# Levels.fyi Salary Scraper — Real Tech Comp Data | No Login (`bovi/levels-fyi-scraper`) Actor

Scrape real salary submissions from Levels.fyi by company and role. Returns individual compensation records with base salary, total comp, stock, bonus, level, location, and experience. No proxy, no auth. Pay per result.

- **URL**: https://apify.com/bovi/levels-fyi-scraper.md
- **Developed by:** [Vitalii Bondarev](https://apify.com/bovi) (community)
- **Categories:** Jobs, Business, AI
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $3.49 / 1,000 levels.fyi salary scraper — real tech comp data | no logins

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — 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

## Levels.fyi Salary Scraper — Real Tech Comp Data | from $3/1K | No Login

**For recruiters benchmarking offers, HR teams building comp bands, job-seekers negotiating, and AI models that need real salary training data** — Levels.fyi has the most transparent individual compensation records in tech.

**$3.00 per 1,000 salary records.** These are real individual submissions from Levels.fyi — not estimates, not averages. Each row is a unique compensation package with base, stock, bonus, level, and location. Real submissions, not estimates. Individual records — not just averages. No login required. FAANG + 100+ companies.

**Pricing example:** 200 Google SWE records = **$0.60**. 3 companies × 2 roles × ~100 records = 600 records = **$1.80**. Full FAANG sweep (5 companies × 3 roles × ~200 records = 3,000 records) = **$9.00**.

**Use case: salary negotiation** — Pull 200 Google L5 SWE records in SF to benchmark your competing offer. Job-seekers are the largest audience for this data.

| Source | Price | Individual records? | Level codes? | Stock/bonus split? |
|---|---|---|---|---|
| **This actor** | $3/1k | Yes | Yes (L3/E4/IC3) | Yes |
| Glassdoor salary scraper | $2-5/1k | Estimated only | No | No |
| Payscale data | Enterprise | Aggregate only | No | No |
| LinkedIn salary insights | Enterprise | Aggregate only | No | No |

Scrape real tech compensation data from [Levels.fyi](https://www.levels.fyi/) by company and role.
Returns individual salary submissions with full compensation breakdown per level.

### Features

- **Zero cost to run** — no proxy, no auth, no API key required
- **Individual salary records** — not just averages; each submission is a separate row
- **Full compensation breakdown** — base salary, total comp, stock (annualized), bonus
- **Multi-company, multi-role** — scrape any combination in a single run
- **Level-aware** — data grouped by company level (L3/E4/IC3/etc.) with level codes normalized to uppercase
- **parse\_confidence** — every record carries a data quality score (1.0 = all fields present)
- **Batch queries** — list of companies × list of roles, all in one run

### Input

```json
{
  "companies": ["google", "meta", "openai", "amazon", "microsoft"],
  "roles": ["software-engineer", "data-scientist"],
  "maxItems": 500
}
```

| Field | Type | Default | Description |
|---|---|---|---|
| `companies` | `string[]` | `[]` | Company slugs from Levels.fyi URLs (lowercase, hyphenated) |
| `roles` | `string[]` | `["software-engineer"]` | Role/job-family slugs |
| `searchQueries` | `string[]` | `[]` | Company name search (used if `companies` is empty) |
| `maxItems` | `integer` | `200` | Max total records (0 = unlimited) |
| `includeAllLevels` | `boolean` | `true` | Include all levels or just page defaults |
| `minSamplesPerLevel` | `integer` | `1` | Skip levels with fewer samples |

**Common company slugs:** `google`, `meta`, `amazon`, `microsoft`, `apple`, `netflix`, `openai`, `stripe`, `airbnb`, `uber`, `lyft`, `linkedin`, `salesforce`, `nvidia`, `bytedance`

**Common role slugs:** `software-engineer`, `data-scientist`, `product-manager`, `software-engineering-manager`, `product-designer`, `data-engineer`, `machine-learning-engineer`

### Output schema

Each result row is a salary submission with these fields:

| Field | Type | Description |
|---|---|---|
| `company` | string | Company display name (e.g. "Google") |
| `title` | string | Specific job title (e.g. "Backend Software Engineer") |
| `role` | string | Job family (e.g. "Software Engineer") |
| `level` | string | Company level code, normalized (e.g. "L3", "E4", "IC3") |
| `location` | string | City, State (e.g. "San Francisco, CA") |
| `total_comp` | integer | Total annual compensation (USD) |
| `base_salary` | integer | Annual base salary (USD) |
| `stock` | integer | Annualized stock grant value (USD) |
| `bonus` | integer | Annual bonus (USD) |
| `years_experience` | number | Total years of professional experience |
| `years_at_company` | number | Years at this company |
| `date` | string | Submission/offer date (ISO 8601 YYYY-MM-DD) |
| `uuid` | string | Levels.fyi submission UUID |
| `source_url` | string | Page URL this record was scraped from |
| `scraped_at` | string | ISO 8601 UTC scrape timestamp |
| `parse_confidence` | float | Data quality score 0.0–1.0 (1.0 = all fields present) |
| `warnings` | string\[] | List of quality warning codes |

### Example output

```json
{
  "company": "Google",
  "title": "Software Engineer",
  "role": "Software Engineer",
  "level": "L3",
  "location": "San Francisco, CA",
  "total_comp": 210000,
  "base_salary": 160000,
  "stock": 40000,
  "bonus": 10000,
  "years_experience": 3,
  "years_at_company": 1,
  "date": "2026-05-23",
  "uuid": "2218e4a5-18f0-404a-aa6f-805b4508f944",
  "source_url": "https://www.levels.fyi/companies/google/salaries/software-engineer",
  "scraped_at": "2026-05-31T10:00:00Z",
  "parse_confidence": 1.0,
  "warnings": []
}
```

### Use cases

- **Salary negotiation research** — benchmark your offer against real submissions
- **Compensation benchmarking** — compare packages across FAANG and Big Tech
- **HR analytics** — market salary data by role, level, and location
- **Job market research** — track comp trends across companies and roles
- **Recruiting tools** — power competitive compensation analysis dashboards

### Pricing

Pay per result (PPE). **$3.00 per 1,000 salary records.** Each salary record = 1 charge event (`salary-item`).

### Technical notes

- Data source: Levels.fyi's Next.js SSR `__NEXT_DATA__` JSON blob (stable, structured)
- No scraping proxy needed — data is server-side rendered and publicly accessible
- Each page provides ~10–30 individual samples per level; `count` field shows the full DB size
- Rate limiting: no observed limits on standard page requests; actor uses conservative single-page fetches
- Not affiliated with Levels.fyi

### parse\_confidence trust score

Every record carries a `parse_confidence` score (0.0–1.0). Score 1.0 means all fields are present. The `warnings` list tells you which specific fields were missing for programmatic filtering.

### Use with AI agents (MCP)

AI salary research via MCP — ask your AI agent "what does a Google L5 engineer make in NYC?" and get real Levels.fyi submissions back, structured.

Config: https://mcp.apify.com/?tools=bovi/levels-fyi-scraper

### Integrations

Built for recruiters benchmarking offers, HR teams building comp bands, and job-seekers negotiating tech pay by level and location — the JSON/dataset output drops into the tools you already run, no glue code:

- **n8n / Make / Zapier** — trigger a run or pipe every new dataset item into 500+ apps (Google Sheets, Airtable, Slack, HubSpot, your database) with no code: [n8n](https://docs.apify.com/platform/integrations/n8n), [Make](https://docs.apify.com/platform/integrations/make), [Zapier](https://docs.apify.com/platform/integrations/zapier).
- **Webhooks** — fire your own endpoint the moment a run finishes, to push results straight into your pipeline ([docs](https://docs.apify.com/platform/integrations/webhooks)).
- **MCP server** — expose this actor as a tool to Claude, Cursor, or any [MCP client](https://mcp.apify.com) so an AI agent can pull this data mid-conversation ([guide](https://blog.apify.com/how-to-use-mcp/)).
- **API & SDKs** — fetch the dataset as JSON, CSV, or Excel through the Apify REST API or the Python / JS SDKs.

See all [Apify integrations](https://apify.com/integrations).

### Legal

Levels.fyi salary data is publicly accessible (no login required). This actor fetches only the same data visible to any web browser on the publicly accessible pages. Users are responsible for compliance with Levels.fyi's Terms of Service and applicable laws in their jurisdiction.

# Actor input Schema

## `companies` (type: `array`):

Company slugs to scrape. Use the slug from Levels.fyi URLs, e.g. 'google', 'meta', 'amazon', 'microsoft', 'apple', 'netflix', 'openai'. Lowercase, hyphenated.

## `roles` (type: `array`):

Role slugs to scrape per company. Use the slug from Levels.fyi URLs, e.g. 'software-engineer', 'product-manager', 'data-scientist', 'software-engineering-manager'. Defaults to 'software-engineer' if empty.

## `searchQueries` (type: `array`):

Alternative to 'companies': free-text company names resolved to Levels.fyi slugs automatically. Example: 'Open AI' resolves to 'openai'. Ignored if 'companies' is already set. Use 'companies' when you know the exact slug.

## `maxItems` (type: `integer`):

Maximum total salary records to return across all company/role pairs. 0 = no limit (scrapes all available samples per level). Default 200.

## `includeAllLevels` (type: `boolean`):

If true, scrape salary samples from every available level (L3–L10 or equivalent). If false, only the levels returned by default on the page. Default true.

## `minSamplesPerLevel` (type: `integer`):

Skip a level if it has fewer than this many samples (too sparse for reliable data). Default 1 (include all). Set to 5 to skip very sparse levels.

## Actor input object example

```json
{
  "companies": [
    "google",
    "meta",
    "amazon"
  ],
  "roles": [
    "software-engineer"
  ],
  "maxItems": 200,
  "includeAllLevels": true,
  "minSamplesPerLevel": 1
}
```

# Actor output Schema

## `results` (type: `string`):

Dataset containing Levels Fyi Scraper records (company, title, level, location, total\_comp, base\_salary, stock, bonus, years\_experience, years\_at\_company, date, parse\_confidence).

# 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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "companies": [
        "google",
        "meta",
        "amazon"
    ],
    "roles": [
        "software-engineer"
    ],
    "maxItems": 200,
    "includeAllLevels": true,
    "minSamplesPerLevel": 1
};

// Run the Actor and wait for it to finish
const run = await client.actor("bovi/levels-fyi-scraper").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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "companies": [
        "google",
        "meta",
        "amazon",
    ],
    "roles": ["software-engineer"],
    "maxItems": 200,
    "includeAllLevels": True,
    "minSamplesPerLevel": 1,
}

# Run the Actor and wait for it to finish
run = client.actor("bovi/levels-fyi-scraper").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "companies": [
    "google",
    "meta",
    "amazon"
  ],
  "roles": [
    "software-engineer"
  ],
  "maxItems": 200,
  "includeAllLevels": true,
  "minSamplesPerLevel": 1
}' |
apify call bovi/levels-fyi-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,bovi/levels-fyi-scraper"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/F84Xglwr6qmsGaSG4/builds/01zc3je8tdEOd0xTP/openapi.json