Proxycurl alternatives for LinkedIn profile, company, and job data

Proxycurl was a widely used API for LinkedIn-style profile data, company data, and job data across B2B SaaS, sales, and recruiting workflows. LinkedIn sued Proxycurl over scraping and fake accounts, the API shut down, and teams that had built on it lost their main data source. A replacement has to cover the same three data types, and it has to fit your requirements for coverage, freshness, pricing, and compliance with platform terms and privacy regulation such as GDPR and CCPA.

This comparison is written by the team behind EnvoAPI, which is one of the options below. The provider assessments are our reading of how each product is positioned rather than a measured benchmark — verify coverage against your own record set before you commit.

The alternatives at a glance

All of them return data comparable to what Proxycurl provided, but they differ in how the data is sourced, how the vendor handles compliance, and how the bill behaves as volume grows. The split that matters most is shape: a unified enrichment API, a bulk dataset, a scraping platform, or a workflow runner.

How to evaluate a replacement

Evaluate technical fit and legal fit together. Five criteria are usually enough to separate the candidates, and each one should be written as a number you can test against a sample of your own records.

• Profile coverage. Set a floor — for example, 90% of your sample resolving with name, headline, location, and current role. That is what keeps lead scoring and routing logic intact after the swap.
• Company coverage. Check depth in your target markets, with structured company name, industry, location, headcount, and revenue band, so account-based targeting and territory planning still work.
• Job freshness. Decide the staleness you can tolerate and test it: a role posted today should appear within your window, or alerting and outreach built on it will misfire.
• Integration effort. Budget days, not weeks, and require versioned endpoints, sample requests, and an OpenAPI spec you can generate a typed client from.
• Pricing at your volume. Model 10,000, 100,000, and 1,000,000 calls per month, including monthly minimums and overage, rather than comparing entry prices.

The alternatives in depth

EnvoAPI — unified profile, company, and job API

EnvoAPI is an unofficial public professional data API that exposes profile, company, and job data through one interface: 47 documented endpoints across profiles, companies, search, jobs, and posts, one API key in one header, and consistent JSON envelopes with versioned paths. The published figures are an average response time under 1.8 seconds and 99.95% uptime over the last 90 days. There is also an MCP server over the same key and credits, so AI clients can call the same endpoints in plain language, and any automation platform with a generic HTTP step — n8n, Make, Zapier — can call it without a bespoke connector.

What it is not: EnvoAPI is not an official LinkedIn partner, and it does not ship bulk flat-file datasets for offline download. If you want a raw data lake of your own, or scraping across hundreds of unrelated sites, a dataset-first or scraper-first product gives you more low-level control.

• Best for teams that want one API for profile, company, and job enrichment with predictable per-credit pricing.
• Best for SaaS products, internal tools, and AI workflows that want structured responses rather than running scrapers or browser automation.

Coresignal — workforce and job-market datasets

Coresignal licenses public web data about people and companies, with strong workforce and job-market coverage. It suits work that needs millions of records for BI, HR analytics, and labour-market research rather than per-record lookups, and it arrives in normalized tables that load cleanly into a warehouse.

The trade-off is latency. A dataset is a snapshot, not a live endpoint, so it does not replace a real-time lookup inside a product surface, and profile records describe career structure more than current activity. Licences are typically annual contracts priced by tables, regions, and record counts.

• Best for offline workforce analytics, trend analysis, and macro-level reporting.
• Best for data teams that would rather query a table they own than integrate another live API.

People Data Labs — person and company enrichment

People Data Labs pairs large person and company graphs with both API and bulk enrichment. Its strength is identity resolution: matching work email addresses, domains, and company names onto consistent person and company entities across CRM, CDP, and marketing systems.

Job listings are not a first-class surface in the way person and company attributes are, so a jobs-heavy workflow needs a second source. The dataset-plus-API model also adds a schema mapping step against your existing platform. Pricing generally follows enriched records or API volume.

• Best for high-volume CRM and CDP enrichment where person and company precision matters more than listings.
• Best for RevOps and marketing teams that need reliable identity resolution across existing records.

Bright Data — scraper API and web data infrastructure

Bright Data sells scraping infrastructure: a large proxy network, real-time scraper APIs, and prepared datasets. It has also defended the model in court against Meta and X, where rulings supported the position that collecting publicly visible, non-authenticated data can be lawful when it does not bypass technical controls.

It asks for more engineering than a unified enrichment API. You need people comfortable with proxies, browsers, parsing, and governance, and you own the storage, transformation, and retention decisions that come with the raw output. Pricing is usage-based, mixing per-request or per-record fees with plan minimums, and complex projects climb quickly.

• Best for teams that treat web scraping as a core competency and want control over targets and configuration.
• Best for platforms aggregating many sites, rather than a like-for-like Proxycurl replacement.

LinkdAPI — LinkedIn-shaped endpoints

LinkdAPI concentrates on LinkedIn-specific calls: profile URL to JSON, company profiles, and some job search. The endpoint model sits close to Proxycurl's, which makes it the least disruptive swap if your pipeline already stores profile URLs and company URLs as its primary identifiers.

The narrow focus cuts both ways: coverage can be shallower than dataset-first products, and there is no second source when a LinkedIn-shaped identifier fails to resolve. As with any provider touching member profiles, check how the data is sourced before you build on it. Pricing follows call-tier bands.

• Best for developers who want to change a base URL and field names rather than redesign a pipeline.
• Best for workflows keyed on LinkedIn URLs that need few non-LinkedIn sources.

Apify — actors and custom automations

Apify offers a library of LinkedIn actors for profiles, search, and jobs, orchestrated with queues, schedulers, and webhooks. PhantomBuster occupies similar ground with low-code automation recipes. Both are strongest when the job is a multi-step flow — crawl, filter, deduplicate, export — rather than a single lookup.

Neither behaves like a plain REST API. You work with actors, run limits, and output formats, and you usually add a normalization step before results are usable in a CRM or ATS. Pricing is usage-based by compute, runs, or credits.

• Best for growth and ops teams already running scheduled, queue-based workflows.
• Best where scraping, enrichment, and delivery belong in one automation rather than one API call.

Choosing by shape, not by vendor

Most of the decision is settled before you compare vendors, by deciding which of four shapes the workload wants.

• Unified API when a product surface needs a structured answer per request, and you would rather not own collection, parsing, or storage.
• Dataset when the priority is offline analysis over millions of records per year and freshness is measured in weeks, not minutes.
• Scraper platform when you already operate storage, transformation, and a compliance layer, and you need many sites rather than one.
• Workflow runner when the value is in the orchestration — schedules, queues, retries, delivery — more than in the lookup itself.

The shapes combine. A common architecture licenses a dataset for the historical baseline and calls a unified API for the records that have to be current at read time.

Feature and pricing comparison

Competitor pricing is described by shape rather than by figure on purpose: published prices move, and several of these vendors quote per workload. Model your own volume against each vendor's current pricing page before deciding.

Full plan limits, annual pricing, and credit top-ups are on the pricing section of the homepage.

Migrating a Proxycurl integration to EnvoAPI

The endpoint shapes line up closely enough that most of the work is a field mapping rather than a redesign.

A five-step cutover keeps the risk contained:

• Audit every current call — endpoint, monthly volume, and the fields you actually read downstream.
• Create separate keys for staging and production.
• Put an adapter layer between your code and the provider, so the base URL, authentication header, and field names are changed in one place.
• Verify coverage before cutover: run both providers in parallel if the old one is still live, or re-enrich a sample of records you already stored and diff them field by field. Proxycurl itself stopped serving traffic in July 2025, so a stored baseline is the only comparison left there.
• Move all traffic across, then update your documentation, dashboards, and alerts.

The API documentation covers authentication, rate limits, errors, and pagination end to end, and the free plan includes 100 credits so you can validate coverage against your own records before moving any production traffic.

FAQs about replacing Proxycurl