Bulk-analyze 1,000 keywords efficiently

The max batch size is 1,000 keywords. At that scale, small inefficiencies compound. This guide is how to run big batches without wasting credits or time.

Why bulk is cheaper per keyword

The setup fee is flat — 10 credits — regardless of batch size. So:

10 keywords → 10 + 10 = 20 credits → 2 credits/keyword
100 keywords → 10 + 100 = 110 credits → 1.1 credits/keyword
1,000 keywords → 10 + 1,000 = 1,010 credits → 1.01 credits/keyword

Large batches hit the theoretical minimum cost. If you're doing broad discovery, batch as large as you can justify.

Prep: dedupe and normalize

Before pasting your list:

Lowercase everything. SERPTool lowercases internally but if you paste Best Laptop and best laptop separately you may end up with two rows (depending on dedup).
Trim whitespace and quotes. A keyword with a leading space or surrounding quotes is treated as distinct from the same keyword clean.
Remove duplicates. In Sheets: Data → Remove duplicates. Trivial but easy to forget.
Remove empty lines. SERPTool ignores them but they look sloppy.
Strip brand-specific noise. If you pasted from a keyword tool that appends (yourbrand) to every row, strip it.

A cleanup pass typically reduces a 1,000-row paste to ~900 unique rows. That's 100 credits saved.

Prep: sanity-check with a small sample first

Before spending 1,010 credits, run a 20-keyword sample from the same list. Check:

Do the results look reasonable? Sometimes seed lists are full of typos or industry-specific jargon that Google doesn't understand, and you don't want to discover that at 1,000 scale.
Is the location and device setting right? Default is US desktop; if you're UK mobile-targeting, set it now.
Is the intent distribution matching what you expected? If you intended commercial but you're seeing 80% informational, the seed list needs re-scoping.

Sample runs cost 10 + 20 = 30 credits. Cheap insurance.

Running the batch

Paste all 1,000 keywords into the KEYWORD mode textarea. Name the analysis meaningfully (e.g., Cluster 3: Integration guides - 2026-04) so you can find it in the dashboard list later.

Click Start. The run is queued to the background worker and processes at roughly 3–8 keywords per second, depending on DataForSEO's SERP API latency. A 1,000-keyword batch typically completes in 5–15 minutes.

You can close the tab. The worker keeps going. Come back when you're ready.

While it's running

The results page refreshes live as keywords complete. You can:

Sort and filter partial results while processing continues.
Expand rows that have finished to look at their SERP breakdown.
Add completed keywords to collections as they come in — no need to wait for the full batch.

Don't cancel unless you have to — cancelling stops new keywords from being processed, but credits already spent on completed keywords aren't refunded.

After the batch completes

Expect 0.5–3% of keywords to fail (failed_count on the analysis page). Common reasons:

DataForSEO returned no results (keyword doesn't register in its database).
Timeout on a particularly slow SERP.
Transient API error.

Failed keywords don't cost credits. Re-run the failed ones in a separate small batch if they matter.

Working with the results

At 1,000-keyword scale, the UI can feel cluttered. Three tactics:

Aggressive filtering. Set Opportunity Score ≥ 70 and search volume ≥ 200 to reduce visible rows to the ~200 worth looking at.

Paginate and work in chunks. Sort by Opportunity Score and review the top 50 in detail. Collect the winners. Re-sort by a different metric (e.g., CPC descending) and review the top 50 of that cut. You're building a mental model faster than scrolling through 1,000 rows.

Export early. Export the Summary CSV once the run completes. Do the detailed analysis in Sheets where filtering and pivoting are faster.

Don't bulk-fetch AI Mentions

AI Mentions costs 40 credits per keyword. Running it on all 1,000 = 40,000 credits = a monthly Professional plan's worth. Fetch AI Mentions only for the 20–50 keywords you're actually considering targeting, after the main analysis has winnowed them down.

When to split a batch

If you have 5,000 seed keywords, don't just do 5 × 1,000. Some alternatives:

Topical chunks: batch-by-cluster (e.g., "integration keywords", "tutorial keywords") so each analysis corresponds to a coherent topic.
Priority chunks: process your top 1,000 by external relevance score first. If the results are meh, you've saved 4,000 credits.
Head-vs-long-tail chunks: separate batches for head terms vs long-tail — different decision criteria, different review thresholds.