AI infrastructure seed VCs in 2026, grouped by check size

Most "AI investor" lists mix together firms that back AI apps, firms that back AI plumbing, and firms that stopped writing seed checks a while ago. A firm that appears on every list is not necessarily wiring seed money right now. This list focuses on infrastructure and groups firms by typical check size, so you can start from the right tier.

Check sizes below are approximate and change as funds raise new vehicles. Verify each firm's recent deals before you pitch.

What counts as AI infrastructure for this list

Before the rankings, definitions matter. For this piece, AI infrastructure means:

• Model training and fine-tuning platforms
• Inference optimization and serving layers
• Vector databases and embedding stores
• LLM ops, observability, and evaluation tools
• Data labeling, synthetic data, and RLHF tooling
• GPU orchestration and compute marketplaces

Not included: vertical AI apps, AI copilots for sales or support, or general dev tools that happen to use AI. Those lists exist elsewhere. This is plumbing only.

How to read this list

Firms are grouped by the check size they typically write at seed. Some go lower for pre-seed, some stretch higher if leading a large round. Treat the ranges as a starting point, not a quote.

This is not every AI infrastructure seed investor on earth, and inclusion is not a claim that a firm is deploying today. Before you email anyone, confirm two things yourself: a recent AI infrastructure deal (a press release, a founder LinkedIn post, or a portfolio page update), and a check size that fits your round.

Tier 1: Leading $2M to $3M rounds

These firms tend to write the anchor check and set the terms.

Benchmark (San Francisco)
Typical check: $2.5M to $3M
Note: Almost never leads rounds under $2M. If your raise is $1.5M, they will pass even if the product is exceptional.

Accel (Palo Alto)
Typical check: $2M to $3M
Note: Prefers technical founding teams with prior ML research experience. If you have not published, get a warm intro.

Greylock (Menlo Park)
Typical check: $2M to $3M
Note: Strong preference for founders who have built infra at scale before. Previous BigTech ML infra experience opens doors here.

Madrona (Seattle)
Typical check: $2M to $2.8M
Note: Pacific Northwest focus. If you are building in Seattle or Vancouver, lead with that.

Amplify Partners (San Francisco)
Typical check: $2M to $3M
Note: Known for backing technical, infrastructure-heavy founders. A strong technical memo helps.

Tier 2: Writing $1M to $2M checks, willing to co-lead

These firms may anchor a round if the other pieces are committed, or come in as a strong second check.

Costanoa Ventures (Palo Alto)
Typical check: $1.2M to $2M

Uncork Capital (San Francisco)
Typical check: $1M to $1.8M

Lightspeed Venture Partners (Menlo Park)
Typical check: $1.5M to $2M

Bain Capital Ventures (Boston, San Francisco)
Typical check: $1.5M to $2.5M

Susa Ventures (San Francisco)
Typical check: $1M to $1.5M

Crane Venture Partners (London)
Typical check: $1M to $2M
Note: If you are EU-based and your infra solves a compliance or latency problem specific to European deployment, make that the headline.

Firstmark Capital (New York)
Typical check: $1.2M to $2M

Tier 3: $500K to $1M checks, often the second or third money in

These firms often follow rather than lead, and tend to move once a lead is committed.

Village Global (San Francisco)
Typical check: $500K to $750K
Note: Strong community of founder LPs. If you are a second-time founder, the LP network often opens the door before you pitch the partners.

Haystack Ventures (Boston, San Francisco)
Typical check: $500K to $1M

Afore Capital (San Francisco)
Typical check: $500K to $1M

Liquid 2 Ventures (San Francisco)
Typical check: $750K to $1M
Note: Joe Montana's fund. If your product has a sports or performance angle (speed, cost per inference, etc.), lean into that in the deck.

Ludlow Ventures (Detroit, San Francisco)
Typical check: $500K to $750K

Gradient Ventures (Mountain View)
Typical check: $500K to $1M
Note: Google's AI-focused fund. Helps if your tool integrates with Vertex or solves a problem Google Cloud customers care about.

Bloomberg Beta (San Francisco, New York)
Typical check: $500K to $1M
Note: Strong preference for open-source foundations and developer tools. If you have GitHub traction, lead with that.

Pear VC (Menlo Park)
Typical check: $500K to $1M

Tier 4: Pre-seed and small seed, $250K to $500K

If you are raising under $1M total, these firms write the right check size and do not force you to raise more than you need.

Hustle Fund (San Francisco)
Typical check: $250K to $500K
Note: Known for moving quickly at pre-seed. If you have revenue or LOIs, lead with them.

Essence VC (Tel Aviv, San Francisco)
Typical check: $300K to $500K

Fuel Capital (San Francisco)
Typical check: $300K to $600K

Precursor Ventures (San Francisco)
Typical check: $250K to $500K

Everywhere Ventures (Remote)
Typical check: $250K to $400K
Note: Remote-first fund. If your team is distributed, they are more comfortable with that than most other seed firms.

What the data says about cold outreach to these firms

We do not have per-firm reply rates to share. Across all investors, founders on VC Boom have sent 1,962 emails since 2026-04-16, with a 2.5% overall reply rate. Cold email is a low-yield channel, so targeting matters.

AI infrastructure investors tend to be technical. They read GitHub repos. They understand the problem space. A good cold email with a working demo or a benchmarking repo gives them something concrete to forward.

Bad cold emails still fail. The most common mistake: sending the same generic pitch to 200 firms without filtering for stage, check size, or recent activity. If you are doing that, this data on cold email vs. warm intros will save you a month of wasted time.

How to pick 12 firms from this list

You do not email everyone. You pick 12 to 15 based on:

1. Check size match. If you are raising $1.5M and a firm typically writes $3M checks, they will pass. Not because your company is bad, but because the check does not fit their model.
2. Recent activity in your specific subcategory. A firm that just closed two vector database deals is less likely to do a third in the same quarter. Look for firms that do AI infra but have not done your exact thing recently.
3. Geographic and network overlap. If you are in New York and a firm is SF-only with no East Coast portfolio, your close cycle will be slower. Not impossible, just slower.

The sharpest filter is not stage or check size. It is timing. A firm that closed an LLM observability deal 8 weeks ago is unlikely to do another one this quarter. A firm that writes AI infra checks but has not done one in your subcategory yet may be looking for exactly what you are building. Fresh capital is another timing signal: 1,377 investors in our database raised a new fund in the last 12 months, according to SEC filings.

If you want a filtered list based on your deck and your specific raise, score your deck here and Claude will surface the firms that best match your company. It is free, takes 30 seconds, and the output is a target list with contact info, not just names. We check SEC filings and firm portfolio pages, and show the date we checked.