s thesis is that solving this data layer constraint is essential for moving agentic systems from pilot to production and for enabling cloud providers to move beyond selling GPU capacity into higher value enterprise AI services.
What is Silk.ai?
Silk (formerly Kaminario) is a software defined cloud storage and data platform headquartered in Needham, Massachusetts, with operations tied to Israel. Founded in 2008 by Dani Golan (who previously held executive roles at EMC and led Performix Technologies before its acquisition by NICE Systems), the company evolved from an all flash storage hardware business into a software defined cloud data layer focused on mission critical workloads. It rebranded as Silk around 2020.
The platform sits between cloud infrastructure and customer databases/applications. It delivers high performance, predictable I/O without requiring application rearchitecting. Key claimed capabilities include:
- Up to 10x performance improvement.
- Up to 69% lower cloud costs.
- Real time access to live production data for AI inference, RAG pipelines, and agentic systems without ETL delays or stale copies.
- Ability to absorb bursty workloads with consistent sub-millisecond latency.
- Instant, thin clones (via features such as Silk Echo) that isolate AI workloads from production systems.
- Support for mixed workloads, data copy management, and multi cloud environments (AWS, Azure, Google Cloud).
Recommended: CADDi Raises $114 Million In Series D Funding Round
Customer examples cited on the company site include $2.8 million in annual cloud storage savings at Franciscan Healthcare and 32× faster analytics/ETL workflows at Sentara Health. The company has also validated support for the latest AI optimized cloud instance families and recently appointed Aaron Shoffa as Chief Business Development Officer to lead alliances with emerging AI infrastructure providers.
Prior capital raises include a $30 million growth capital commitment from Trinity Capital in January 2025 (focused on scaling in finance, healthcare, insurance, and retail amid rising AI data demands) and earlier equity rounds (including a $55 million Series B led by S Capital in 2021 with participation from Sequoia and others). Aggregated figures from industry trackers place total capital raised in the range of roughly $166–370 million depending on the source and inclusion of historical Kaminario era funding; the latest $45 million facility adds non dilutive or lightly dilutive growth capital on top of an existing recurring revenue base.
Silk differentiates itself as purpose built for the specific demands of agentic and real time AI workloads (high concurrency, bursty access, low latency, and the need to operate against live production data) rather than general purpose cloud storage or training centric infrastructure. By removing the need for extensive replication, caching layers, or complex pipelines, the platform aims to simplify architecture while controlling costs as inference becomes a larger share (estimated 60–70%) of AI compute spend.
The raise strengthens Silk’s balance sheet at a moment when the category of data platforms optimized for agentic AI is still forming. It enables continued product investment ahead of demand, expanded coverage of specialized AI cloud providers, and deeper penetration of the major hyperscalers. For enterprises, the value proposition centers on running production AI workloads with higher performance and lower cost without rearchitecting applications. For cloud providers and neoclouds, it offers a data layer that can absorb machine paced agent traffic more effectively than native general purpose offerings.
The $45 million facility reflects investor conviction in Silk’s commercial traction, the urgency of the agentic data bottleneck, and the company’s ability to deliver measurable performance and cost advantages in a rapidly scaling market.
Please email us your feedback and news tips at hello(at)techcompanynews.com
