# Reddit Lead Finder: Find Buying Intent Leads (`labrat011/reddit-lead-finder`) Actor
Find Reddit posts with buying intent: people asking for a tool like yours, unhappy with a competitor, or ready to switch. Delivered scored, ranked, and with a suggested reply on each. No API key, no login.
- **URL**: https://apify.com/labrat011/reddit-lead-finder.md
- **Developed by:** [mick\_](https://apify.com/labrat011) (community)
- **Categories:** Lead generation, Social media, Marketing
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet
## Pricing
from $20.00 / 1,000 qualified leads
This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event
## What's an Apify Actor?
Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".
## How to integrate an Actor?
If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.
In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js/docs.md):
```bash
npm install apify-client
```
In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.md):
```bash
pip install apify-client
```
In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md):
````bash
# MacOS / Linux
curl -fsSL https://apify.com/install-cli.sh | bash
# Windows
irm https://apify.com/install-cli.ps1 | iex
```bash
In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/integrations/mcp.md).
If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md).
For usage examples, see the [API](#api) section below.
For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).
# README
## Reddit Lead Finder: Find Buying Intent & Competitor Complaints
Turn Reddit into a lead list. Reddit Lead Finder scans public Reddit for people **asking for a tool like yours, complaining about a competitor, or looking to switch**, then hands you the few posts that matter, scored, ranked, and with a suggested reply on each. No API keys, no login, no manual thread-reading.
Built for founders and marketers who want customers, not a 1,000-row data dump.
### Why this over a raw Reddit scraper?
A raw scraper returns 1,000 posts and leaves you to read them. This finds the 12 that are actually leads.
| | Raw Reddit scraper | 🎯 Reddit Lead Finder |
|---|---|---|
| Output | Every post that matched | Only scored buying-intent leads |
| Reading required | You read 1,000 rows | You read the ranked shortlist |
| Per lead | Intent? Unknown | Intent type + score + why |
| Next step | Figure it out | Suggested reply on every lead |
| You pay for | Every row | Only qualified leads |
### 👥 Who uses this
#### 🚀 Bootstrapped SaaS founders
You want customers from Reddit without spamming. Point it at your category and competitors, get back threads where someone is actively asking for what you sell.
```json
{
"productDescription": "A lightweight time-tracking app for freelancers.",
"keywords": ["time tracking app", "track freelance hours", "toggl alternative"],
"competitors": ["Toggl", "Harvest", "Clockify"],
"intentTypes": ["seeking_recommendation", "competitor_complaint", "switching_intent"],
"timeFilter": "month",
"maxLeads": 50
}
````
#### 📣 Growth marketers & agencies
Run social-selling and competitor-conquesting campaigns. Filter to `competitor_complaint` + `switching_intent` to catch people mid-churn from a rival.
```json
{
"productDescription": "A CRM built for small real-estate teams.",
"keywords": ["real estate CRM", "CRM for realtors"],
"competitors": ["Follow Up Boss", "kvCORE"],
"intentTypes": ["competitor_complaint", "switching_intent"],
"minIntentScore": 65
}
```
#### 🔎 Market researchers
Surface unmet needs and feature requests in your space with `feature_request` + `problem_unaware`, scored so you can rank pain by frequency.
### 🧠 How it works
1. **Scans Reddit** for your keywords and competitor names (public data, residential proxies, no login).
2. **Scores every post** through a three-tier pipeline (details below).
3. **Delivers only qualified leads**, each with an intent type, a 0 to 100 score, the reasons it scored, and a suggested reply.
#### Three-tier scoring (precision without a key)
| Tier | What it does | Cost |
|------|--------------|------|
| **1. Lexicon** | Fast keyword and intent-cue match. Kills obvious noise. | Free, on by default |
| **2. Semantic gate** | Local embeddings check that a post is actually about your product, not just sharing a word. Drops "time tracking" in r/Porsche (track days) even though the phrase matches. | Free, on by default, no key |
| **3. LLM (optional)** | Set `scoringMode: llm` with your own API key for the sharpest read on ambiguous posts. Falls back to lexicon on any error. | Your key, only runs on posts that pass tiers 1 and 2 |
Tiers 1 and 2 need no API key and run on every post, so you get semantic precision out of the box. The LLM tier is an optional top layer, and because it only sees posts that already passed the cheaper tiers, it stays cheap.
### 📤 Sample output
Each dataset item is one qualified lead:
```json
{
"leadId": "t3_1abc234",
"type": "post",
"intentType": "competitor_complaint",
"intentScore": 82,
"scoreReasons": [
"matched intent cue: 'fed up with'",
"mentions competitor(s): Toggl",
"first-person (author is likely the buyer)"
],
"matchedKeywords": ["time tracking", "Toggl"],
"title": "Fed up with Toggl's new pricing, what are people using instead?",
"snippet": "Been on Toggl 3 years but the per-seat jump is brutal. Freelancer, just need start/stop + CSV export...",
"url": "https://www.reddit.com/r/freelance/comments/1abc234/x/",
"subreddit": "freelance",
"author": "designerjane",
"score": 47,
"numComments": 23,
"createdAt": "2026-07-11T14:02:00+00:00",
"ageHours": 189.0,
"suggestedAction": "Reply empathizing with the pain point, then softly mention your product as an option. They mentioned Toggl; contrast on the point they complained about.",
"sentiment": "negative",
"isAnswered": false
}
```
### 🏷️ Intent types
| Intent | Catches | Example |
|--------|---------|---------|
| `seeking_recommendation` | People directly asking for a tool | "Any recommendations for a time tracker?" |
| `competitor_complaint` | People unhappy with a rival | "Toggl is too expensive now" |
| `switching_intent` | People actively leaving a tool | "Moving off Harvest, need a replacement" |
| `feature_request` | People describing a missing capability | "Wish there was a tool that could..." |
| `problem_unaware` | People with the problem, no solution named | "Spending hours reconciling invoices by hand" |
### 💸 Pricing (plain and honest)
Two line items. That is the whole bill.
- **$0.06 per search run**: charged once, only when a scan actually returns posts.
- **$0.02 per qualified lead**: charged only for leads delivered to your dataset.
**You are never charged when we deliver nothing.** Blocked run or zero matches = **$0.00**. Posts that score below your threshold are free. Set `ACTOR_MAX_TOTAL_CHARGE_USD` to hard-cap any run. Full detail: [docs/PRICING.md](docs/PRICING.md).
Example: 12 leads = `$0.06 + 12 × $0.02 = $0.30`.
### 📥 Bulk input
Driving from a spreadsheet or a prior run? Skip the keyword box:
- `keywordsDatasetId`: an Apify dataset of keyword strings or `{ "keyword": "..." }` objects.
- `keywordsFileUrl`: a public `.csv` (column `keyword`) or newline-delimited `.txt`.
All sources merge with the inline list and de-duplicate automatically.
### ⚙️ Input reference
| Field | Type | Default | Notes |
|-------|------|---------|-------|
| `productDescription` | string | - | **Required.** What you sell. Powers intent matching. |
| `keywords` | array | - | Category terms + competitor names. Optional if using bulk input. |
| `competitors` | array | `[]` | Names that boost complaint/switching scores. |
| `subreddits` | array | `[]` | Empty = all of Reddit. One value restricts search. |
| `intentTypes` | array | 3 core types | Which signals to surface. |
| `minIntentScore` | int | `55` | Below this: not returned, not charged. |
| `timeFilter` | enum | `month` | day / week / month / year / all. |
| `maxLeads` | int | `50` | Cap on delivered leads (1-500). |
| `scoringMode` | enum | `lexicon` | `lexicon` (free: tiers 1+2) or `llm` (adds tier 3, your key). |
| `openaiApiKey` | secret | - | Only for `scoringMode: llm`. Never stored. |
| `keywordsDatasetId` / `keywordsFileUrl` | string | - | Bulk keyword sources. |
| `proxyConfiguration` | object | Residential | Reddit blocks datacenter IPs. |
### 🤖 Built for AI agents (MCP + x402 payments)
This actor is agent-native:
- **MCP ready.** Callable as a tool by Claude, ChatGPT, and Cursor through the Apify MCP server, no extra setup. Your agent asks for Reddit leads in plain language and gets back scored, structured results.
- **x402 agentic payments.** Because this actor uses pay-per-event pricing, an AI agent can **run and pay for it autonomously in USDC on Base**, with no Apify account and no API key, paying per use over HTTP via the open x402 protocol. Perfect for autonomous lead-gen agents that find and act on prospects without a human in the loop.
### 🔌 Integrations
Output is a standard Apify dataset: export to **JSON, CSV, Excel**, pipe into **n8n, Zapier, Make**, or wire into your own stack via the Apify API. Schedule daily runs to catch leads while they are still fresh.
### ❓ FAQ
**Do I need a Reddit account or API key?** No. Public data only.
**Why residential proxies?** Reddit hard-blocks datacenter IPs. Residential is required for reliable results and is included in the price above.
**Is this against Reddit's terms?** It reads only public pages, takes no in-Reddit actions (no posting, voting, or DMing), and paces requests politely. Use the leads to engage authentically, not to spam.
**How accurate are the leads?** Default scoring runs two tiers with no key: a lexicon filter plus a local semantic gate that checks a post is genuinely about your product, not just sharing a keyword. This catches most false positives (like "time tracking" in a car forum). For the sharpest read on ambiguous posts, set `scoringMode: llm` with your own key to add a third LLM tier, which only runs on posts that already passed the first two.
**Can an AI agent run and pay for this by itself?** Yes. It supports x402, so an agent can pay per run in USDC on Base with no Apify account or API key. It is also exposed as an MCP tool for Claude, ChatGPT, and Cursor.
**What if a run finds nothing?** You pay nothing. No leads, no charge.
***
Made by mick\_. Companion to [Reddit Scraper](https://apify.com/labrat011/reddit-scraper), use that for raw data at scale, this for ready-to-act leads.
# Actor input Schema
## `productDescription` (type: `string`):
Used to match buying intent. Example: 'A lightweight time-tracking app for freelance designers.'
## `keywords` (type: `array`):
Search terms to seed discovery. Include your product category plus competitor names. Optional if you supply a keywords dataset or file below.
## `keywordsDatasetId` (type: `string`):
Dataset whose items are keyword strings or objects with a 'keyword' field. Merged with the list above.
## `keywordsFileUrl` (type: `string`):
Public link to a .csv (column 'keyword') or newline-delimited .txt. Merged with the list above.
## `competitors` (type: `array`):
Posts mentioning these plus complaint or switching language score higher.
## `subreddits` (type: `array`):
Leave empty to search all of Reddit.
## `intentTypes` (type: `array`):
Which kinds of buying signal to return.
## `minIntentScore` (type: `integer`):
Leads below this score are not returned and never charged.
## `timeFilter` (type: `string`):
How far back to search. Fresher leads are easier to act on.
## `maxLeads` (type: `integer`):
Stop after this many qualified leads are delivered (1-500).
## `scoringMode` (type: `string`):
lexicon is deterministic and needs no API key. llm is sharper but needs your own LLM API key.
## `openaiApiKey` (type: `string`):
Your own key. Used only for scoring. Never stored.
## `proxyConfiguration` (type: `object`):
Passed through to the underlying Reddit scraper. Residential recommended.
## Actor input object example
```json
{
"productDescription": "A lightweight time-tracking app for freelancers.",
"keywords": [
"time tracking app",
"toggl alternative",
"track freelance hours"
],
"intentTypes": [
"seeking_recommendation",
"competitor_complaint",
"switching_intent"
],
"minIntentScore": 55,
"timeFilter": "month",
"maxLeads": 50,
"scoringMode": "lexicon",
"proxyConfiguration": {
"useApifyProxy": true,
"apifyProxyGroups": [
"RESIDENTIAL"
]
}
}
```
# Actor output Schema
## `leads` (type: `string`):
Dataset containing all qualified leads
# API
You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.
## JavaScript example
```javascript
import { ApifyClient } from 'apify-client';
// Initialize the ApifyClient with your Apify API token
// Replace the '' with your token
const client = new ApifyClient({
token: '',
});
// Prepare Actor input
const input = {
"productDescription": "A lightweight time-tracking app for freelancers.",
"keywords": [
"time tracking app",
"toggl alternative",
"track freelance hours"
]
};
// Run the Actor and wait for it to finish
const run = await client.actor("labrat011/reddit-lead-finder").call(input);
// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
console.dir(item);
});
// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs
```
## Python example
```python
from apify_client import ApifyClient
# Initialize the ApifyClient with your Apify API token
# Replace '' with your token.
client = ApifyClient("")
# Prepare the Actor input
run_input = {
"productDescription": "A lightweight time-tracking app for freelancers.",
"keywords": [
"time tracking app",
"toggl alternative",
"track freelance hours",
],
}
# Run the Actor and wait for it to finish
run = client.actor("labrat011/reddit-lead-finder").call(run_input=run_input)
# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item)
# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start
```
## CLI example
```bash
echo '{
"productDescription": "A lightweight time-tracking app for freelancers.",
"keywords": [
"time tracking app",
"toggl alternative",
"track freelance hours"
]
}' |
apify call labrat011/reddit-lead-finder --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=labrat011/reddit-lead-finder",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "Reddit Lead Finder: Find Buying Intent Leads",
"description": "Find Reddit posts with buying intent: people asking for a tool like yours, unhappy with a competitor, or ready to switch. Delivered scored, ranked, and with a suggested reply on each. No API key, no login.",
"version": "0.1",
"x-build-id": "TcAidHhpJayIcRDd1"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/labrat011~reddit-lead-finder/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-labrat011-reddit-lead-finder",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/labrat011~reddit-lead-finder/runs": {
"post": {
"operationId": "runs-sync-labrat011-reddit-lead-finder",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/labrat011~reddit-lead-finder/run-sync": {
"post": {
"operationId": "run-sync-labrat011-reddit-lead-finder",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
}
},
"components": {
"schemas": {
"inputSchema": {
"type": "object",
"required": [
"productDescription",
"keywords"
],
"properties": {
"productDescription": {
"title": "What you sell (1-2 sentences)",
"type": "string",
"description": "Used to match buying intent. Example: 'A lightweight time-tracking app for freelance designers.'"
},
"keywords": {
"title": "Seed keywords / phrases",
"type": "array",
"description": "Search terms to seed discovery. Include your product category plus competitor names. Optional if you supply a keywords dataset or file below.",
"items": {
"type": "string"
}
},
"keywordsDatasetId": {
"title": "Bulk keywords: Apify dataset ID (optional)",
"type": "string",
"description": "Dataset whose items are keyword strings or objects with a 'keyword' field. Merged with the list above."
},
"keywordsFileUrl": {
"title": "Bulk keywords: CSV or TXT URL (optional)",
"type": "string",
"description": "Public link to a .csv (column 'keyword') or newline-delimited .txt. Merged with the list above."
},
"competitors": {
"title": "Competitor names (optional)",
"type": "array",
"description": "Posts mentioning these plus complaint or switching language score higher.",
"items": {
"type": "string"
}
},
"subreddits": {
"title": "Restrict to subreddits (optional)",
"type": "array",
"description": "Leave empty to search all of Reddit.",
"items": {
"type": "string"
}
},
"intentTypes": {
"title": "Intent types to surface",
"type": "array",
"description": "Which kinds of buying signal to return.",
"items": {
"type": "string",
"enum": [
"seeking_recommendation",
"competitor_complaint",
"switching_intent",
"problem_unaware",
"feature_request"
],
"enumTitles": [
"Seeking a recommendation",
"Complaining about a competitor",
"Wants to switch tools",
"Has the problem, unaware of solutions",
"Requesting a feature"
]
},
"default": [
"seeking_recommendation",
"competitor_complaint",
"switching_intent"
]
},
"minIntentScore": {
"title": "Minimum intent score (0-100)",
"minimum": 0,
"maximum": 100,
"type": "integer",
"description": "Leads below this score are not returned and never charged.",
"default": 55
},
"timeFilter": {
"title": "Recency",
"enum": [
"day",
"week",
"month",
"year",
"all"
],
"type": "string",
"description": "How far back to search. Fresher leads are easier to act on.",
"default": "month"
},
"maxLeads": {
"title": "Max qualified leads",
"minimum": 1,
"maximum": 500,
"type": "integer",
"description": "Stop after this many qualified leads are delivered (1-500).",
"default": 50
},
"scoringMode": {
"title": "Scoring engine",
"enum": [
"lexicon",
"llm"
],
"type": "string",
"description": "lexicon is deterministic and needs no API key. llm is sharper but needs your own LLM API key.",
"default": "lexicon"
},
"openaiApiKey": {
"title": "LLM API key (only if scoring engine = llm)",
"type": "string",
"description": "Your own key. Used only for scoring. Never stored."
},
"proxyConfiguration": {
"title": "Proxy",
"type": "object",
"description": "Passed through to the underlying Reddit scraper. Residential recommended.",
"default": {
"useApifyProxy": true,
"apifyProxyGroups": [
"RESIDENTIAL"
]
}
}
}
},
"runsResponseSchema": {
"type": "object",
"properties": {
"data": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"actId": {
"type": "string"
},
"userId": {
"type": "string"
},
"startedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"finishedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"status": {
"type": "string",
"example": "READY"
},
"meta": {
"type": "object",
"properties": {
"origin": {
"type": "string",
"example": "API"
},
"userAgent": {
"type": "string"
}
}
},
"stats": {
"type": "object",
"properties": {
"inputBodyLen": {
"type": "integer",
"example": 2000
},
"rebootCount": {
"type": "integer",
"example": 0
},
"restartCount": {
"type": "integer",
"example": 0
},
"resurrectCount": {
"type": "integer",
"example": 0
},
"computeUnits": {
"type": "integer",
"example": 0
}
}
},
"options": {
"type": "object",
"properties": {
"build": {
"type": "string",
"example": "latest"
},
"timeoutSecs": {
"type": "integer",
"example": 300
},
"memoryMbytes": {
"type": "integer",
"example": 1024
},
"diskMbytes": {
"type": "integer",
"example": 2048
}
}
},
"buildId": {
"type": "string"
},
"defaultKeyValueStoreId": {
"type": "string"
},
"defaultDatasetId": {
"type": "string"
},
"defaultRequestQueueId": {
"type": "string"
},
"buildNumber": {
"type": "string",
"example": "1.0.0"
},
"containerUrl": {
"type": "string"
},
"usage": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "integer",
"example": 1
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
},
"usageTotalUsd": {
"type": "number",
"example": 0.00005
},
"usageUsd": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "number",
"example": 0.00005
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
}
}
}
}
}
}
}
}
```