Claygent Alternative: Cheaper Web Research after Clay's 2026 Overhaul

A r/gtmengineering thread asking for a cheaper Claygent alternative hit a nerve this week. Clay's March 2026 pricing overhaul moved Claygent access to the $185/mo Clay Launch tier with 2,500 data credits and 15,000 actions. Teams running any real outbound enrichment volume hit the ceiling in a week. Here is the breakdown on migrating off.

What Claygent Actually Does

Claygent bundles three things: a web-research LLM prompt template, a data fetch layer (mostly Bing-based), and the Clay table UI that fills a column. The bundle is convenient and expensive. Unbundling it is the cost win.

The Unbundled Replacement

Replace each layer with a cheaper direct equivalent:

• Web-research LLM prompt: your own prompt pointed at Claude Haiku ($0.25/M tokens), Gemini 2.5 Flash ($0.15/M tokens), or a hosted open model.
• Data fetch layer: Scavio's multi-platform API. Google SERP, LinkedIn via SERP, Reddit threads, YouTube. Typed JSON means no markdown parsing step.
• Clay table UI: replaced by n8n, a custom TypeScript worker, or a Google Sheets plus Apps Script combo. Pick what your team already runs.

The Cost Math

For 10,000 enrichments per month:

• Claygent: $185/mo Clay Launch + credit overages (typically $100-$300/mo at this volume) = $285-$485/mo.
• Unbundled: Scavio $30/mo + LLM ~$30-80/mo + n8n Cloud $24/mo = $84-$134/mo. Roughly 3x cheaper.

At 100,000 enrichments per month the gap widens to 5x+ because Scavio and LLM costs scale linearly while Clay requires higher plan tiers for the extra actions.

The Working Code

Forty lines replicate the Claygent column behavior for a prospect enrichment task.

import os, requests, anthropic

SCAVIO = os.environ['SCAVIO_API_KEY']
client = anthropic.Anthropic()

def research_prospect(company: str, title: str) -> dict:
    # Fetch multi-platform signal
    serp = requests.post('https://api.scavio.dev/api/v1/search',
        headers={'x-api-key': SCAVIO},
        json={'query': f'{company} {title}'}).json()

    reddit = requests.post('https://api.scavio.dev/api/v1/search',
        headers={'x-api-key': SCAVIO},
        json={'query': company, 'platform': 'reddit'}).json()

    # Compose context
    context = '\n'.join([
        f"SERP: {x['title']} - {x['snippet']}"
        for x in serp.get('organic_results', [])[:5]
    ]) + '\n\n' + '\n'.join([
        f"REDDIT: {p['title']}"
        for p in reddit.get('posts', [])[:3]
    ])

    # LLM composition (bring your own prompt)
    msg = client.messages.create(
        model='claude-haiku-4-5-20251001',
        max_tokens=512,
        messages=[{'role': 'user', 'content': f'''
Given this prospect research on {company} ({title}):

{context}

Return JSON with:
- personalized_hook: one-sentence opener for an outbound email
- recent_signal: one recent event worth mentioning
- objection_risk: likely push-back
'''}])

    return {'company': company, 'title': title,
            'research': msg.content[0].text}

What You Lose

Three things. One, the Clay table UI where non-technical teammates click buttons. If your GTM team is not comfortable with n8n or a Google Sheet, stay on Clay. Two, Clay's integrations with Salesforce, HubSpot, and others that the DIY path has to rebuild. Three, Clay's prebuilt enrichment recipes that solve common patterns out of the box.

What You Gain

Four things. One, roughly 3x to 5x cost reduction. Two, full control of the prompt (tune it weekly based on reply rates). Three, Reddit coverage which Claygent does not have. Four, data portability - your enrichment outputs land in your DB, not inside Clay.

The Migration Timeline

Two weeks is realistic. Week one: build the unbundled pipeline for one Clay column. Run it in parallel with Claygent, compare outputs. Week two: port the remaining columns, cut over, downgrade the Clay plan.

The full comparison is at scavio-vs-claygent with the detailed per-column breakdown. The competitor profile of Claygent itself is at competitors/claygent for teams evaluating in the other direction.