The best AI lead scoring tool depends on what you are scoring. If you are scoring inbound leads by what they did on your site, HubSpot or Salesforce Einstein fit best. If you are scoring prospects before you contact them, use Clay or Apollo to score from firmographic data, or Sliq, which checks each prospect against your criteria and then shows which kinds of prospects actually reply.
What is AI lead scoring?
AI lead scoring uses a model to rate how likely a lead is to buy, instead of a points table someone set by hand. There are two kinds:
- Predictive scoring learns from your history. HubSpot and Salesforce Einstein look at which past leads converted and score new ones by how similar they are.
- Prompt-based scoring judges each lead against criteria you write, such as "Series A to C B2B software company, head of sales or above." Clay's AI columns and scripts built on TypeSafe's Jev model work this way.
Predictive scoring needs a lot of past conversions to learn from. Prompt-based scoring works from day one, but it is only as good as the criteria you give it.
How is outbound lead scoring different from inbound?
Most lead scoring tools were built for inbound. They score engagement: page views, form fills, email opens. That works when leads come to you.
Outbound is different. The people you are about to contact have not engaged with you yet, so there is nothing to score except who they are: role, company, size, industry, and recent activity. For outbound, the useful questions are "does this person fit?" before you reach out, and "which kinds of people actually reply?" after.
What are the best AI lead scoring tools?
| Tool | Best for | What it scores from | Price with scoring |
|---|---|---|---|
| Sliq | Outbound on LinkedIn and email | Each prospect's profile, checked against your criteria; reply and meeting rates per group | Credits per person tagged |
| HubSpot | Inbound leads already in HubSpot | Contact properties and engagement | Pro from $800/mo (Marketing); AI scores need Enterprise at $3,600/mo |
| Salesforce Einstein | Large Salesforce orgs | Your past lead conversions | Add-on; price not published |
| Apollo | Scoring contacts from Apollo's database | Apollo data plus your Apollo activity | Paid plans; see Apollo's pricing page |
| Clay | Teams who want to build their own scoring | Enriched data, run through AI prompts you write | From $167/mo, credit-metered |
| Warmly | Scoring website visitors | Identified visitors, ICP fit, intent | $10,000/yr |
| Jev (build it yourself) | Developers scoring huge volumes cheaply | Any text you send it | $0.042 per million input tokens |
1. Sliq
Sliq is best for founders and account executives running outbound on LinkedIn and email who want to know which prospects to prioritize.
Sliq runs LinkedIn and email outbound from Claude, ChatGPT, or any other AI agent. Sliq finds prospects, researches them, and drafts every message, then adds each one to an approval queue for you to review.
It handles lead quality at two points:
- When it finds prospects. Sliq checks each person against the criteria you gave it and records a verdict with the reason. People who don't fit are filtered out before any message is written, and you can review them if you disagree.
- After you've reached out. In Insights, you ask a question like "Do directors reply more than VPs?" or "Which industries book meetings?" and list the tags to sort by. Sliq tags every prospect into those groups using TypeSafe's Jev model, then shows the reply and meeting rate for each group. New prospects added to the campaign are tagged automatically.
The second step is what most scoring tools skip. A fit score is a guess about who will reply. Reply rates by group tell you who actually did, so the next list you build is aimed better. For the full method, see how to find which prospects reply.
Sliq is best if you want:
- Prospects checked against your criteria before anyone gets a message
- To see which roles, industries, or company sizes reply and book meetings
- Scoring that feeds straight into LinkedIn and email outreach
- An approval queue before anything is sent
Sliq may not be best if:
- You need to score inbound leads by website visits and form fills
- You need a single numeric score written back to your CRM
2. HubSpot
HubSpot's lead scoring tool is best for teams whose leads already live in HubSpot and come in through marketing.
It builds fit scores from contact properties like job title, company size, and revenue, and engagement scores from site visits, email opens, and CTA clicks, or both combined. The scoring tool is included in Marketing Hub and Sales Hub Professional and Enterprise. Marketing Hub Professional starts at $800 a month and Sales Hub Professional at $90 per seat a month (billed annually).
AI-built scores need Marketing Hub Enterprise, at $3,600 a month, and at least 50 sample contacts: 25 that converted and 25 that did not. Professional plans allow up to 5 scores; Enterprise allows 50.
HubSpot is best if you want:
- Scoring inside the CRM you already use
- Engagement scoring for inbound leads
HubSpot may not be best if:
- Your leads haven't engaged with you yet (outbound)
- You don't want to pay for Enterprise to get AI scoring
3. Salesforce Einstein Lead Scoring
Einstein Lead Scoring is best for large Salesforce orgs with plenty of conversion history.
It trains a predictive model on your own leads and which ones converted. It needs at least 1,000 leads created in the last 200 days, with at least 120 converted. On most editions it's an add-on; Salesforce's pricing page lists lead scoring as "available for purchase" from Starter ($25 per user a month) up, without publishing the add-on price.
Salesforce Einstein is best if you want:
- A model trained on your own conversions
- Scoring native to Salesforce
Salesforce Einstein may not be best if:
- You don't have 1,000 recent leads and 120 conversions
- You're a small team or not on Salesforce
4. Apollo
Apollo's lead scoring is best for teams that already prospect from Apollo's database.
Apollo scores contacts and accounts from your company website, your account and prospecting activity, and Apollo's own demographic, firmographic, and behavioral data. You can use AI-generated scores or build custom ones. Scoring is on paid plans; check Apollo's pricing page for current tiers, since the number of custom scores depends on the plan.
Apollo is best if you want:
- Scoring and a contact database in one place
- Scores applied as you build lists
Apollo may not be best if:
- Your prospects aren't in Apollo's database
- You want to see what happened after outreach, not just a score before it
5. Clay
Clay is best for teams who want to build their own scoring logic.
Clay doesn't have a built-in scoring model. You enrich a list, then add AI columns (Claygent) that run a prompt you write, such as "Rate this company's fit from 1 to 10 given these criteria." That makes it flexible: you can score from any data Clay can pull. Plans start at $167 a month (Launch) with 15,000 actions and 3,000 data credits, and CRM sync needs Growth at $446 a month.
Clay is best if you want:
- Full control over how leads are scored
- Scoring from enrichment and web research data
Clay may not be best if:
- You don't want to design and maintain the scoring yourself
- You want scoring connected to outreach without a separate sending tool
6. Warmly
Warmly is best for teams scoring the companies and people visiting their website.
It identifies website visitors, filters them by ICP fit, and adds intent signals. Lead scoring is part of its AI Web-Deanonymization plan at $10,000 a year. Warmly's pricing page lists lead scoring as a feature but doesn't explain how scores are calculated.
Warmly is best if you want:
- To score and act on website visitors
- Intent signals alongside fit
Warmly may not be best if:
- Your pipeline comes from outbound, not website traffic
- $10,000 a year is too much for your stage
7. Build it yourself with Jev
Jev is a model from TypeSafe AI that returns a decision, a score, or a choice with a confidence number, instead of writing text. It opened for early access on September 15, 2026.
It is cheap enough to score everything. Jev costs $0.042 per million input tokens, and output is free. A lead's profile runs around 1,000 tokens, so scoring 1,000 leads costs about 4 cents. For comparison, running the same job through a general-purpose chat model costs several times more and takes longer.
The catch is that Jev is an API, not an app. You write the script that sends each lead, set the criteria, and decide what to do with the score. It also can't explain its answer in words; it returns a label and a confidence. For more on where it fits, see Jev use cases in sales and go-to-market.
Jev is best if you want:
- To score very large lists for almost nothing
- Full control and you're comfortable writing code
Jev may not be best if:
- You want a tool that works without code (Sliq runs Jev for you)
- You need a written reason for each score
What happened to MadKudu?
MadKudu was a well-known predictive lead scoring tool. It was acquired by HG Insights in August 2025, and its pricing page now redirects to HG Insights, so it's no longer sold as a standalone product.
How do you choose a lead scoring tool?
Start with what you're scoring, then check what data you have.
- Decide what you're scoring. Inbound leads who engaged with you, or outbound prospects who haven't yet.
- Check what data you have. Predictive tools need hundreds of past conversions. Without them, use prompt-based scoring against written criteria.
- Check where scores go. A score only matters if it changes who gets contacted first. Prefer a tool connected to your outreach.
- Close the loop. Compare your scores against who actually replied. If high scores don't reply more, your criteria are wrong.
FAQ
What is AI lead scoring?
AI lead scoring uses a model to rate how likely a lead is to buy, instead of a points table someone set by hand. Predictive tools like HubSpot and Salesforce Einstein learn from your past conversions. Prompt-based tools like Clay or a Jev script score each lead against criteria you write. Either way, the goal is to decide who gets contacted first.
What is the difference between AI lead scoring and rule-based lead scoring?
Rule-based scoring adds fixed points for things like job title or an email open. AI scoring either learns the weights from your past wins, or reads each lead's profile and judges it against a description of your ideal customer. Rules are easy to explain but go stale. AI scoring adapts, but predictive models need enough past conversions to learn from.
How much does AI lead scoring cost?
It ranges from a few cents to thousands of dollars a month. HubSpot's AI-built scores need Marketing Hub Enterprise at $3,600 a month. Clay starts at $167 a month and meters AI columns in credits. Warmly's plan with lead scoring is $10,000 a year. Scoring 1,000 leads yourself with TypeSafe's Jev model costs about 4 cents in API fees, but you have to build it.
Can Jev score leads without writing code?
Not directly. Jev is a model API from TypeSafe AI, not an app, so on its own you need a script to send it each lead and read back the score. To use it without code, pick a tool that runs Jev for you. Sliq uses Jev to tag your prospects into categories you define and shows the reply and meeting rate for each one.
Which lead scoring tool is best for outbound?
For outbound, you are scoring people before they have engaged with you, so tools that score website visits and email opens have little to work with. Clay and Apollo score prospects from firmographic and enrichment data. Sliq checks each prospect against your criteria when it finds them, then shows which kinds of prospects actually reply, so you can aim the next list better.