How to enrich leads in Google Sheets without Clay

You can enrich leads in Google Sheets without Clay by scraping each company's pages and extracting the fields you need with AI, directly in the sheet, paying only for rows that succeed. Clay is a powerful, well-built platform, but it is a separate tool with a credit system, per-seat pricing, and charges on failed lookups. If your enrichment is mostly firmographic (company size, industry, location, funding) and lives on the open web, you can do it in Sheets for a fraction of the cost. This post is an honest comparison: where the Sheets approach wins, where Clay still wins, and exactly how to run the pipeline.

Why look for a Clay alternative at all?

Clay is genuinely good at what it does, so the reasons to look elsewhere are usually about cost structure and complexity, not capability.

• Credit pricing is hard to predict. Clay restructured pricing in 2026 into two self-serve plans (roughly $185 and $495 per month), and the real cost per enriched lead lands anywhere from about $0.14 to $0.67 depending on plan and how many credits a row burns.
• Failed lookups can still cost credits. A low-coverage source can quietly consume 20 to 30 percent of an allocation on rows it never resolved.
• It is another tool to learn and pay seats for. Clay's table-and-recipe model is powerful but has a real learning curve, and it sits outside the spreadsheet where your list already lives.

None of that makes Clay wrong. It makes it overkill for a team whose enrichment is company-level and whose data already lives in a sheet.

What you give up, honestly

A fair comparison names the gap. Clay's strength is its data marketplace: it fans a single row out across many providers in a waterfall, so for direct-dial mobile numbers, deep technographics, and high-coverage personal emails, it will out-cover a scrape-and-extract approach. That data is licensed, not scraped, and you cannot reproduce all of it from public pages.

So if your enrichment depends on personal phone numbers at scale, or a 15-source parallel waterfall that resolves a cold list in minutes, Clay (or a dedicated data vendor) is the right tool. The Sheets approach is not trying to beat a licensed contact database at its own game.

Where the Sheets approach wins

For the common case, enriching company-level fields and then writing personalised copy, doing it in Google Sheets has real advantages:

• You only pay for rows that succeed. Failed or blank lookups cost nothing, so there is no silent credit drain.
• You see the price before you run. A cost preview shows the exact figure for your row count, so there is no end-of-month surprise.
• Every field is auditable. The scraped text and each extracted column sit right there in the sheet, so you can spot-check a value instead of trusting a black-box credit.
• The AI step is first-class. Scoring rows against your ICP, summarising a scraped page, or writing a personalised opener is the same engine as the extraction, not a bolt-on.
• No new tool. It runs in a sidebar where your list already is.

How to enrich leads in Google Sheets, step by step

Here is the pipeline that replaces a basic Clay enrichment table. It chains scrape, AI extraction, AI scoring, and verify in one sheet.

1. Start with a domain. Your list needs at least a company name or website per row. A name alone is fine; the scrape step will find the site.
2. Scrape the source pages. Open the sidebar with Extensions, ReplyLabs, Open sidebar, select the rows, and run Scrape on the domain column. The about, product, and careers text lands in a context column.
3. Extract firmographics with AI. In the AI tab, write an extraction prompt that references the scraped column by header: "From {{Scraped text}}, return the industry, employee range, and headquarters city, one per column. If the page does not say, return Unknown."
4. Score against your ICP. Run a second AI prompt: "Given industry {{Industry}} and size {{Employee range}}, reply with exactly one of: Fit, Not a fit." Now you can sort and filter.
5. Verify the survivors. Filter to the rows marked Fit, then run Verify on the email column so you only spend the verify budget on accounts worth contacting.

The result is a segmented, scored, deliverable list, the same output a Clay table produces, built from open-web data and AI in the sheet you already use. The full mechanics live in the lead enrichment in Google Sheets guide.

A cost comparison you can sanity-check

Take a 1,000-row list you want to enrich with firmographics, score, and verify.

On a credit-based platform, at roughly $0.14 to $0.67 per enriched lead, 1,000 rows is somewhere between $140 and $670 of credits, before you account for credits burned on failed lookups, and on top of the monthly platform fee.

In ReplyLabs, you pay per operation and only on success:

• Scrape from $0.005 per URL, so about $5 for 1,000 pages.
• AI extraction plus scoring at the provider's raw cost times 1.25 plus a $0.0025 base fee per succeeded row, low single-digit dollars per pass on a small model.
• Verify at $0.01 per email, and only on the rows that survived the filter, not all 1,000.

You also start with a free $20 credit, which covers a first full pass on a list this size with room to spare. On Pro and Scale you can bring your own AI key and drop the AI markup entirely. To model your own numbers, use the AI cost calculator, and for the head-to-head feature view see the Clay alternative comparison.

Common questions

Is there a free Clay alternative for lead enrichment?

ReplyLabs gives every new account a $20 credit, which is enough to enrich and verify a list of around a thousand company-level rows. After that you pay per operation, only for rows that succeed, with no monthly platform fee.

Can I get the same data as Clay in Google Sheets?

For firmographic and company-level data that lives on the open web, yes, scraping plus AI extraction covers it. For high-coverage personal phone numbers and deep technographics from licensed databases, a dedicated platform like Clay still has the edge.

Does enrichment in Sheets handle waterfall logic?

It handles the practical version: scrape the most likely source, extract, and re-run only the rows that came back thin. It does not fan a single row across 15 licensed data vendors the way Clay does, which is the trade-off for the lower cost.

How much does it cost to enrich 1,000 leads in Google Sheets?

A full scrape, extract, score, and verify pass on 1,000 company-level rows typically lands in low double-digit dollars, and the free $20 credit covers a first run. The cost preview shows the exact figure before each step.

Do I need to know how to code?

No. The whole pipeline runs from a sidebar in Google Sheets: select rows, write a prompt, check the cost, click run. See how it works for an overview.