FullEnrich

Most teams looking at FullEnrich vs Gumloop are scoping the wrong question. These tools sit at different layers of an AI-native GTM stack: FullEnrich is a specialist enrichment component, Gumloop is a general-purpose workflow builder. The honest framing isn't "which one"—it's "which bottleneck is biting first." Pick by bottleneck and the buy decision sharpens.

Typical fit: who each tool is built for

Typical FullEnrich customer

RevOps or GTM engineer who already runs an orchestration tool (Clay, n8n, Make.com). Workflow chains multiple enrichment providers (Apollo, ZoomInfo, Hunter, Lusha) and credit waste is the actual line item. Needs verified emails and mobile phone numbers at volume; mobiles are the hard sub-problem. ABM motion enriching hundreds to low thousands of contacts per week. Comfortable treating FullEnrich as a component, not a platform.

Typical Gumloop customer

Series A–B RevOps with LLM-step workflow shape as the bottleneck—web scrape → LLM reason → CRM writeback in one graph. Wants LLM-of-choice flexibility (Anthropic for reasoning, OpenAI for structured extraction). Bottleneck is not enrichment depth; the team would build the same flow on Zapier or Make.com but their LLM ergonomics are too weak. Tolerates BYO LLM API key and separate cost instrumentation.

Neither if you're…

• Bottlenecked on signal-to-touch latency—see Unify or Common Room.
• Needing enterprise data depth + intent + GTM workflow under one DPA—see ZoomInfo.
• Needing general-purpose enrichment orchestration with 100+ data sources—see Clay.

When FullEnrich wins

FullEnrich wins when contact-resolution cost is the binding constraint at volume.

• Hit-only billing on the waterfall. You're charged once per contact when any provider returns a match, not once per provider queried. Materially cheaper than chaining the same providers via separate Clay enrichment columns where each column burns its own credits even on misses.
• Mobile phone cascade. Phone data is the genuinely hard sub-problem in B2B enrichment—email finders are commoditized, verified mobiles are not. FullEnrich routes through Lusha, Cognism, Datagma, and others; roughly the same set EU-focused buyers tap directly.
• Single HTTP column inside Clay tables. Compresses three or four Clay enrichment columns into one paid lookup. The integration story most operators actually wire is: build the list and research logic in Clay, then call FullEnrich as one HTTP column. See the SDR list-building playbook and the CRM enrichment use case.

System view: input = LinkedIn URL or name + company from Clay table, AI step = waterfall routing logic across 15+ providers (deterministic, not generative), human review = RevOps validates aggregate match rate and false-positive rate on a sample, writeback = enriched fields land in CRM contact records, metric = cost per enriched contact and match rate by region.

When Gumloop wins

Gumloop wins when LLM-step workflow shape is the binding constraint.

• LLM-of-choice nodes. Claude for nuanced reasoning, GPT for structured JSON extraction—switch by node, not by platform. Material when model capabilities diverge by use case, which they increasingly do in 2026.
• Visual node graph approachable for RevOps. Closer to Make.com's UX than Zapier's linear step list. Subworkflows allow composing agent patterns without rebuilding. See the AI account research use case.
• Web scraping + LLM + CRM writeback in one canvas. Closes the gap that makes Zapier painful for GTM research workflows—scraping a target site for ICP signals without a separate Apify/Browse.ai bill, then LLM-classifying, then writing to Salesforce or HubSpot. See the AE discovery prep playbook.

System view: input = webhook trigger (CRM event, form), scheduled run, or row input from Google Sheets/Airtable/Notion, AI step = LLM-of-choice nodes for summarization/classification/extraction/generation, human review = RevOps tests workflow on sample before promoting, writeback = CRM update or Slack message or Notion page, metric = workflow runs per dollar (LLM cost + Gumloop plan), output quality on sampled outputs.

When you need both

Genuinely common for ABM teams at Series A–B and beyond. The pattern: Gumloop orchestrates the workflow (web scrape → LLM classify → identify contact need), FullEnrich resolves the contact via a single HTTP node when needed, CRM gets the writeback. Or: Clay is the canvas, FullEnrich is one HTTP column for contact resolution, Gumloop runs separate LLM-heavy plays (account research, personalization drafts) that Clay's Claygent isn't the right model for.

Both tools deliberately stop short of the other's surface. FullEnrich has no workflow canvas; Gumloop has no enrichment-provider waterfall. Wiring them together is straightforward via REST API or webhook—neither tool fights you on integration. The cost discipline is harder: instrument LLM cost per run in Gumloop and credit consumption in FullEnrich; both can scale to surprise the CFO at the same time if you're not watching.

Pricing and per-account math

FullEnrich: ~$29/mo Starter to ~$1,950/mo Enterprise (market reports), credit-based with hit-only billing across the waterfall.^[1] Higher tiers unlock more concurrent providers and team seats. Effective $/contact depends on which providers fire most often in your specific ICP—run the match-rate audit before scaling spend.

Gumloop: free tier, ~$37/mo Starter, ~$244/mo Pro, Enterprise custom (vendor-published).^[2] LLM API costs are typically bring-your-own-key on top. At scale, LLM cost dwarfs the Gumloop subscription—a workflow that costs $0.05 per run at pilot scale costs $5,000 at 100K runs/month.

Per-account math sanity check (illustrative, not invented dollars): for an ABM motion enriching 1,000 contacts per week, FullEnrich's effective $/contact at 60%+ match rate on clean LinkedIn-URL inputs tends to come out cheaper than chaining providers in Clay—but model it against your match rate, not the headline tier. For Gumloop, instrument cost per run from day one: account-research workflows with Anthropic Claude reasoning + web scraping can run $0.10–$0.50 per account before you notice; scale that to 1,000 accounts/week and the LLM bill is the actual line item, not the Gumloop plan.

Feature overlap and gaps

The overlap is small. Most "shared" capabilities are surface integrations (CRM, Zapier, API)—not the actual job each tool does.

Compare against Clay on the canvas surface and Apollo on the bundled-data side—see Clay vs Apollo.

The buying mistakes we see most

1. Buying FullEnrich as the only enrichment tool when you also need firmographic enrichment or AI research. Cost: a quarter of half-built workflows; nothing to drive list-building or branching logic from. Fix: scope FullEnrich to contact resolution; pair with Clay for the orchestration layer.
2. Buying Gumloop expecting it to replace Clay's enrichment depth. Cost: six months in, discover enrichment depth is the actual bottleneck; end up paying for both. Fix: vet the bottleneck with the one-week test below before committing.
3. Skipping LLM cost instrumentation on Gumloop. Cost: $5K LLM bill in month three on a workflow nobody knew was running 100K times. Fix: instrument cost per run from day one, alert on monthly spend, build dedupe and rate limiting into the trigger layer.

What to test in week 1

FullEnrich one-week test: pick one segment—e.g., "EU mid-market mobile numbers for the top-200 target accounts" or "verified emails for inbound MQLs missing contact data." Pull 100 records you've already enriched another way (Clay table, Apollo export, ZoomInfo lookup); you need known answers to score. Run those 100 through FullEnrich's bulk import. Record match rate, credit cost, latency. For 20 sampled records (hit + miss), manually verify the email or phone. Score false-positive rate. If effective $/verified-contact is cheaper than your existing pipeline and match rate is within 5% of your current setup, scale it.

Gumloop one-week test: pick one workflow—account research for AE discovery prep, ICP enrichment from a target-account list, or cold-email personalization draft for SDR sequences. Write the success definition in a shared doc. Audit upstream data (source-of-truth completeness, duplicate handling). Build in Gumloop with all human-approval gates on. Track cost per run (Gumloop plan share + LLM API), latency per run, output quality on 20 manually-reviewed runs. Build the same workflow in your current tool (Zapier, Make.com, or Clay) for honest comparison.

If either week-1 test fails the manual review step, do not scale—data readiness is the bottleneck, not the tool.

Migration and coexistence

FullEnrich and Gumloop are not substitutes. Migration questions are usually framed wrong. The real patterns:

• FullEnrich → Clay native enrichment: rare, because you're moving from cheap hit-only billing to per-column credits; usually only happens when an enterprise contract bundles Clay credits at a steep discount.
• Gumloop → Clay: common when the actual bottleneck turned out to be enrichment depth, not LLM workflow shape. The visual graphs don't migrate to Clay's table syntax cleanly—expect to rebuild.
• Gumloop → Make.com or Zapier: common when LLM-step ergonomics aren't the real need and pure integration plumbing wins.

Coexistence (the most common production pattern): Clay as the canvas, FullEnrich as one HTTP column for contact resolution, Gumloop as a parallel surface for LLM-heavy plays Clay's Claygent isn't suited for. All three write to Salesforce or HubSpot with explicit field ownership. RevOps owns the cost instrumentation across all three—LLM spend in Gumloop, credit consumption in FullEnrich, Clay credit consumption in Clay.

FAQ

Is FullEnrich a replacement for Gumloop? No. FullEnrich is contact-resolution waterfall; Gumloop is LLM-of-choice visual workflow building. They solve unrelated bottlenecks. Most ABM teams that adopt both keep both.

Is Gumloop a replacement for FullEnrich? No. Gumloop has no enrichment-provider waterfall; you can call FullEnrich (or Apollo, or ZoomInfo) as an API node inside a Gumloop workflow, but Gumloop itself doesn't resolve contacts. Calling external enrichment APIs from Gumloop is the integration pattern, not a replacement.

Can we use both with one CRM without dual-write conflicts? Yes, with explicit field ownership. Same governance pattern as the Salesforce field-ownership default—define per-field which tool owns the write. FullEnrich typically owns email + phone columns; Gumloop typically owns AI-generated summary or classification fields.

Where does Clay fit? Underneath both, usually. Clay is the orchestration canvas with 100+ data sources; FullEnrich runs as one HTTP column; Gumloop runs separate LLM-heavy plays. See Clay vs Apollo for the orchestration-vs-bundled framing.

What if our bottleneck is signal-to-touch latency, not enrichment or workflow shape? Neither tool. See Unify for signal-aggregation + built-in sending or Common Room for community/dev-signal coverage feeding into Outreach or Salesloft.

Does gtmpod earn commission on either tool? No affiliate on either page. We name Clay and Apollo as the better starting points when the use case extends past one of these two narrow lanes.