7 Proxycurl Migration Pitfalls Teams Hit in 2026
Most Proxycurl migrations break in the same seven places. We've reviewed 60+ migration tickets across LinkFetch's first six months, plus public postmortems from teams that switched to Apify, Bright Data, Coresignal, or LinkdAPI. The pattern is remarkably consistent: the same field maps wrong, the same rate-limit cliff bites, the same geo-coverage gap turns a green dashboard red two weeks in.
This guide walks the seven, with the specific fix for each. If you're mid-migration and something is failing, scan the pitfall titles below and jump to yours.
Why this matters now
Proxycurl went dark on July 4, 2025 after LinkedIn filed suit six months earlier. The complaint alleged that Proxycurl operated hundreds of thousands of fake LinkedIn accounts to scrape millions of profiles, a model that LinkedIn argued violated both its terms and the Computer Fraud and Abuse Act. Proxycurl's founder posted a farewell note shortly after, ending the service.
The shutdown left thousands of production pipelines stranded. Most teams reached for the closest-looking replacement and ran a script to swap the base URL. Two weeks later, the dashboards started lying.
The root cause is almost always the same: Proxycurl was a specific shape, and replacement APIs are not the same shape. Field names differ, match rates vary by geography, rate limits behave differently under burst, and the legal posture of the replacement matters in ways that weren't a question when Proxycurl was still up.
LinkFetch is a compliance-first LinkedIn data API for AI agents, built on a passive-observer Chrome extension instead of fake accounts. We see these pitfalls in customer migrations and on Discord every week. The list below is in roughly the order teams hit them.
Pitfall 1: Assuming field names map one-to-one
The first migration script almost always assumes Proxycurl's
linkedin_profile_url is linkedin_profile_url everywhere, that
occupation is occupation, that country_full_name is what every
provider calls "country". None of that is true.
LinkFetch normalises Proxycurl's occupation field into a structured
current_position object with title, company, and started_at.
Apify's actor returns headline, which overlaps with Proxycurl's
occupation but isn't identical, since Proxycurl synthesised
occupation from the headline + the most recent role. Bright Data's
schema renames it current_role.title. Coresignal calls it position.
The fix isn't a regex find-and-replace. It's a deliberate field-mapping table maintained outside your codebase, with a thin adpater layer that converts the new provider's response into your internal schema. We publish the Proxycurl field mapping cheatsheet for the LinkFetch direction; for other providers, build the same table once and check it into the repo. The week you spend on the table saves the month you'd otherwise spend on data-quality bug reports.
Pitfall 2: Replacing one fake-account API with another
Several teams migrated off Proxycurl in mid-2025 by switching to a "compliant alternative" that turned out to use the exact same fake-account-and-residential-proxy model. Six months later, those providers are also under enforcement pressure, and the same teams are migrating again.
The legal posture isn't a vendor-marketing detail. The reason Proxycurl shut down is that LinkedIn won an injunction against the fake-account model itself, not against Proxycurl as a company. LinkedIn's legal win established a precedent that any provider using fake or stolen accounts to scrape profiles is on the wrong side of both the platform terms and US federal law.
The fix is to ask the candidate provider one question before you migrate: Does your service require any LinkedIn account, fake or real, to operate? If the answer is yes, the provider is exposed to the same fate as Proxycurl. If the answer is no, you've narrowed the field to session-based, user-principal, or warehouse-style providers. LinkFetch falls into the user-principal category: data is observed through the end user's own LinkedIn session, never through a service-owned account.
Pitfall 3: Underestimating the geo coverage gap
Proxycurl had famously high US match rates. In 2024 it returned populated profile data for around 92% of US Software Engineer URLs in LinkedIn's index. The DACH region, in contrast, sat closer to 60 to 70%. Many teams built around the US number and discovered the gap only after migrating to a provider with even worse non-US coverage.
We see this most in two cohorts: European recruiting platforms (DACH + Nordics + UK) and Asian outbound tools (India + SEA). Both have non-trivial coverage requirements outside the US, and both regularly discover post-migration that their new provider's effective match rate in their core geo is materially worse than what they had on Proxycurl.
The fix is a benchmark step before commitment. Pull a thousand known- good URLs from your current production set, weighted by the geo and seniority distribution you actually serve. Run them through the candidate provider on a one-week trial. Compare populated-field rate per region, not aggregate. The aggregate number hides the gap that will show up in your customer support queue.
Pitfall 4: Treating the migration as a code change instead of a contract change
Proxycurl shut down with around 90 days of notice, which most teams treat as long. It is not long if your migration also requires re-signing a DPA with a new sub-processor, getting a new vendor through your customer's procurement (especially if any of them are in regulated industries), and updating your privacy policy to name the new sub-processor.
Several teams we talked to in late 2025 had their replacement provider operationally working in week three but spent another six weeks getting it through enterprise procurement. During those six weeks, they ran both providers in parallel, doubling their cost.
The fix is to start the contract paperwork the day you sign the trial. Don't wait until the technical migration is done. The DPA review at most enterprises is a 4 to 6-week loop with general counsel; you want it running in parallel with engineering, not after.
Pitfall 5: Rate-limit behaviour differs more than you think
Proxycurl's rate limit was a soft per-key throttle: bursts were
absorbed, sustained throughput was capped, and you got 429s with a
clear retry-after header. Most replacement providers behave
differently. Some impose a hard concurrency cap (you can have at most
N requests in flight at once, regardless of throughput). Some use a
token-bucket that empties faster than it refills (so a brief burst is
fine, but a sustained high-volume window will degrade). Some return
429s without a retry-after and expect you to back off heuristically.
A migration script that worked perfectly during a 50-row test will queue-explode the first time you run it against a 100K-row backfill. The fix is to read the new provider's rate-limit doc carefully (or ask their support), and rewrite your retry/backoff logic specifically for that shape. Don't assume Proxycurl's behaviour port forward.
LinkFetch publishes the rate-limit shape in the API reference; the self-serve Pro tier uses a token bucket with a published refill rate, and the Scale tier negotiates custom bands. If you're moving to a different provider, ask explicitly: what happens to my burst, and what do I do when I get a 429?
Pitfall 6: Pricing per-result instead of per-request
This one bites differently depending on which way you migrated. If you moved to a per-result API (some Apify actors, some Bright Data plans, historically Proxycurl on certain endpoints), you're paying every time a search returns more rows. If you moved to a flat-per-request API (LinkFetch, some Coresignal plans), you're paying once per call regardless of result count.
The two pricing models drive opposite optimisation behaviour. On per-result, you write narrow queries and accept higher latency from multiple round trips. On flat-per-request, you write broader queries and post-filter in your code. A pipeline tuned for the first pricing model and run against a provider with the second can cost 3 to 5x more per equivalent workload, because you're making far more calls than you need to.
The fix is to re-tune the query shape after migration. Don't carry forward the Proxycurl-era pattern of "one call per profile, narrow filter". The costs comparison post walks through how the math actually pencils across providers; the shape of your traffic matters as much as the headline price.
Pitfall 7: Skipping the data-quality regression test
The single most common cause of a "we migrated and now something is silently wrong" ticket is that nobody compared the new provider's output against the old provider's output on a known set before the cutover.
The right test isn't "do the new provider's responses look reasonable". It's "for the same 1,000 input URLs, what fields are populated in the new provider's response that weren't in the old, what fields are missing, and where they overlap, do the values match?" Match-rate, populated- field-rate, and value-equality are three different metrics. All three should be checked.
Most teams skip this because it's tedious. The teams that skip it
discover, three weeks post-migration, that the new provider returns a
slightly different headline formatting that breaks downstream
de-duplication, or that the current_company.name field is now an
object instead of a string, or that the experience array is sorted
oldest-first instead of newest-first. None of these break the API call;
all of them break the application logic that depended on the old shape.
The fix: the day you sign the trial, set up a side-by-side diff job that runs both providers on a sample weekly. Eyeball the diffs. Fix the adapter. Then cut over.
Frequently asked questions
How long should a Proxycurl migration take?
A small team with a single integration and 10K-row scale can move in under a week, mostly the field-mapping table and the regression test. A larger team with multiple downstream consumers, customer-facing schemas, and an enterprise procurement loop should plan for 6 to 8 weeks, mostly contract review and parallel-running cost.
Should we abstract the data source behind an interface?
Yes. The single most useful refactor coming out of the Proxycurl shutdown is putting an interface between your application code and the provider SDK. The next provider migration is a config change, not a rewrite. Most of the migration tickets we see could have been a two-day swap if the previous integration had been wrapped.
Is it safe to assume LinkFetch won't shut down the way Proxycurl did?
LinkFetch's architecture is materially different. Data flows through the end user's own logged-in LinkedIn session via a Chrome extension the user installs deliberately, with five ban-avoidance guardrails that keep the per-account request profile within human-realistic bounds. There are no fake accounts, no service-owned proxies, no scraped session cookies. The architecture is the compliance story; the legal exposure that killed Proxycurl doesn't apply. For the longer answer, see how LinkFetch avoids LinkedIn bans.
What if our internal schema is too tightly coupled to Proxycurl's?
Build the adapter anyway, even if it has to be a thick one for a release or two. The alternative, rewriting your internal schema during the migration, doubles the work and the risk. Get to functional parity on the new provider first, then refactor your internal schema in a separate pass once the migration is stable.
How do we test for the geo coverage gap before committing?
Pull 200 known-good URLs per region you serve (US, UK, DACH, France, APAC, etc.), run them through the candidate provider on a trial key, and report populated-field rate per region. Don't trust the provider's own coverage marketing; their headline number is almost always a US- weighted blended average that hides regional weakness.
Can a small team without a data engineer do this?
Yes, with a caveat. The field-mapping table and the diff job are straightforward enough that a single backend engineer can ship them in a week. The procurement and DPA loop is where small teams get stuck, because they don't have a legal contact at the new provider. Ask explicitly for the DPA template on the trial signup form; most serious providers have one ready to send.
Last updated: 2026-05-11 by the LinkFetch team.