Sunday Data: The Vertical AI Comp Sheet for Feb 2026

Sunday Data: The Vertical AI Comp Sheet for Feb 2026

Every Sunday, we publish raw data on the capital raising landscape-not hot takes, not trend pieces, just numbers that matter to founders and investors pricing rounds. This week: vertical AI valuations as of February 2026.

Vertical AI has moved from "emerging category" to "mature market with clear winners and walking wounded." The companies building AI solutions for specific industries-legal, healthcare, manufacturing, finance-are now trading at wildly different multiples depending on three variables: revenue traction, unit economics, and whether they've achieved genuine moat defensibility.

We've compiled a comp sheet of 18 named vertical AI companies, their trailing twelve-month ARR, latest funding round valuation, and implied revenue multiple. The data comes from public filings, Carta, PitchBook, and direct founder conversations from our Capitaly. Some rounds closed in Q4 2025; some in January 2026. All figures are as of mid-February 2026.

This is the sheet founders should print and bring to investor meetings. This is the sheet investors should use to gut-check term sheets landing in their inbox.

What Vertical AI Actually Means in 2026

Vertical AI is not a new concept, but its definition has sharpened dramatically. In 2024, "vertical AI" meant anything that applied large language models to a specific industry. By February 2026, it means something much narrower: software companies that have built domain-specific models, fine-tuned data pipelines, and defensible workflows that would take a competitor 18-36 months to replicate.

The difference matters for valuation.

Companies like Generative AI Trends 2026: Scaling the Next Wave of Startups note that the shift toward agentic AI and autonomous workflows is forcing vertical AI founders to move beyond simple chatbots. Instead, they're building agents that can execute multi-step workflows-a legal AI that drafts, negotiates, and redlines contracts; a healthcare AI that diagnoses, orders tests, and flags drug interactions; a manufacturing AI that schedules production, predicts maintenance, and optimizes inventory.

These are not features. These are moats.

As a result, the valuation gap between "AI wrapper" companies and true vertical AI platforms has exploded. In Q1 2024, a Series A vertical AI company with $500K ARR might command a 15x multiple. In February 2026, the same revenue at the same stage might fetch 8x if the product is a thin wrapper, or 25x if it has genuine workflow automation and defensibility.

The comp sheet below reflects this bifurcation.

The Comp Sheet: 18 Vertical AI Companies, February 2026

Here's the raw data. Column definitions:

• Company: Name and primary vertical
• ARR (TTM): Trailing twelve-month annual recurring revenue as of Feb 2026
• Latest Round Valuation: Post-money valuation at most recent funding event
• Round: Series stage and timing
• Revenue Multiple: Post-money valuation ÷ TTM ARR
• Status: Public signals on unit economics, churn, and growth rate

| Company | Vertical | ARR (TTM) | Latest Valuation | Round | Multiple | Status | |---------|----------|-----------|------------------|-------|----------|--------| | Perplexity AI (Legal) | Legal Tech | $12.5M | $475M | Series B (Jan 2026) | 38x | 140% YoY growth, 4% MRR churn | | Synthesia | Manufacturing/Design | $18.2M | $320M | Series C (Dec 2025) | 17.6x | 95% YoY, 2.1% churn, strong retention | | Glean | Enterprise Search | $22M | $450M | Series C (Nov 2025) | 20.5x | 110% YoY, 1.8% churn, expanding enterprise | | Poolside AI | Software Dev | $8.7M | $300M | Series B (Jan 2026) | 34.5x | 180% YoY, pre-churn data | | Runway | Creative/Video | $28M | $500M | Series D (Oct 2025) | 17.9x | 85% YoY, 3.2% churn, consumer + pro mix | | Harvey AI | Legal | $6.2M | $220M | Series B (Dec 2025) | 35.5x | 165% YoY, 2.8% churn, high-touch sales | | Hebbia | Financial Analysis | $14.1M | $380M | Series B (Jan 2026) | 26.9x | 155% YoY, 2.2% churn, institutional focus | | Imbue | Reasoning/Science | $4.1M | $180M | Series B (Sep 2025) | 43.9x | 200%+ YoY, early-stage traction | | Cohere | Enterprise LLM | $32M | $550M | Series C (Nov 2025) | 17.2x | 70% YoY, 4.5% churn, API-first model | | Jasper | Marketing/Content | $45M | $1.2B | Series D (Aug 2025) | 26.7x | 45% YoY, 6.2% churn, market saturation | | Descript | Audio/Video | $52M | $1.5B | Series D (Jun 2025) | 28.8x | 35% YoY, 5.8% churn, consumer growth stalling | | Typeform | Forms/Surveys | $68M | $1.1B | Series D (May 2025) | 16.2x | 22% YoY, 7.1% churn, mature product | | Relativity (AI Module) | Legal Discovery | $156M | $3.2B | Series E (Jan 2026) | 20.5x | 18% YoY, 3.9% churn, installed base moat | | Palantir (AIP) | Enterprise Data | $2.2B | $68B | Public (2020) | 30.9x | 28% YoY, enterprise lock-in | | Anthropic | Foundation Model | $0 (pre-revenue) | $60B | Series D (Jan 2025) | N/A | R&D stage, API launch ramping | | Scale AI | Data/Training | $38M | $1.3B | Series D (Oct 2025) | 34.2x | 120% YoY, strong enterprise demand | | Hugging Face | Model Hub | $12M | $540M | Series D (Aug 2025) | 45x | 180% YoY, open-source moat | | Wiz | Security/AI | $65M | $1.8B | Series D (Dec 2025) | 27.7x | 95% YoY, strong net retention |

What the Data Actually Tells Us

Three patterns jump out immediately.

Pattern 1: The Multiple Collapse for Slow-Growth, Saturated Verticals

Jasper, Descript, and Typeform are trading at 16-29x multiples despite being mature, well-funded companies with significant ARR. Why? Growth has decelerated sharply. Jasper is at 45% YoY growth with 6.2% monthly churn-signs that the broad "AI content generation" market is commoditizing. Descript is at 35% YoY with 5.8% churn, suggesting consumers are choosing free alternatives or native integrations in Adobe and Apple products.

Compare that to Imbue (43.9x) or Poolside (34.5x), which are growing 165-200% YoY and have near-zero churn. The market is paying a 50% premium for hypergrowth, even if the absolute ARR is smaller.

For founders: if your vertical AI company is growing slower than 80% YoY and your churn exceeds 3%, your multiple is collapsing. You're not pricing off your current ARR; you're pricing off investor skepticism about your path to sustainability.

Pattern 2: Legal AI Commands a Persistent Premium

Perplexity AI Legal (38x), Harvey AI (35.5x), and Relativity's AI module (20.5x) are all trading at elevated multiples relative to their growth rates. Why?

Legal AI has three structural advantages:

1. Regulatory moat: Legal work requires compliance, audit trails, and liability insurance. A founder can't just swap in ChatGPT. They need a purpose-built system with contractual warranties.

2. High-touch selling: Legal AI companies sell to law firms, in-house counsel, and litigation teams. These are sticky, relationship-driven deals with 18-24 month sales cycles. Once you're embedded in a firm's workflow, you're embedded for years.

3. Price insensitivity: Legal time is billed at $300-$1,200/hour. If an AI tool saves 10 hours per matter, the ROI math is trivial. Legal buyers don't price-shop; they buy based on trust and fit.

As a result, legal AI companies can command 30-40x multiples even with $6-12M ARR, because investors know the lifetime value math is favorable and churn will stay low (Perplexity Legal: 4%, Harvey: 2.8%).

For founders in other verticals: if you can't articulate why your AI solution has a regulatory, switching cost, or price-insensitivity advantage, you're competing on feature parity. And feature parity is a race to the bottom.

Pattern 3: Infrastructure and Models Are Decoupling from Vertical Applications

Notice that Anthropic, Cohere, and Hugging Face (foundation models and infrastructure) are trading at dramatically different multiples than vertical AI applications, even when controlling for growth rate.

Anthropicholds a $60B valuation with zero revenue. Hugging Face is at 45x with $12M ARR. These are not comparable using traditional SaaS multiples. Investors are pricing foundation models on three factors: (1) compute efficiency and inference cost, (2) enterprise adoption of the API, and (3) defensibility against open-source alternatives.

Vertical AI companies, by contrast, are priced on traditional SaaS metrics: ARR, growth rate, churn, and net retention.

This separation is crucial for founders deciding whether to build a vertical application or a horizontal infrastructure layer. If you're building vertical AI, you're competing on unit economics and customer stickiness. If you're building infrastructure, you're competing on model performance, cost, and adoption velocity. The financial models are completely different.

How to Use This Comp Sheet in Your Fundraise

If you're a founder raising a Series A or Series B in vertical AI, here's how to use this data:

Step 1: Find Your True Comparable

Don't just pick the company in your vertical with the highest multiple and anchor to that. Instead, find the company that matches your metrics: growth rate (within 20 percentage points), churn (within 1 percentage point), and customer profile (enterprise vs. mid-market vs. SMB).

If you're a legal AI company growing 150% YoY with 2.5% churn and selling to law firms, Harvey AI (35.5x) is your comp. If you're a legal AI company growing 60% YoY with 5% churn and selling to in-house counsel, you're closer to Relativity's module (20.5x).

Investors will do this analysis themselves. Getting ahead of it and anchoring to the right comp gives you control of the narrative.

Step 2: Understand the Multiple Drivers

For each comp, ask: why is this multiple 25x instead of 15x? Is it because:

• Growth rate is 150%+ YoY (Imbue, Poolside, Hugging Face)?
• Churn is sub-2% (Glean, Synthesia), suggesting strong product-market fit?
• The vertical has structural defensibility (legal, healthcare, finance)?
• The company has achieved net negative churn (expanding revenue from existing customers)?
• The team has prior exits or institutional backing (founder pedigree matters)?

If your company matches three of these, you can justify a 25-35x multiple. If you match one, you're at 12-18x.

For more detailed guidance on how to position your AI startup for higher valuations, see AI Startup Valuations: The Reality Check You Need for Fundraising Success.

Step 3: Pressure-Test Your Growth Assumptions

Investors will compare your growth projections to the comps. If you're claiming 200% YoY growth with $2M current ARR, that's credible based on Poolside and Imbue. If you're claiming 200% YoY growth with $30M current ARR, investors will be skeptical-that's $90M ARR in 12 months, and there's no vertical AI company at that scale growing that fast.

Use the comp sheet to set realistic growth targets. Then, in your pitch, explain why you'll beat those targets: superior product, larger TAM, faster sales cycles, or stronger unit economics.

For a detailed walkthrough on pitching your AI company to investors, review A Step-by-Step Guide for Entrepreneurs on How to Pitch Their AI Projects and Raise Private Money.

The February 2026 Market Context

To understand why these multiples exist, you need to know what happened in January and early February 2026.

The AI Funding Boom Continued-But Selectively

According to AI Market Highlights - Feb, 2026: Analysis, Trends & Opportunities, generative AI funding in Q1 2026 is tracking 15% ahead of Q4 2025 pace. But the money is concentrating in two buckets:

1. Vertical AI companies with clear unit economics (legal, healthcare, finance)
2. Infrastructure and model companies with enterprise traction

Generic "AI-powered" tools are struggling to raise at previous valuations. The market has become ruthlessly efficient at distinguishing between companies with real moats and companies with feature parity.

Agentic AI Is the New Frontier

Research from What to expect from Vertical AI in 2026 - Aforza highlights that the next wave of vertical AI is moving beyond copilots and chatbots to autonomous agents. These are AI systems that can execute multi-step workflows without human intervention: a legal agent that reviews contracts, flags risks, and suggests redlines; a manufacturing agent that monitors production, predicts equipment failure, and schedules maintenance.

Companies building these agents (Poolside, Hebbia, Scale AI) are commanding premium multiples because agents represent a fundamentally different level of defensibility. You can't commoditize an agent the way you can commoditize a chatbot.

For founders still building chatbots or copilots, this is a warning: your multiple is about to compress unless you have a clear path to agentic workflows.

Enterprise Adoption Is Accelerating, But Churn Is Rising

According to AI Trends and Outlook for 2026 - AlphaSense, enterprise adoption of AI tools is accelerating, but so is churn. Companies are experimenting with 3-5 different AI tools, then consolidating to 1-2 that deliver measurable ROI.

This is brutal for mid-market vertical AI companies. You might land 50 customers in 2025, but if your product doesn't deliver ROI faster than your competitors, you'll lose 30% of them by Q3 2026.

The companies with the lowest churn (Glean: 1.8%, Synthesia: 2.1%) are those that have integrated deepest into customer workflows and tied their success directly to measurable business outcomes (time saved, error reduction, revenue impact).

The Vertical AI Category Is Maturing Faster Than Expected

When we published our first vertical AI comp sheet in Q2 2024, the category felt nascent. By February 2026, it's mature. Companies like Relativity (20.5x) and Wiz (27.7x) are trading at multiples that reflect market leadership, installed bases, and predictable churn. Newer entrants have to justify 30-40x multiples by demonstrating hypergrowth and superior unit economics.

For investors: if a Series A vertical AI company is pitching you on a 40x multiple with $2M ARR and 100% YoY growth, ask hard questions about unit economics, customer concentration, and whether they can sustain that growth to $10M ARR.

For founders: if you're at $2M ARR and growing 100% YoY, congratulations-you're in the top 10% of vertical AI companies. But don't expect a 40x multiple unless you can articulate a structural moat (regulatory, switching cost, data advantage, or workflow integration) that justifies it.

Deep Dive: Unit Economics and the Churn-Multiple Relationship

Here's the insight that separates sophisticated investors from the crowd: the revenue multiple is almost entirely determined by churn and growth rate.

Look at the data:

• Glean: 20.5x, 110% growth, 1.8% churn
• Jasper: 26.7x, 45% growth, 6.2% churn
• Harvey: 35.5x, 165% growth, 2.8% churn

If growth is constant, lower churn justifies a higher multiple. If churn is constant, higher growth justifies a higher multiple. But the relationship is multiplicative, not additive.

A simplified model:

Implied Multiple = (Growth Rate / Churn Rate) × Base Multiple

Where Base Multiple is 8-12x for mature SaaS, and 15-20x for AI-specific risk adjustment.

Let's work through an example. Suppose you're a Series A legal AI company with:

• $1.5M ARR
• 120% YoY growth
• 3% MRR churn
• 8 customers, average contract value $15K/month

Using the model:

Implied Multiple = (120 / 3) × 15 = 600 basis points = 6x base multiple

So your Series A valuation should be around $9M post-money ($1.5M × 6x).

But wait-you're a legal AI company, and legal AI commands a premium. Adjust for vertical defensibility:

Adjusted Multiple = 6x × 1.5 (legal premium) = 9x

Adjusted Valuation = $13.5M post-money

Now compare to Harvey AI: $6.2M ARR, 165% growth, 2.8% churn.

Harvey Implied Multiple = (165 / 2.8) × 15 = 885 basis points = 8.85x base Harvey Adjusted Multiple = 8.85x × 1.5 (legal premium) = 13.3x Harvey Valuation = $82.4M post-money (approximately $220M / 2.67 series expansion factor)

The math checks out. Harvey's 35.5x multiple reflects the fact that they're growing faster and churning slower than your hypothetical Series A company.

For founders: this is how investors will price your round. Build a detailed unit economics model (CAC, LTV, payback period, churn, expansion revenue) and show how you'll improve each metric over the next 18 months. That's your valuation story.

For investors: use this framework to gut-check term sheets. If a Series A vertical AI company is raising at a 35x multiple with 80% growth and 4% churn, they're overpriced by 30-40%. Push back.

The Outliers: Why Some Companies Trade at Unexpected Multiples

Every comp sheet has outliers. Let's examine three.

Outlier 1: Anthropic at $60B with Zero Revenue

Anthropicis valued at $60B with no revenue. By traditional metrics, this is insane. But investors aren't pricing Anthropic on revenue multiples; they're pricing it on:

1. Model performance: Claude Opus is competitive with GPT-4 on most benchmarks.
2. Enterprise adoption: Anthropic's API is being integrated into enterprise workflows at scale.
3. Defensibility: Anthropic's constitutional AI approach is difficult to replicate; it's a moat.
4. Market size: If Anthropic captures even 10% of the enterprise LLM market, it's a $100B+ company.

This is a venture capital bet on a company that will be 10x larger in 5 years, not a SaaS valuation based on current ARR.

For founders: if you're building a foundation model, don't use SaaS multiples to price your round. Instead, use venture capital multiples (25-50x potential exit value / current valuation).

Outlier 2: Hugging Face at 45x with $12M ARR

Hugging Face is a model hub and open-source community platform. It's trading at 45x ($540M / $12M), which is higher than almost every vertical AI company except Imbue and Poolside.

Why? Because Hugging Face has:

1. Massive network effects: 2M+ developers, 500K+ models, 50K+ datasets. Switching cost is enormous.
2. Data moat: Every model trained on Hugging Face improves the platform's utility.
3. Open-source defensibility: Hugging Face is the de facto standard for open-source AI. Replicating that would take years.
4. Hypergrowth: 180% YoY growth with only $12M ARR means the company is still in early adoption.

Once Hugging Face hits $50M ARR, the multiple might compress to 20-25x (because the law of large numbers kicks in), but at current scale, 45x is justified.

For founders: if you're building a platform business (not a vertical application), network effects and data moats matter more than unit economics. Price accordingly.

Outlier 3: Typeform at 16.2x with $68M ARR

Typeform is a mature form-building platform that's been around since 2012. It's trading at 16.2x ($1.1B / $68M), which is lower than almost every other company on the sheet.

Why? Because Typeform is:

1. Mature and slowing: 22% YoY growth is respectable, but it's a fraction of the growth rates of younger vertical AI companies.
2. Churning: 7.1% MRR churn is the highest on the sheet. Customers are leaving for Notion, Microsoft Forms, or custom solutions.
3. Commoditized: Form-building is a solved problem. Typeform doesn't have a moat; it has brand and user experience.
4. Crowded market: Typeform competes with 50+ other form builders. Price competition is intense.

Typeform is a good business, but it's not a venture-scale growth story anymore. The multiple reflects that.

For founders: if your vertical AI company is approaching Typeform's growth rate (20-25% YoY), start thinking about profitability and cash generation, not venture scale. Your investor base will shift from growth VCs to private equity or strategic acquirers.

What Happens Next: February to August 2026

Based on the trends we're seeing, here's what we expect from the vertical AI comp sheet over the next six months.

Multiples Will Compress for Slow-Growth Companies

If Jasper, Descript, and Typeform don't re-accelerate growth, their multiples will compress by 20-30% by August. The market has moved on from "AI is cool" to "AI has to deliver ROI." Companies with decelerating growth are getting marked down.

Agentic AI Companies Will Command a Premium

Companies building autonomous agents (Poolside, Hebbia, Scale AI) will see their multiples expand. As agentic AI moves from research to production, these companies will be seen as the next generation of vertical AI.

Legal AI Will Remain Resilient

Legal AI will continue to command 30-40x multiples because the structural advantages (regulatory moat, switching cost, price insensitivity) are real and durable. If you're a founder in legal AI, now is the time to raise at premium multiples.

Enterprise AI Will Bifurcate

Enterprise AI companies will split into two categories: (1) those that have integrated into core workflows and achieved net negative churn (Wiz, Glean), and (2) those that are still point solutions (Jasper, Descript). The gap between them will widen.

Foundation Models Will Consolidate

We'll see 2-3 foundation models (Anthropic, OpenAI, potentially Google or Meta) dominate the market. Smaller model companies (Cohere, Hugging Face) will find niches or be acquired. This is a winner-take-most market.

For a detailed look at how major VCs are positioning themselves in this landscape, check out Andreessen Horowitz's $20B AI Fund: The 2025 Game Changer for U.S. Tech Startups.

How to Build a Vertical AI Company That Commands Premium Multiples

If you're a founder building vertical AI and you want to justify a 30x+ multiple, here's the playbook:

1. Pick a Vertical with Structural Defensibility

Not all verticals are equal. Legal, healthcare, and finance have regulatory moats. Manufacturing and supply chain have data moats. Consumer has network effects.

Pick a vertical where:

• Regulation requires audit trails and compliance: Legal, healthcare, finance
• Data is proprietary and difficult to replicate: Manufacturing, supply chain, fraud detection
• Switching costs are high: Enterprise software, infrastructure
• Price sensitivity is low: Any vertical where AI saves more than 5 hours per transaction

Avoid verticals where:

• Switching costs are low: Consumer productivity, content generation
• Price sensitivity is high: SMB, consumer
• Data is commoditized: General-purpose writing, customer service

2. Build for Workflows, Not Features

Don't build a chatbot. Build a system that executes multi-step workflows. Examples:

• Legal AI: Draft → Review → Negotiate → Redline → Execution
• Healthcare AI: Intake → Diagnosis → Testing → Treatment → Follow-up
• Manufacturing AI: Forecast → Schedule → Monitor → Predict → Optimize

Each step should be automated, with human oversight at critical junctures. This is how you achieve defensibility and low churn.

3. Optimize for Unit Economics from Day One

Don't chase revenue. Chase unit economics. Your goal:

• CAC payback period: <12 months
• LTV/CAC ratio: >3x
• Net retention: 110%+
• Churn: <3% MRR

If you hit these metrics at $1M ARR, you can justify a 25x multiple. If you hit them at $5M ARR, you can justify a 30x multiple. If you hit them at $10M ARR, you can justify a 20x multiple (law of large numbers).

For detailed guidance on capital raising playbooks, see 11 Capital Raising Playbooks for Startup Founders.

4. Build a Data Moat

The best vertical AI companies have data advantages that compound over time. Examples:

• Relativity: 20+ years of legal discovery data
• Wiz: Cloud security data from millions of endpoints
• Scale AI: Training data from thousands of enterprise workflows

Your data moat should make it 2-3x harder for a competitor to replicate your product. This justifies premium multiples.

5. Sell to the Right Customers

Not all customers are equal. Enterprise customers with high switching costs and long contract terms are worth 3-5x more than SMB customers.

Your go-to-market should target:

• Enterprise: 18-24 month sales cycles, $100K-$1M ACV, 2-3% MRR churn
• Mid-market: 6-12 month sales cycles, $20K-$100K ACV, 4-5% MRR churn
• SMB: 1-3 month sales cycles, $1K-$20K ACV, 8-10% MRR churn

If you're selling to SMB, your multiple will be 50% lower than if you're selling to enterprise, even with the same growth rate.

For more on pitch deck strategy, review 6 Pitch Deck Red Flags: What to Avoid in Your Quest for Venture Capital.

The Investor Perspective: How to Use This Comp Sheet in Due Diligence

If you're an investor looking at a vertical AI Series A or Series B, here's how to use this comp sheet in your diligence process.

Step 1: Find the Right Comp

Don't just pick the company in the same vertical. Pick the company with the most similar metrics:

• Growth rate (within 20 percentage points)
• Churn (within 1 percentage point)
• Customer profile (enterprise vs. mid-market vs. SMB)
• Sales model (self-serve vs. sales-led vs. high-touch)

If the founder is anchoring to Harvey AI (35.5x) but their company is growing 80% YoY with 5% churn, Harvey is the wrong comp. Relativity (20.5x) is more accurate.

Step 2: Pressure-Test the Unit Economics

Request detailed unit economics: CAC, LTV, payback period, net retention, churn, and expansion revenue. Model out the next 3 years and see if the company can hit the multiples implied by the comp sheet.

If a Series A company is raising at 30x with a 18-month CAC payback period, that's a red flag. They're overpriced relative to their unit economics.

Step 3: Assess the Moat

For each company, ask: what's the defensibility? Is it:

• Regulatory (legal, healthcare): Can a competitor replicate it in 12 months?
• Data (manufacturing, supply chain): Do you have 12+ months of proprietary data advantage?
• Workflow (enterprise software): Is the product integrated into 5+ customer workflows?
• Network (platform): Are there network effects that compound over time?

If the moat is weak, the multiple should be 30-40% lower than the comp sheet suggests.

Step 4: Benchmark Against Macro Trends

Use research from 2026 vertical AI predictions: Our industry experts on the future of AI and AI Trends for the Second Week of February 2026 to understand whether the vertical is heating up or cooling down.

If the vertical is heating up (legal, healthcare, manufacturing), multiples should expand. If it's cooling down (general content generation, customer service chatbots), multiples should compress.

Step 5: Look for Red Flags

Red flags that suggest a company is overvalued:

• Founder is anchoring to a company in a different vertical (e.g., legal AI founder anchoring to Jasper, a content AI company)
• Growth is decelerating but multiple is expanding (suggests the founder is inflating projections)
• Customer concentration is high (top 5 customers represent >40% of ARR)
• Churn is increasing (suggests product-market fit is weakening)
• CAC payback period is >18 months (suggests the business model is broken)

If you see three or more of these, pass or negotiate aggressively on valuation.

Final Thoughts: The Comp Sheet as a Living Document

This comp sheet is a snapshot of February 2026. It will be outdated by August 2026. New companies will emerge, old companies will be acquired or shut down, and multiples will shift based on macro conditions and category maturity.

But the principles are durable:

1. Growth and churn determine multiples more than any other factor.
2. Verticals with structural defensibility command premium multiples.
3. Agentic AI is the next frontier, and companies building agents will trade at higher multiples than companies building copilots.
4. Enterprise customers are worth 3-5x more than SMB customers.
5. Unit economics matter more than revenue, because unit economics predict sustainability.

For founders raising capital, use this comp sheet to anchor your fundraise to comparable companies with similar metrics. For investors evaluating opportunities, use it to gut-check term sheets and identify overvalued companies.

For more on how to structure your capital raising strategy, see 5 Steps to Create an Outstanding Capital Raising Plan [Free Templates].

And if you're looking to reach investors without warm intros, review Raise Capital Without Warm Intros: The AI-Personalized Cold Outreach Blueprint (Templates, Cadence, Compliance) That Actually Gets Replies.

The vertical AI market is maturing fast. The companies that will win are those with genuine moats, strong unit economics, and the discipline to optimize for the metrics that matter. Use this comp sheet to make sure you're one of them.