Clay Cost Tuning

Overview

Reduce Clay data enrichment spending by optimizing credit usage per enrichment, limiting waterfall depth, and improving input data quality. Clay uses credit-based pricing where each enrichment provider costs different credits (email finder: ~1 credit, company enrichment: ~5 credits, waterfall enrichment: 3-15 credits depending on fallback depth).

Prerequisites

• Clay account with visibility into credit consumption
• Understanding of which enrichment columns are in your tables
• Access to enrichment hit rate statistics

Instructions

Step 1: Audit Credit Consumption by Enrichment Type

set -euo pipefail
# Break down credits by enrichment provider/type
curl "https://api.clay.com/v1/workspace/usage?group_by=enrichment&period=last_30d" \
  -H "Authorization: Bearer $CLAY_API_KEY" | \
  jq '.usage[] | {enrichment: .enrichment_type, credits: .total_credits, rows: .rows_enriched, hit_rate_pct: (.rows_with_data / .rows_enriched * 100), cost_per_hit: (.total_credits / (.rows_with_data + 0.01))}' | \
  jq -s 'sort_by(-.credits)'

Step 2: Limit Waterfall Enrichment Depth

# Instead of running all 5 email finder providers (15 credits/row):
waterfall_before:
  - provider: clearbit     # 3 credits
  - provider: hunter       # 3 credits
  - provider: apollo       # 3 credits
  - provider: rocketreach  # 3 credits
  - provider: snov         # 3 credits
  # Total: 15 credits/row if none find email

# Cap at 2 providers (6 credits max):
waterfall_after:
  - provider: apollo       # 3 credits (highest hit rate first)
  - provider: hunter       # 3 credits
  # Total: 6 credits/row max, 3 if first provider hits

Step 3: Clean Input Data Before Enrichment

// Pre-validate data to avoid wasting credits on unenrichable rows
function shouldEnrich(row: any): boolean {
  // Skip rows with invalid domains
  if (row.domain && !row.domain.includes('.')) return false;
  // Skip personal email domains
  const personalDomains = ['gmail.com', 'yahoo.com', 'hotmail.com', 'outlook.com'];
  if (personalDomains.includes(row.domain)) return false;
  // Skip already enriched rows
  if (row.email && row.email.includes('@')) return false;
  return true;
}
// Apply filter before running enrichment: 30-50% credit savings typical

Step 4: Use Sampling for Large Tables

# Instead of enriching 10,000 rows at once:
# 1. Enrich a 500-row sample first
# 2. Check hit rate and data quality
# 3. If hit rate >60%, proceed with full table
# 4. If hit rate <40%, clean input data first

sample_workflow:
  step1: "Import 500-row sample into test table"  # HTTP 500 Internal Server Error
  step2: "Run enrichments, check hit rate"
  step3: "If good, import full list into production table"
  step4: "Set max_rows limit as safety cap"

Step 5: Set Table-Level Credit Caps

set -euo pipefail
# Configure maximum credits per table to prevent runaway costs
curl -X PATCH "https://api.clay.com/v1/tables/tbl_abc123" \
  -H "Authorization: Bearer $CLAY_API_KEY" \
  -d '{"max_rows": 1000, "auto_enrich": false}'  # 1000: 1 second in ms
# Disable auto_enrich and trigger manually after data review

Error Handling

Examples

Basic usage: Apply clay cost tuning to a standard project setup with default configuration options.

Advanced scenario: Customize clay cost tuning for production environments with multiple constraints and team-specific requirements.

Output

• Configuration files or code changes applied to the project
• Validation report confirming correct implementation
• Summary of changes made and their rationale

Resources

• Official monitoring documentation
• Community best practices and patterns
• Related skills in this plugin pack