12 Best AI Lead Scoring Tools Compared (2026)

Only 27% of leads that marketing sends to sales are actually qualified. The rest? They burn rep time, inflate pipeline forecasts, and drag down conversion rates. Meanwhile, 61% of B2B marketers admit they send every lead to sales without any scoring at all.

That gap between "leads generated" and "leads worth pursuing" is exactly what AI lead scoring tools are built to close. The lead scoring software market hit $2.23 billion in 2025 and is growing at 11.4% CAGR, driven by a simple reality: organizations using lead scoring achieve 138% ROI on lead generation compared to 78% without it. Machine learning models specifically deliver 75% higher conversion rates than rule-based scoring.

But the market is crowded and confusing. Half the tools on older comparison lists have been acquired, rebranded, or discontinued entirely. This guide covers 12 platforms that are actively maintained, independently verified, and worth your evaluation time in 2026 -- with real pricing, honest limitations, and practical guidance on which tool fits which use case.

What Is AI Lead Scoring and Why Does It Matter?

Traditional lead scoring assigns points based on static rules: +10 for downloading a whitepaper, +5 for visiting the pricing page, -20 for being a student. These models are brittle. They require constant manual tuning, they cannot adapt to changing buyer behavior, and they tend to reflect the biases of whoever built them.

AI lead scoring replaces those static rules with machine learning models trained on your actual conversion data. Instead of a marketing ops person deciding that "VP titles are worth 15 points," the model analyzes thousands of historical wins and losses to surface which combinations of attributes, behaviors, and timing actually predict closed deals.

The difference is measurable. According to a Brixon Group analysis of Forrester research, companies using AI-driven lead scoring see 10-15% increases in sales productivity and 10-20% improvements in conversion rates. Salesforce's 2025 State of Sales report found that 83% of sales teams using AI reported revenue growth, with the highest-performing teams spending 34% less time on research.

What Separates Good Lead Scoring from Bad?

Not all AI lead scoring is created equal. The best platforms share these characteristics:

• Model transparency -- You can see why a lead scored high or low, not just that it did. Black-box models erode trust with sales teams.
• Multi-signal ingestion -- The model incorporates firmographic data, behavioral signals (web visits, email engagement, content consumption), technographic data, and intent signals from third-party sources.
• Continuous learning -- The model retrains as your data changes, rather than degrading silently over months.
• CRM integration depth -- Scores need to surface inside the tools reps actually use (Salesforce, HubSpot, Outreach), not in a separate dashboard nobody checks.
• Lead-to-account matching -- In B2B, individual leads matter less than buying committee activity at the account level. The best platforms score both.

12 AI Lead Scoring Platforms Compared (2026)

A note on methodology: We verified pricing through vendor sites, Vendr, Capterra, and published third-party analyses. We excluded several tools that appeared on older lists because they have been acquired and folded into parent products (Mintigo by Anaplan in 2019, EverString by ZoomInfo in 2020, Lattice Engines by Dun & Bradstreet in 2019, Radius Intelligence by Kabbage/sold off). We verified each platform below is actively maintained and accepting new customers as of February 2026.

1. Salesforce Einstein Lead Scoring

Best for: Teams already on Salesforce Enterprise+ who want native, zero-integration scoring.

Einstein Lead Scoring is built directly into Salesforce CRM. It analyzes your historical lead data to build a predictive model, then scores every new lead on a 1-100 scale. The Spring 2026 release expanded Opportunity Scoring to all Sales Cloud users at no additional cost, though Lead Scoring still requires Enterprise Edition or higher.

Key features:

• Predictive scores surfaced directly on lead records in Salesforce
• Explanation cards showing top factors influencing each score
• Opportunity scoring and next-best-action recommendations
• Automated lead assignment based on predicted conversion likelihood

Pricing: Einstein Lead Scoring requires Sales Cloud Enterprise ($165/user/month) or higher. The AI add-on (Einstein for Sales) starts at $50/user/month, though Revenue Intelligence is $220/user/month. A 10-person sales team typically spends $40,000+ annually on Einstein capabilities. Implementation ranges from $50K-$500K+ depending on complexity.

Watch out for: Einstein needs substantial historical data (minimum ~1,000 converted leads) to build an accurate model. Organizations with small deal volumes or new Salesforce instances may see poor initial model quality. Total cost of ownership -- including implementation, admin, and consulting -- often exceeds the license cost.

2. HubSpot Lead Scoring

Best for: SMBs and mid-market teams wanting an all-in-one platform with scoring built in.

HubSpot offers both manual (rule-based) scoring on Professional plans and AI predictive scoring on Enterprise. In August 2025, HubSpot overhauled its scoring infrastructure, replacing legacy scoring properties with a more powerful Lead Scoring tool featuring advanced logic, multi-model support, and explainability features showing which signals contributed most to each score.

Key features:

• Rule-based scoring with AND/OR logic (Professional+)
• AI predictive scoring with signal explainability (Enterprise)
• Multiple scoring models for different regions, product lines, or personas
• Workflow triggers based on score thresholds
• Breeze Intelligence enrichment (formerly Clearbit, acquired Dec 2024) for 200+ B2B attributes

Pricing: Manual lead scoring requires Marketing Hub or Sales Hub Professional ($890/month for 3 seats). Predictive scoring requires Enterprise ($3,600/month, 10-seat minimum, $3,500 onboarding). Breeze Intelligence is an add-on starting at $45/month for 100 credits.

Watch out for: The predictive scoring gap between Professional and Enterprise is significant. Many teams buy Enterprise primarily for this feature, which is a steep jump. Breeze Intelligence credits are consumed quickly if you're enriching at scale.

3. 6sense Revenue AI

Best for: Enterprise ABM teams that want intent-driven account scoring alongside lead scoring.

6sense is primarily an account-based marketing platform that scores accounts based on intent data, engagement signals, and buying stage predictions. Its AI ingests over 1 trillion signals to predict which accounts are in-market and at what stage of the buying journey.

Key features:

• Account-level scoring based on buying stage (Awareness through Decision)
• Intent data from proprietary and third-party sources
• Predictive analytics for pipeline and deal closure
• Multi-channel orchestration (ads, email, web personalization)
• Lead-to-account matching and routing

Pricing: 6sense does not publish pricing. Based on Vendr benchmarks and Warmly's analysis, annual contracts typically range from $60,000-$300,000 depending on company size and modules. The Business tier starts around $19,000/year for up to 10K visitors; Enterprise starts at $30,000/year.

Watch out for: 6sense is built for account-based motions. If your go-to-market is primarily inbound or lead-based (not account-based), you're paying for capabilities you won't use. The platform's complexity also requires a dedicated admin or RevOps resource to manage effectively.

4. Demandbase One

Best for: Enterprise ABM teams needing combined intent data, advertising, and scoring in one platform.

Demandbase competes directly with 6sense as a full-stack ABM platform with built-in lead and account scoring. Its "Engagement Minutes" scoring framework tracks and quantifies every interaction across the buying committee, giving account-level visibility into how engaged each target account is.

Key features:

• Engagement Minutes scoring at both person and account levels
• Pipeline Predict scores for deal-level forecasting
• Intent data from Demandbase's proprietary network plus Bombora
• Integrated advertising, personalization, and sales intelligence
• Customizable scoring criteria and weighting

Pricing: Demandbase pricing is custom and modular. Published estimates start at $18,000-$32,000/year for ~200-employee companies, with enterprise deployments running significantly higher. Add-ons for advertising credits, AI orchestration, and CRM connectors increase the total.

Watch out for: Like 6sense, Demandbase requires commitment to an ABM strategy to justify the investment. The Engagement Minutes framework, while powerful, demands ongoing calibration to ensure score accuracy as your campaigns evolve.

5. Apollo.io

Best for: Growth-stage companies wanting lead scoring bundled with a prospecting database and sequencing.

Apollo combines a B2B contact database (210M+ contacts) with AI lead scoring, email sequencing, and dialer -- making it the most affordable all-in-one option on this list. Its AI scoring predicts conversion likelihood based on engagement signals and profile data.

Key features:

• AI lead scoring based on intent signals and engagement data (Professional+)
• 210M+ contact and company database with real-time enrichment
• Email sequencing with A/B testing (Professional+)
• Score-based filters for prioritizing outbound lists
• Dialer with call recording and AI insights

Pricing: Free plan available (100 credits/month). Basic at $49/user/month (annual). Professional at $99/user/month (includes AI scoring). Organization at $149/user/month (3-user minimum). Credit-based model means actual spend depends on enrichment volume.

Watch out for: Apollo's scoring is less sophisticated than purpose-built platforms like MadKudu or 6sense. It works well for teams that need "good enough" scoring bundled with prospecting tools, but organizations with complex scoring needs or large CRM datasets may outgrow it. Credit governance is critical -- teams that enrich aggressively can blow through credits quickly.

6. MadKudu

Best for: Product-led growth (PLG) companies that need to score based on product usage data.

MadKudu built its reputation on helping PLG companies like Figma and Notion identify which free-tier users are most likely to convert to paid. Unlike most lead scoring tools that focus on marketing engagement signals, MadKudu excels at incorporating product usage patterns into its models.

Key features:

• Fit + engagement + intent scoring combining multiple data dimensions
• Product usage data analysis for PLG conversion prediction
• Real-time scoring as prospects interact with your product or website
• Transparent model logic -- you can see exactly how scores are calculated (unlike "black box" alternatives)
• Integrations with Salesforce, HubSpot, Marketo, and Segment

Pricing: Growth plan at $1,999/month for 2,000 leads. Pro plan at $3,499/month for 6,000 leads. Enterprise is custom. Larger organizations typically spend $35,000-$97,000/year, with Vendr data showing discounts up to 31% are negotiable.

Watch out for: MadKudu is expensive relative to its scope -- it does scoring and segmentation, but it does not include a contact database, sequencing, or CRM. If you're not a PLG company with meaningful product usage data, you'll get less value from its core differentiator.

7. ZoomInfo Copilot

Best for: Sales teams that need lead scoring tightly bundled with the largest B2B contact database.

ZoomInfo's Copilot, launched in 2024 and expanded through 2025, adds AI-driven lead scoring and prioritization on top of ZoomInfo's massive B2B data asset. It surfaces real-time account insights, recommends next-best contacts, and scores leads based on intent signals from ZoomInfo's proprietary data.

Key features:

• AI-powered lead and account scoring with intent data from Clickagy (acquired 2020) and Scoops
• Real-time account insights and conversational queries
• Automated outreach recommendations based on scoring
• 275M+ professional profiles and 100M+ company records
• Copilot AI recommendations for prospect prioritization

Pricing: ZoomInfo Professional starts at $14,995/year. Copilot features require the Advanced ($24,000+/year) or Elite ($39,995/year) plans. Credit consumption varies by usage. Total contracts for mid-market teams typically land between $25,000-$60,000/year.

Watch out for: Copilot's scoring is newer and less mature than purpose-built scoring tools. You're primarily buying ZoomInfo for its data, with scoring as a value-add. The credit-based pricing model can be unpredictable if not managed carefully. Contract lock-in and auto-renewals are a common complaint.

8. LeanData

Best for: RevOps teams that need sophisticated lead-to-account matching and routing alongside scoring.

LeanData is not primarily a scoring tool -- it's a revenue orchestration platform focused on lead routing, matching, and workflow automation. But its lead-to-account matching and prioritization capabilities make it a critical component of any enterprise lead scoring stack, especially for teams running ABM motions.

Key features:

• AI-powered lead-to-account matching even with incomplete or messy data
• Visual drag-and-drop routing workflows (FlowBuilder)
• Buying group identification and routing
• Round-robin assignment with weighting, territory, and capacity rules
• Native Salesforce integration with real-time processing

Pricing: LeanData Standard starts at $468/user/year. Advanced is $588/user/year. A 100-user deployment typically negotiates to $24,000-$29,000/year. Custom enterprise plans for complex Salesforce architectures can be significantly higher.

Watch out for: LeanData complements a scoring tool but does not replace one. Think of it as the routing layer that ensures scored leads get to the right rep fast -- not the scoring engine itself. Implementation can be complex for organizations with non-standard Salesforce configurations.

9. Warmly

Best for: Teams focused on de-anonymizing website visitors and scoring them in real time.

Warmly takes a different approach to lead scoring by starting with website visitor identification. It de-anonymizes visitors using a combination of first-party and third-party data, then enriches and scores them based on ICP fit, intent signals, and engagement patterns.

Key features:

• Person-level website visitor de-anonymization
• AI-powered ICP and intent scoring
• Real-time Slack alerts for high-fit visitors
• Automated outreach workflows triggered by visitor scores
• Buying committee identification from account activity

Pricing: Free plan identifies up to 500 visitors/month. AI Data Agent starts at $10,000/year. AI Inbound Agent at $16,000/year. AI Outbound Agent at $22,000/year. Marketing Ops Agent (includes AI-powered account scoring) at $25,000/year.

Watch out for: Warmly's scoring is inherently tied to website traffic. If your prospects don't visit your site before engaging (common in cold outbound-heavy motions), you'll have fewer signals to score on. De-anonymization accuracy varies by traffic volume and geography.

10. Keyplay

Best for: RevOps and marketing ops teams that want to build custom scoring models without writing code.

Keyplay focuses specifically on account scoring and ICP definition. Its AI Lookalike feature analyzes your best customers and identifies similar accounts in the market, while its no-code scoring builder lets you layer in custom signals (technographic data, hiring signals, financial triggers) without engineering resources.

Key features:

• AI Lookalike matching trained on your best customers
• No-code custom scoring builder with 100+ signal types
• Hiring signal detection (job postings as intent indicators)
• Technographic and firmographic enrichment
• Native integrations with Salesforce, HubSpot, and Outreach

Pricing: Free forever plan available with limited features. Growth plan at $12,000/year. Scale plan with custom pricing for larger deployments.

Watch out for: Keyplay focuses on account-level scoring, not individual lead scoring. If you need person-level predictions (e.g., "which contact at this account is most likely to respond?"), you'll need to pair Keyplay with another tool. The free plan is genuinely useful for testing the approach before committing.

11. Clari + Salesloft (Merged)

Best for: Revenue teams wanting pipeline scoring, deal health prediction, and sales engagement in one merged platform.

Clari and Salesloft completed their merger in late 2025, combining Clari's revenue intelligence (pipeline scoring, deal health, forecast accuracy) with Salesloft's sales engagement (cadences, dialer, conversation intelligence). Clari was named a Leader in Gartner's inaugural Magic Quadrant for Revenue Action Orchestration (December 2025).

Key features:

• AI pipeline scoring predicting deal health and close probability
• Revenue leak detection and forecast accuracy analytics
• Buyer signal capture from email, call, and meeting data (1 trillion+ signals ingested)
• Cadence management with AI-prioritized next steps
• Conversation intelligence and coaching (via Salesloft)

Pricing: Post-merger pricing is still being consolidated. Historically, Salesloft starts around $125-$165/user/month and Clari's revenue intelligence starts at custom enterprise pricing. Expect bundled pricing to emerge through 2026.

Watch out for: The merger is still in early integration. Product roadmap consolidation is ongoing, and some features may be in flux through 2026. If you need stable, mature lead scoring today, consider purpose-built alternatives and revisit Clari+Salesloft in H2 2026.

12. Breeze Intelligence (HubSpot/Clearbit)

Best for: HubSpot users who want enrichment-powered scoring without leaving the HubSpot ecosystem.

After acquiring Clearbit in December 2024, HubSpot rebranded the technology as Breeze Intelligence. It enriches contact and company records with 200+ B2B attributes, enabling more accurate lead scoring by filling in the firmographic and technographic gaps in your CRM data.

Key features:

• Instant enrichment from 200+ data sources (250M+ buyer and company profiles)
• Dynamic form shortening (pre-fill known fields to reduce friction)
• Buyer intent tracking for website visitors
• Real-time lead scoring powered by enriched data
• Native HubSpot integration -- no API configuration needed

Pricing: Breeze Intelligence is an add-on to any HubSpot plan, starting at $45/month for 100 credits ($50/month if billed monthly). Credit consumption depends on usage: every enrichment, form submission, or intent push consumes credits. Standalone enrichment packages previously started around $99/month under the Clearbit brand.

Watch out for: Breeze Intelligence is an enrichment layer that makes scoring more accurate, not a standalone scoring engine. You still need HubSpot's native scoring (Professional) or predictive scoring (Enterprise) to act on the enriched data. Credit burndown can be surprisingly fast for high-traffic sites.

How Do These Tools Compare on Pricing?

Here is a quick-reference guide by budget tier:

Under $15,000/year:

• Apollo.io Professional -- $99/user/month ($1,188/user/year). Best value for teams that also need a contact database and sequencing.
• Keyplay Growth -- $12,000/year. Purpose-built account scoring with no-code model builder.
• Warmly AI Data Agent -- $10,000/year. Website visitor identification plus ICP scoring.
• HubSpot Professional + Breeze -- ~$11,220/year (3 seats + Breeze credits). Rule-based scoring with enrichment.

$15,000-$50,000/year:

• MadKudu Growth -- $24,000/year. Best for PLG companies with product usage data.
• LeanData Standard -- ~$24,000/year (100 users). Lead routing and matching, pairs with any scoring tool.
• ZoomInfo Advanced -- $24,000+/year. Scoring bundled with the largest B2B database.
• HubSpot Enterprise -- $43,200/year (10 seats). Full predictive scoring with Breeze Intelligence.

$50,000+/year:

• Salesforce Einstein -- $40,000+/year (10 users). Native CRM scoring for Salesforce shops.
• 6sense -- $60,000-$300,000/year. Enterprise ABM with intent-driven account scoring.
• Demandbase One -- $18,000-$100,000+/year. Full-stack ABM with advertising, intent, and scoring.
• Clari + Salesloft -- Custom enterprise pricing. Pipeline and deal scoring + engagement.

Which Tool Should You Choose? A Decision Framework

Selecting a lead scoring tool is less about finding the "best" platform and more about finding the right fit for your specific go-to-market motion, data maturity, and budget. Use these questions to narrow your options:

What CRM are you on?

If you run Salesforce, Einstein Lead Scoring is the lowest-friction starting point. If you run HubSpot, their native scoring (especially with Breeze Intelligence enrichment) is the natural first choice. Fighting your CRM's ecosystem usually creates more problems than it solves.

Are you running account-based or lead-based motions?

For ABM strategies, 6sense and Demandbase are purpose-built. They score accounts and buying committees, not just individual leads. For lead-based inbound motions, HubSpot, Apollo, or MadKudu are better fits.

Is product-led growth a significant part of your model?

MadKudu is the standout for PLG companies because it uniquely incorporates product usage data into scoring models. If free-tier-to-paid conversion is a key metric, start here.

What's your data maturity?

AI models need training data. If you have fewer than 1,000 historical converted leads, predictive scoring will struggle to produce accurate results regardless of the tool. In that case, start with rule-based scoring (HubSpot Professional, Keyplay) and upgrade to AI scoring as your dataset grows.

Do you need scoring, routing, or both?

LeanData handles the "what happens after scoring" problem -- ensuring scored leads reach the right rep instantly. Most teams need both a scoring engine and a routing engine. LeanData pairs well with any scoring tool on this list.

How to Make Lead Scoring Actually Work

Buying the tool is the easy part. Making it produce accurate, actionable scores that sales teams trust requires operational discipline. Here are the practices that separate successful implementations from expensive shelfware:

Start with your closed-won data, not assumptions

Before configuring any scoring model, export your last 12-24 months of closed-won and closed-lost deals. Look for patterns in company size, industry, title, engagement behavior, and buying signals. Let the data define your ICP, not the other way around. According to Gartner's 2025 sales technology report, 89% of revenue organizations now use AI-powered tools -- but the ones seeing real results are those with clean historical data feeding their models.

Define a shared SLA between marketing and sales

Scoring is meaningless without agreement on what happens at each score threshold. Define clear handoff points: What score triggers an MQL? When does sales accept or reject? What's the expected follow-up time? Without this, sales will ignore the scores (they usually do anyway -- 70% of leads are lost to inadequate follow-up).

Build in negative scoring and decay

Leads go cold. A prospect who downloaded three whitepapers six months ago and hasn't engaged since is not the same as one who visited your pricing page yesterday. Implement time-based score decay and negative scoring for disqualifying signals (competitor domains, student emails, unsubscribes).

Review and recalibrate quarterly

Even AI models degrade. Markets shift, buyer behavior changes, and your product evolves. Set a quarterly cadence to review model accuracy: What percentage of high-scored leads actually converted? Are reps finding the scores useful? Adjust weights and retrain models based on actual outcomes.

Layer in intent and signal data

The most accurate scoring models combine first-party engagement data (your website, emails, product) with third-party intent data and buying signals. A lead who visited your pricing page and is researching your category on G2 and their company just raised a funding round is a fundamentally different prospect than someone who only visited your blog. Tools like Autobound can surface 350+ buying signals (funding events, leadership changes, hiring surges, competitive mentions) that add context standard scoring models miss.

What About the Graveyard? Tools That Didn't Make the List

If you're comparing this guide to older lists, you'll notice several names missing. Here's why:

• Mintigo -- Acquired by Anaplan in 2019. Technology absorbed; no standalone product.
• EverString -- Acquired by ZoomInfo in 2020. Data assets folded into ZoomInfo's platform.
• Lattice Engines -- Acquired by Dun & Bradstreet in 2019. Now part of D&B's sales intelligence suite.
• Radius Intelligence -- Assets sold to Kabbage (subsequently acquired by American Express). No standalone product.
• Infer -- Originally acquired by Ignite Technologies (2017), then acquired by Frisbii in 2025. Focused on recurring revenue management, not standalone lead scoring.
• LeadZen -- India-based startup with limited North American market presence and sparse independent reviews. Worth watching but not mature enough for a general recommendation.
• CaliberMind -- Small niche CDP. Still operating but has not achieved the scale or independent validation of the platforms on our list.

This consolidation is the market working as expected. Standalone predictive lead scoring tools either got absorbed into larger platforms (Salesforce, ZoomInfo, Demandbase) or gave way to AI-native platforms that combine scoring with adjacent capabilities. The trend favors platforms over point solutions.

Key Takeaways

Lead scoring is one of those capabilities that sounds simple but determines whether your sales team spends time on qualified opportunities or chases ghosts. Here's what to remember:

• AI scoring dramatically outperforms rule-based models -- 75% higher conversion rates and 138% better ROI according to industry research. If you're still running static point systems, you're leaving money on the table.
• Your CRM should drive the decision. Salesforce shops start with Einstein. HubSpot shops start with HubSpot + Breeze. Fight against your ecosystem at your own peril.
• Data quality > tool sophistication. No AI model can compensate for a CRM full of stale, duplicate, or incomplete records. Invest in data enrichment before (or alongside) scoring.
• Scoring alone is not enough. The best teams combine predictive scores with real-time buying signals -- funding events, leadership changes, competitive mentions, hiring surges -- to prioritize outreach with context, not just a number. Explore signal-based selling as the next evolution beyond static scoring.
• Start with what you have, then layer up. If budget is tight, Apollo's Professional tier or Keyplay's free plan can get you meaningful results. Enterprise platforms like 6sense and Demandbase make sense once your deal volumes and ABM maturity justify the investment.