VAST Data is an AI Data Platforms company founded in 2016 and based in New York, United States. It has raised $1.4B in total funding, most recently a Series F in 2026 at a $30B valuation.
| Date | Stage | Amount | Valuation | Lead investors |
|---|---|---|---|---|
| Apr 21, 2026 | Series F | $1B | $30B | Drive Capital, Access Industries |
| Dec 6, 2023 | Series E | $118M | $9.1B |
| Fidelity Management & Research Company |
| May 4, 2021 | Series D | $83M | $3.7B | Tiger Global Management |
| Apr 16, 2020 | Series C | $100M | $1.2B | Next47 |
| Feb 26, 2019 | Series B | $40M | — | Greenfield Partners |
| Jan 1, 2018 | Series A1 | $25M | — | Dell Technologies Capital |
| Jan 1, 2016 | Series A | $15M | — | 83North, Norwest Venture Partners |
Neocloud Nebul deploys DDN's Infinia OS for inference KV caching, alongside NetApp and VAST Data storage.
Micron skips a NAND generation, pulling its Gen 11 roadmap forward with twice G9's bit density, up to 1.5x I/O speed, and over 400 layers in H2 next year.
VAST Data unveils FSGAS, a sandbox stack that persists agent state and tool-call records, built on Nvidia NemoClaw and OpenShell.
DeepL adopts VAST Data's storage and AI OS to support new translation offerings, expecting roughly 3x storage capacity from existing hardware.
ScaleFlux pitches its storage controller chips as a fix for AI data pipelines bottlenecked on memory and storage.
VAST Data argues that stitched-together AI pipelines struggle to keep embeddings, permissions and source data aligned for sensitive enterprise data.
The umbrella platform announced in May 2025 that unifies VAST's DataStore, DataBase, DataEngine, DataSpace, InsightEngine, and AgentEngine into a single operating layer for AI, spanning exabyte-scale data services and agentic workflows across clusters of GPUs in data centers and clouds.
Exabyte-scale unstructured data storage built on the DASE (Disaggregated Shared-Everything) all-flash architecture, which eliminates storage tiering and serves file and object data fast enough to keep large GPU clusters saturated during AI training and inference.
A transactional and analytical database built into the platform that combines the capabilities of a database, data warehouse, and data lake, letting AI pipelines query structured and semi-structured data directly where it is stored.
A real-time data-processing and retrieval service built with NVIDIA that uses NVIDIA inference microservices (NIM) to embed, index, and vectorize data as it lands, powering retrieval-augmented generation over enterprise data.
An auto-scaling AI agent deployment runtime added with the AI OS launch in 2025, providing a low-code environment and an MCP-compatible tool server so agents can invoke data, metadata, functions, and other agents as tools.
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