Indeed Salary Analyzer
Pricing
Pay per event
Go to Apify Store
Indeed Salary Analyzer
Comprehensive salary intelligence from Indeed - Extract and analyze compensation data for benchmarking, market research, and HR analytics.
Pricing
Pay per event
Comprehensive salary intelligence from Indeed - Extract and analyze compensation data for benchmarking, market research, and HR analytics.
You can access the Indeed Salary Analyzer programmatically from your own applications by using the Apify API. You can also choose the language preference from below. To use the Apify API, you’ll need an Apify account and your API token, found in Integrations settings in Apify Console.
{ "openapi": "3.0.1", "info": { "version": "1.0", "x-build-id": "R0i5BTlz2n8xohQJm" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/barrierefix~indeed-salary-analyzer/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-barrierefix-indeed-salary-analyzer", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/barrierefix~indeed-salary-analyzer/runs": { "post": { "operationId": "runs-sync-barrierefix-indeed-salary-analyzer", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/barrierefix~indeed-salary-analyzer/run-sync": { "post": { "operationId": "run-sync-barrierefix-indeed-salary-analyzer", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "searchMode" ], "properties": { "searchMode": { "title": "Search Mode", "enum": [ "job_titles", "companies", "locations", "keywords" ], "type": "string", "description": "How to search for salary data", "default": "job_titles" }, "jobTitles": { "title": "Job Titles", "type": "array", "description": "List of job titles to analyze (e.g., 'Software Engineer', 'Data Scientist')", "default": [ "Software Engineer" ], "items": { "type": "string" } }, "companies": { "title": "Companies", "type": "array", "description": "List of companies to analyze (e.g., 'Google', 'Amazon')", "default": [], "items": { "type": "string" } }, "locations": { "title": "Locations", "type": "array", "description": "List of locations to analyze (e.g., 'San Francisco, CA', 'New York, NY', 'Remote')", "default": [ "San Francisco, CA" ], "items": { "type": "string" } }, "keywords": { "title": "Keywords", "type": "array", "description": "Search by keywords (e.g., 'machine learning', 'frontend')", "default": [], "items": { "type": "string" } }, "experienceLevels": { "title": "Experience Levels", "type": "array", "description": "Filter by experience level", "items": { "type": "string", "enum": [ "all", "entry", "mid", "senior", "lead" ], "enumTitles": [ "All Levels", "Entry Level", "Mid Level", "Senior Level", "Lead/Principal" ] }, "default": [ "all" ] }, "industryFilter": { "title": "Industry Filter", "type": "array", "description": "Filter by industries (optional)", "default": [], "items": { "type": "string" } }, "maxResults": { "title": "Maximum Results", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Maximum number of salary records to scrape", "default": 10 }, "includeSalaryRange": { "title": "Include Salary Range", "type": "boolean", "description": "Include min/max salary ranges in output", "default": true }, "includeHistoricalData": { "title": "Include Historical Data", "type": "boolean", "description": "Track changes across runs for trend analysis", "default": true }, "useResidentialProxies": { "title": "Use Residential Proxies", "type": "boolean", "description": "Required for Indeed to bypass anti-bot protection. Costs extra but necessary.", "default": true }, "requestDelay": { "title": "Request Delay (ms)", "minimum": 0, "maximum": 10000, "type": "integer", "description": "Delay between requests in milliseconds (higher = safer from blocks)", "default": 2000 }, "maxConcurrency": { "title": "Max Concurrency", "minimum": 1, "maximum": 10, "type": "integer", "description": "Maximum concurrent requests (lower = slower but safer)", "default": 3 }, "minSampleSize": { "title": "Minimum Sample Size", "minimum": 0, "maximum": 100, "type": "integer", "description": "Minimum number of salaries required for a record to be included (0 = allow all)", "default": 0 }, "onlyVerifiedSalaries": { "title": "Only Verified Salaries", "type": "boolean", "description": "Only include salaries with high confidence scores", "default": false } } }, "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 } } } } } } } } }}OpenAPI is a standard for designing and describing RESTful APIs, allowing developers to define API structure, endpoints, and data formats in a machine-readable way. It simplifies API development, integration, and documentation.
OpenAPI is effective when used with AI agents and GPTs by standardizing how these systems interact with various APIs, for reliable integrations and efficient communication.
By defining machine-readable API specifications, OpenAPI allows AI models like GPTs to understand and use varied data sources, improving accuracy. This accelerates development, reduces errors, and provides context-aware responses, making OpenAPI a core component for AI applications.
You can download the OpenAPI definitions for Indeed Salary Analyzer from the options below:
If you’d like to learn more about how OpenAPI powers GPTs, read our blog post.
You can also check out our other API clients: