Startup profile for Tigris: latest funding, latest known valuation, total disclosed funding, investors, status, sources, and why the company is interesting. Part of the Venture Capital Tracker startup directory.
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Tigris funding, valuation and investors

Globally distributed S3-compatible object storage built for AI workloads.

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Funding, valuation & investors

Answer-first snapshot

Latest funding

Series A

$25M · October 2025

Latest known valuation

Not publicly disclosed

Total disclosed equity funding

$25M

Excludes debt, grants, acquisitions, secondaries, and IPO proceeds.

Current status

private

series a · Sunnyvale, California, USA

Investors in latest funding

Lead: Spark Capital

Other: Andreessen Horowitz

Sources: latest funding Last verified: 2026-07-25

Overview

Tigris Data offers globally distributed, S3-compatible object storage optimized for AI training, inference, and serverless applications. Founded in November 2021 by engineers from Uber's storage infrastructure team, Tigris raised a $25M Series A in October 2025 led by Spark Capital with Andreessen Horowitz participating. The platform provides a single global endpoint, zero egress fees, and low-latency access for datasets and model artifacts. Tigris reports 4,000+ customers and data centers in Virginia, Chicago, and San Jose, expanding to London, Frankfurt, and Singapore.

Why Tigris is interesting

Ex-Uber storage team built multi-region object store with zero egress — Spark-led $25M Series A with a16z returning from seed as AI training demand exploded.

Product & use cases

Tigris is S3-compatible object storage with automatic global distribution, zero egress fees, and a single endpoint — designed for AI datasets, checkpoints, and low-latency serving.

  • AI teams storing training datasets accessible from any GPU region
  • S3 migration without rewriting application code
  • Multi-cloud workloads needing portable object storage

Key facts

  • Series A (Oct 2025): $25M led by Spark Capital; a16z participated (Tigris blog)
  • 4,000+ customers; 8x annual growth since founding
  • Zero egress fees; globally distributed S3-compatible API
  • Founded by ex-Uber storage engineers

Funding history (newest first)

Series A

2025-10 $25M

Source: https://www.tigrisdata.com/blog/series-a/

Investors in our directory

Funds linked from Tigris's profile — open a fund page for stage focus and related deal articles.

Andreessen Horowitz

Competitive landscape

Edge: Founders built Uber-scale storage; own hardware in datacenters rather than reselling Big Cloud — zero egress as structural pricing wedge.

Object storage is price-sensitive; Tigris competes on AI workload latency, global semantics, and egress economics — must prove reliability versus hyperscaler S3.

  • Amazon S3 incumbent

    Default object store; egress fees and regional complexity.

  • Cloudflare R2 direct

    S3-compatible zero egress competitor.

  • MinIO alternative

    Self-hosted S3 API; operational burden on teams.

Notable stories

  • Martin Casado (a16z) backed Tigris seed and Series A — thesis that AI redefines storage like AWS redefined compute (Tigris Series A blog, Oct 2025).

Industries

Infrastructure & Cloud AI & Machine Learning Developer Tools

FAQs about Tigris

Practical answers founders, operators, and investors typically search for.

Tigris provides globally distributed, S3-compatible object storage optimized for AI and serverless workloads.
Spark Capital led Series A; Andreessen Horowitz participated. See /fund/andreessen-horowitz.
Tigris offers S3-compatible APIs with zero egress and global distribution from one endpoint.
October 2025 — $25M.
Engineers from Uber's storage infrastructure team including CEO Osama Khan.
No — zero egress fees is a core pricing claim.
Virginia, Chicago, San Jose; expanding to London, Frankfurt, Singapore.
Managed cloud service with S3-compatible API — not self-hosted MinIO.
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By Venture Capital Tracker

Last updated:

Editorial note: AI tools assisted with research, structure, or drafting. Venture Capital Tracker retains human editorial responsibility for factual accuracy, relevance, and source quality before publication.