API Reporting Dashboard: Monitor Your API Usage

The LeadMagic API Reporting Dashboard provides comprehensive insights into your API usage, helping you monitor consumption, track performance, identify issues, and optimize your integration. Whether you're debugging, forecasting costs, or improving efficiency, the dashboard gives you the data you need.

What you can track:

• Total API requests and success rates

• Credit consumption over time

• Daily usage patterns and trends

• Response times and performance metrics

• Error rates and failed requests

• Detailed request logs with full payloads

• Billing cycle and renewal timing

Who should use this:

• Developers integrating LeadMagic API

• GTM teams using Clay, Apollo, or Instantly

• Engineering teams monitoring production usage

• Finance teams tracking API costs

• Product managers analyzing feature adoption

• Technical operations monitoring system health

Accessing the Dashboard

How to Get There

1. Log in to LeadMagic

   • Go to app.leadmagic.io

   • Sign in with your credentials

2. Navigate to API Dashboard

   • Click "API" in the main navigation

   • Select "Reporting" or "Dashboard"

   • Or go directly to: app.leadmagic.io/api/reporting

Note: You need an active API key to access the dashboard. If you don't have one yet, generate it in Settings > API.

Dashboard Permissions

Who can access:

• Account owners (full access)

• Admins (full access)

• Team members with API permissions (read-only in most cases)

What you can see:

• All API usage for your account

• If you have a team account, you'll see combined team usage

• Individual team member API usage (admin view)

Dashboard Overview

When you open the dashboard, you'll see key metrics at the top providing a quick snapshot of your API health.

Credits Consumed

What it shows:

• Total credits used via API in the last 30 days

• Displayed as a single number (e.g., "4,301.7")

Why it matters:

• Quick view of monthly API spending

• Compare against your subscription allocation

• Identify unusual consumption spikes

• Forecast future credit needs

Example:

What to watch for:

• Consumption approaching your monthly allocation

• Sudden spikes compared to typical usage

• Steady, predictable consumption patterns

• Consumption well below allocation (room to grow)

Total API Requests

What it shows:

• Total number of API calls made in last 30 days

• Success rate percentage

• Displayed as count + percentage (e.g., "60.6K requests, 99% success rate")

Why it matters:

• Measure integration health and reliability

• Track volume trends over time

• Success rate indicates data quality and API stability

• High volume with low success = potential issues

Example:

Success rate benchmarks:

• 98-100%: Excellent - healthy integration

• 95-97%: Good - minor issues to investigate

• 90-94%: Fair - needs attention

• Below 90%: Poor - immediate investigation required

Common causes of low success rates:

• Invalid input data (malformed emails, incorrect domains)

• Rate limiting (too many requests too quickly)

• Network/connectivity issues

• Incorrect API key or authentication

• Using deprecated endpoints

Days Until Renewal

What it shows:

• Countdown to next billing cycle

• Exact renewal date

Why it matters:

• Plan for credit refresh

• Time usage appropriately if running low

• Prepare for upcoming charges

• Schedule heavy usage after renewal if needed

Example:

Usage strategies:

• If running low with days remaining: Pace usage or purchase top-up

• If abundant credits near renewal: Unused credits may roll over (check plan)

• If heavy usage planned: Schedule after renewal for fresh allocation

Credits Consumption Breakdown

This chart visualizes your daily credit usage over the selected time period, making it easy to spot trends and anomalies.

Understanding the Chart

What you see:

• X-axis: Dates (typically last 30 days)

• Y-axis: Credits consumed per day

• Bars: Height represents daily credit usage

• Colors: May differentiate between API products (if multiple)

How to read it:

What to look for:

Healthy patterns:

• Consistent daily usage (steady bars)

• Expected weekday/weekend patterns (higher weekdays, lower weekends)

• Gradual growth as usage scales

• Predictable campaign spikes

Warning signs:

• Unexpected spikes (investigate cause)

• Sudden drops (integration issue?)

• Erratic patterns (inconsistent usage)

• Trending to zero (broken integration?)

Using the Chart for Analysis

Identify usage patterns:

Pattern 1: Steady Daily Usage

• Example: 300-500 credits every day

• Indicates: Consistent production integration

• Action: Predict monthly usage by multiplying daily average × 30

Pattern 2: Business Hours Pattern

• Example: High usage Mon-Fri, low Sat-Sun

• Indicates: Business-driven workflows

• Action: Ensure credits available for weekday peaks

Pattern 3: Campaign Spikes

• Example: Baseline 200/day, spike to 2K on specific day

• Indicates: Batch processing or campaign launches

• Action: Plan credits accordingly for future campaigns

Pattern 4: Gradual Growth

• Example: 100/day increasing to 300/day over weeks

• Indicates: Scaling integration

• Action: Review plan allocation, consider upgrade

Filters and Time Ranges

Time period selector:

• Last 7 days

• Last 30 days (default)

• Last 90 days

• Custom date range

Product filter:

• All products (combined view)

• Email Validation only

• Work Email only

• Mobile Number only

• Company Enrichment only

• Social Profile only

Use cases for filtering:

By product:

• "Which endpoint consumes most credits?"

• "Is email validation driving our usage?"

• "Should we optimize mobile number usage?"

By time period:

• Last 7 days: Recent issues or changes

• Last 30 days: Monthly billing cycle analysis

• Last 90 days: Long-term trends and growth

Daily API Usage Overview

This table provides detailed metrics for each day, giving you granular insights into API performance and reliability.

Table Columns Explained

Date

• Calendar date of usage

• Click "View Requests" to see detailed logs for that day

Requests

• Total API calls made that day

• Higher volume = more integration activity

• Compare day-over-day for trends

Success Rate

• Percentage of successful requests (2xx status codes)

• Target: 98%+ for healthy integration

• Below 95%: Investigate immediately

Credits

• Total credits consumed that day

• Compare to request volume for efficiency

• Some endpoints cost more credits than others

Avg Response Time

• Average time to receive API response

• Measured in milliseconds (ms) or seconds (s)

• Lower is better for performance

• Typical: 500ms - 2s for most endpoints

P95 Response Time

• 95th percentile response time

• Shows slower edge cases (5% of requests take this long or longer)

• Important for worst-case performance planning

• Should be reasonable (under 5s for most use cases)

Errors

• Count of failed requests

• Includes 4xx (client errors) and 5xx (server errors)

• Zero errors is ideal

• Investigate any consistent error patterns

Example Row Analysis

Example: High-performing day

Nov 11, 2025
Requests: 3,293
Success Rate: 100.0%
Credits: 27.55
Avg Response: 853ms
P95 Response: 3.47s
Errors: 0

Analysis:

• High volume with perfect success rate

• Low credit usage (efficient requests)

• Fast average response time

• P95 under 5s (good performance)

• Zero errors (healthy integration)

Example: Problem day

Nov 05, 2025
Requests: 7,672
Success Rate: 95.9%
Credits: 1,320.9
Avg Response: 3.04s
P95 Response: 8.79s
Errors: 315

Analysis:

• High volume but lower success rate

• Very high credit usage

• Slower than average response times

• 315 errors (4.1% error rate)

• Click "View Requests" to investigate errors

Possible causes:

• Batch processing with invalid input data

• Rate limiting (too many requests too fast)

• Network issues or timeouts

• Bug in integration code

• Data quality problems

Sorting and Filtering

Sort by any column:

• Date (chronological order)

• Requests (find highest/lowest volume days)

• Success Rate (identify problem days)

• Credits (find most expensive days)

• Response Time (performance analysis)

• Errors (troubleshooting)

Use cases:

Sort by Credits (descending):

• Find most expensive days

• Identify cost optimization opportunities

• Plan budget for similar future usage

Sort by Success Rate (ascending):

• Find days with most failures

• Prioritize troubleshooting efforts

• Identify patterns in errors

Sort by Errors (descending):

• See all problem days at once

• Investigate root causes

• Track error trends over time

Detailed Request Logs

Click "View Requests" on any day to see individual API calls with full details.

Request Log Columns

Timestamp

• Exact time of API call (HH:MM:SS)

• Timezone: Your account timezone

• Useful for correlating with application logs

Product

• Which LeadMagic product was called

• Examples: Email Validation, Find Work Email, Mobile Number, Company Enrichment

• Helps identify which features drive usage

Endpoint

• API endpoint path

• Examples: /email-validate, /work-email, /mobile-number

• Direct correlation to your code

Credits

• Credits consumed by this specific request

• 0 = Failed request (no charge)

• 1 = Successful single enrichment

• Higher = Successful multi-credit endpoint

Status Code

• HTTP status code returned

• 200 = Success

• 400-499 = Client error (your side)

• 500-599 = Server error (our side)

Response Time

• How long this specific request took

• Measured in milliseconds (ms) or seconds (s)

• Helps identify slow requests

Request Body

• Input data you sent

• JSON formatted

• Example: {"email":"[email protected]"}

Response Body

• Data we returned

• JSON formatted (usually truncated in table)

• Click to expand full response

Example Request Analysis

Successful request:

Timestamp: 00:55:57
Product: Email Validation
Endpoint: /email-validate
Credits: 0
Status: 200
Response Time: 1.1s
Request Body: {"email":"[email protected]"}
Response Body: {"email":"[email protected]","email_status":"unknown"...}

Why 0 credits? Email validation typically doesn't charge credits, or charges based on validation type. Check your plan for specifics.

Failed request example:

Timestamp: 02:34:12
Product: Work Email
Endpoint: /work-email
Credits: 0
Status: 400
Response Time: 234ms
Request Body: {"first_name":"John","last_name":"","domain":"example.com"}
Response Body: {"error":"last_name is required"}

Analysis:

• Status 400 = Client error

• Missing required field (last_name)

• No credits charged (failed request)

• Quick response (error caught early)

• Action: Fix validation in your code

Filtering Request Logs

Filter by Product:

• See only Email Validation requests

• Isolate Work Email searches

• Focus on Mobile Number lookups

• Analyze Company Enrichment calls

Filter by Status:

• All statuses (default)

• Success only (200s)

• Client errors (400s)

• Server errors (500s)

Use cases:

"Show me all errors"

• Filter Status: 400s and 500s

• Identify error patterns

• Find common failure reasons

• Fix integration issues

"Show me slow requests"

• Sort by Response Time (descending)

• Identify performance bottlenecks

• Optimize timeout settings

• Report consistently slow endpoints

"What drove credit usage on this day?"

• Sort by Credits (descending)

• See which requests cost most

• Identify expensive batch operations

• Optimize credit consumption

Common Use Cases

1. Debugging Integration Issues

Scenario: Your application is returning unexpected results or errors.

Steps:

1. Go to Daily API Usage Overview

2. Sort by Success Rate (ascending) to find problem days

3. Click "View Requests" on affected days

4. Filter by Status: 400s and 500s

5. Review error responses for common patterns

6. Check request bodies for data quality issues

7. Correlate timestamps with your application logs

Common findings:

• Missing required fields in request body

• Malformed email addresses or domains

• Rate limiting (429 errors)

• Authentication issues (401 errors)

2. Optimizing Credit Usage

Scenario: Your credit consumption is higher than expected.

Steps:

1. Review Credits Consumption Breakdown chart

2. Identify days with unusual spikes

3. Click "View Requests" on high-consumption days

4. Sort by Credits (descending)

5. Look for patterns:

   • Multiple calls for same data (cache instead)

   • Failed requests retrying repeatedly

   • Unnecessary validation calls

6. Optimize code to reduce redundant requests

Optimization strategies:

• Cache enrichment results (TTL: 30-90 days)

• Batch requests when possible

• Validate input data before API calls

• Implement request deduplication

• Use conditional logic to skip unnecessary enrichments

3. Capacity Planning

Scenario: Planning to scale your integration or launch new features.

Steps:

1. Analyze Credits Consumption Breakdown over 90 days

2. Calculate average daily usage

3. Identify growth trends (linear, exponential)

4. Factor in planned features or campaigns

5. Estimate future credit needs

6. Compare to current plan allocation

Example calculation:

Current average: 200 credits/day
Current monthly: 6,000 credits
Planned 3x scale: 18,000 credits/month
Current plan: 10,000 credits/month
Recommendation: Upgrade to plan with 20,000+ credits

4. Performance Monitoring

Scenario: Ensuring your integration meets SLA requirements.

Steps:

1. Review Avg Response Time and P95 Response across days

2. Set acceptable thresholds (e.g., avg <1s, P95 <3s)

3. Identify days exceeding thresholds

4. Investigate slow requests on those days

5. Check for patterns:

   • Specific endpoints consistently slower

   • Time of day correlation

   • Request size impact

6. Optimize or adjust timeouts accordingly

Response time targets:

• Avg Response < 1s: Excellent for real-time applications

• Avg Response 1-2s: Good for most use cases

• Avg Response 2-5s: Acceptable for background jobs

• Avg Response > 5s: May need optimization or investigation

5. Cost Attribution

Scenario: Multiple teams using same API key; need to attribute costs.

Steps:

1. Review Daily API Usage for date ranges

2. Export request logs (if feature available)

3. Group by Product to see per-feature costs

4. Cross-reference with team deployment timelines

5. Calculate credits per team/feature

6. Use for chargeback or budget planning

Better approach: Consider using separate API keys per team for automatic attribution.

Performance Metrics Explained

Response Time Metrics

Average Response Time:

• Mean time across all requests

• Good for understanding typical performance

• Can be skewed by outliers

• Target: < 2s for most endpoints

P95 Response Time:

• 95% of requests complete within this time

• Better indicator of user experience

• Captures slower edge cases

• Target: < 5s for most endpoints

Why both matter:

• Avg = typical case

• P95 = worst reasonable case

• If Avg is good but P95 is bad = inconsistent performance

• If both are bad = systemic performance issue

Success Rate Calculation

Formula:

Success Rate = (Successful Requests / Total Requests) × 100
Successful = Status codes 200-299
Failed = Status codes 400-499, 500-599

Success rate targets by use case:

• Production real-time: 99%+ required

• Background jobs: 95%+ acceptable

• Experimental/testing: 90%+ okay

Improving success rates:

1. Validate input data before sending

2. Implement proper error handling

3. Retry failed requests with exponential backoff

4. Monitor and fix data quality issues

5. Check API documentation for requirements

Troubleshooting Common Issues

High Credit Usage

Symptoms:

• Credits depleting faster than expected

• Unexplained spikes in consumption

• Running out mid-month

Diagnosis:

1. Check Credits Consumption Breakdown for spikes

2. Review high-credit days in detail

3. Look for duplicate requests (same input)

4. Check for retry loops on failures

5. Identify inefficient usage patterns

Solutions:

• Implement caching (30-90 day TTL)

• Add request deduplication logic

• Fix retry logic (use exponential backoff)

• Validate data quality before API calls

• Consider batching requests

Low Success Rate

Symptoms:

• Success rate below 95%

• Many 400 or 500 errors

• Integration not working reliably

Diagnosis:

1. Filter request logs by Status: 400s

2. Review error messages in response bodies

3. Check for common error patterns

4. Validate request body format

5. Verify authentication

Common causes:

• Missing required fields

• Invalid data format (email, domain)

• Rate limiting (too many requests)

• Expired or invalid API key

• Network connectivity issues

Solutions:

• Validate inputs before sending

• Check API documentation for requirements

• Implement rate limiting on your side

• Verify API key is valid

• Add error handling and retries

Slow Response Times

Symptoms:

• Avg Response > 3s consistently

• P95 Response > 10s

• Application timeouts

Diagnosis:

1. Sort requests by Response Time

2. Identify consistently slow endpoints

3. Check request body size

4. Review time of day patterns

5. Test network latency

Common causes:

• Large batch requests

• Network latency issues

• High load on LeadMagic systems

• Geographic distance to API servers

• Client-side timeout settings too aggressive

Solutions:

• Break large batches into smaller chunks

• Implement parallel processing

• Set appropriate timeout values (30s+)

• Consider caching for repeated queries

• Contact support if consistent slow performance

Missing Data in Dashboard

Symptoms:

• Dashboard shows no data

• Recent requests not appearing

• Counts don't match expectations

Diagnosis:

1. Verify you're using correct API key

2. Check date range filter

3. Confirm requests are actually being sent

4. Review product filter (not filtered out)

5. Wait 5-10 minutes for data sync

Solutions:

• Refresh page

• Clear date range filters

• Select "All Products" in filter

• Check your application logs to verify API calls

• Allow time for data to sync (up to 10 minutes)

Unexpected Errors

Symptoms:

• Sudden increase in 500 errors

• Previously working integration fails

• Inconsistent error responses

Diagnosis:

1. Check LeadMagic status page

2. Review recent code changes

3. Compare error timestamps across requests

4. Check for LeadMagic API updates/changes

5. Review response bodies for error details

Solutions:

• Check status.leadmagic.io (if exists)

• Rollback recent code changes

• Implement retry logic with backoff

• Contact support with example request IDs

• Update integration if API changed

Best Practices

Regular Monitoring

Daily:

• Quick glance at success rate

• Check for error spikes

• Verify credit consumption is normal

Weekly:

• Review Credits Consumption Breakdown

• Analyze daily trends

• Investigate any anomalies

• Check average response times

Monthly:

• Deep dive into usage patterns

• Calculate cost per enrichment

• Plan for next billing cycle

• Review optimization opportunities

• Forecast future needs

Frequently Asked Questions

How often does the dashboard update?

The dashboard updates in real-time as APIs are used and take only seconds to appear in the logs.

Why don't my credit numbers match my billing?

The dashboard shows API credit usage only. Your total billing includes Manual CSV uploads (list credits). Check Settings > Billing for complete credit usage.

Why am I seeing 0 credits for some requests?

Some scenarios result in 0 credit charges:

• Failed requests (4xx/5xx errors)

• Email validation on certain plans

Check the response body and status code to understand why no credits were charged.

How do I improve my success rate?

Focus on data quality:

1. Validate inputs before sending to API

2. Handle required fields properly

3. Format emails and domains correctly

4. Implement proper error handling

5. Avoid rate limiting (pace requests)

What's a good response time target?

Targets by use case:

• Real-time applications: Avg < 1s, P95 < 3s

• Background jobs: Avg < 3s, P95 < 10s

• Batch processing: Avg < 5s, P95 < 15s

Can I get alerts when issues occur?

LeadMagic doesn't have built-in alerting yet.

How far back can I view data?

The dashboard typically shows the last 90 days of data. Older data may not be available unless exported previously.

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