Monster vs Indeed vs ZipRecruiter — US Job Data Coverage Compared (2026)

Published April 27, 2026 · 1503 words · For developers

Thirdwatch publishes structured-data scrapers for Monster, Indeed, and ZipRecruiter — all three at $0.008 per job, all built on the same Camoufox stealth-browser architecture, all returning the same canonical schema. This guide compares coverage, salary fill-rate, listing overlap, and cost so you can decide which to pick (or whether to pick all three) for your specific use case.

Why three boards, not one

US job-board coverage is fragmented by historical accident and segment specialisation. According to the U.S. Bureau of Labor Statistics' employment situation reports, about 7.5 million job openings exist on a typical day in the US — but no single board indexes more than 60% of them. Each board has segment biases: Indeed is broad with strong tech-mainstream representation; Monster skews mid-market healthcare, manufacturing, and finance; ZipRecruiter overweights hourly, retail, and gig roles. Picking the right board (or combination) is the first decision in any US-jobs data pipeline.

The job-to-be-done depends on the consumer. A multi-source aggregator wants comprehensive nationwide coverage and accepts the dedupe overhead. A recruiter pipeline focused on hospital administration roles wants the board with best healthcare coverage and skips the others. A salary-research analyst needs the board with the highest published-salary rate. All three reduce to the same data shape — pull, structure, dedupe — but the right source mix differs by goal.

How does each board compare?

Side-by-side coverage and structure across the three Thirdwatch US-jobs scrapers:

All three return the same canonical schema (title, company, location, salary_min/max/period/currency, description, posted_date, source, url) — which is what makes a multi-source aggregator straightforward to build. The Monster Scraper actor page, the Indeed page, and the ZipRecruiter page all document the same field shape.

How to combine all three sources in 4 steps

Step 1: How do I authenticate against Apify?

Sign in at apify.com (free tier, no credit card), open Settings → Integrations, and copy your personal API token. Every example below assumes the token is in APIFY_TOKEN:

export APIFY_TOKEN="apify_api_xxxxxxxxxxxxxxxx"

Step 2: How do I run all three scrapers in parallel?

Spawn one async run per actor per metro and await all of them. Apify's API supports unlimited concurrent run starts within your account's compute limit.

import os, requests, time, json, pathlib

TOKEN = os.environ["APIFY_TOKEN"]
ACTORS = {
    "monster": "thirdwatch~monster-jobs-scraper",
    "indeed": "thirdwatch~indeed-jobs-scraper",
    "ziprecruiter": "thirdwatch~ziprecruiter-scraper",
}

QUERIES = ["registered nurse", "nurse practitioner"]
LOCATION = "Houston, TX"

run_ids = {}
for source, actor in ACTORS.items():
    r = requests.post(
        f"https://api.apify.com/v2/acts/{actor}/runs",
        params={"token": TOKEN},
        json={"queries": QUERIES, "location": LOCATION, "maxResults": 200},
    )
    run_ids[source] = r.json()["data"]["id"]

results = {}
for source, run_id in run_ids.items():
    while True:
        s = requests.get(
            f"https://api.apify.com/v2/actor-runs/{run_id}",
            params={"token": TOKEN},
        ).json()["data"]["status"]
        if s in ("SUCCEEDED", "FAILED", "ABORTED"):
            break
        time.sleep(20)
    if s == "SUCCEEDED":
        results[source] = requests.get(
            f"https://api.apify.com/v2/actor-runs/{run_id}/dataset/items",
            params={"token": TOKEN},
        ).json()
        print(f"{source}: {len(results[source])} jobs")

Three sources × 200 jobs each = up to 600 raw listings, completing in 15-25 minutes wall-clock with parallelism, costing $4.80 per run.

Step 3: How do I dedupe across sources?

Build the canonical 4-tuple key. SimplyHired's blog (Step 4 there) covers the same pattern; reuse that code:

import pandas as pd, re

def normalise(s):
    return re.sub(r"\W+", " ", (s or "").lower()).strip()

frames = []
for source, items in results.items():
    df = pd.DataFrame(items)
    df["source"] = source
    frames.append(df)

combined = pd.concat(frames, ignore_index=True)
combined["dedupe_key"] = (
    combined.title.apply(normalise) + "|"
    + combined.company.apply(normalise) + "|"
    + combined.location.apply(normalise) + "|"
    + combined.salary_min.fillna(-1).astype(int).astype(str)
)
unique = combined.drop_duplicates(subset=["dedupe_key"], keep="first")
print(f"Combined: {len(combined)} → {len(unique)} unique")

Expect 35-45% overlap to collapse to 55-65% unique listings — the dedupe is the entire reason multi-source aggregators are worthwhile.

Step 4: How do I prioritise sources when conflicting fields appear?

When the same job appears on multiple boards, prefer the source with the most complete data. ZipRecruiter wins on salary fill, Monster wins on description length, Indeed wins on freshness.

SOURCE_PRIORITY = {"ziprecruiter": 0, "monster": 1, "indeed": 2}
combined["priority"] = combined.source.map(SOURCE_PRIORITY)
unique = (combined
    .sort_values(["dedupe_key", "priority"])
    .drop_duplicates(subset=["dedupe_key"], keep="first")
    .drop(columns=["priority"]))

This keeps ZipRecruiter rows when salary is the most important field (highest fill-rate), Monster when description matters most, and falls back to Indeed for the long tail.

Sample output

A single record looks identical across all three boards — the canonical schema is the deliberate output of using the same Camoufox + parsing architecture across all three.

{
  "title": "Registered Nurse - ICU",
  "company": "Beth Israel Deaconess Medical Center",
  "location": "Boston, MA",
  "salary_text": "$80,000 - $115,000",
  "salary_min": 80000,
  "salary_max": 115000,
  "salary_currency": "USD",
  "salary_period": "yearly",
  "description": "...",
  "posted_date": "2026-04-21",
  "source": "monster",
  "url": "https://www.monster.com/job-openings/registered-nurse-icu-boston-ma"
}

The only field that varies by source is the source value itself ("monster", "indeed", or "ziprecruiter") and the URL hostname. Everything else maps consistently — which is what makes the multi-source aggregator pattern viable.

Common pitfalls

Three things break multi-source aggregators on these three boards. Salary-band conflicts — the same job on different boards may have published salary on one and null on another, or the bands may differ slightly because employers tweak postings per board. The 4-tuple dedupe key uses salary_min to prevent merging across genuinely different bands; for cleaner aggregation, fall back to a 3-tuple (drop salary) and re-merge salary later. Posted-date drift — Indeed often shows "1 day ago" rather than absolute dates which the actor returns as relative; for cross-board chronological sort, fall back to the date your pipeline ingested the row. Camoufox-cost compounding — at $0.008 per job × three boards × 1,000 jobs/day = $24/day. Plan accordingly; consider GOLD-tier pricing if you cross 100K records/month.

Thirdwatch's three scrapers all use the same Camoufox + humanize architecture, returning the same canonical schema, with the same 2GB / 3600s defaults — the deliberate symmetry is what makes multi-source ingestion straightforward. The Monster Scraper actor page, the Indeed page, and the ZipRecruiter page all document the same input and output shape.

Related use cases

• Scrape Monster jobs for a recruiter pipeline
• Track US job market with Monster data
• Build a jobs meta-search from Monster
• The complete guide to scraping job boards
• All Thirdwatch use-case guides

Frequently asked questions

Which board has the most US listings?

Indeed leads on raw volume — 250M+ unique listings indexed annually across all sources. Monster sits at 250K active US listings on a typical day. ZipRecruiter publishes 8-10M active listings with strong mid-market US coverage. For pure volume, Indeed wins; for coverage of segments under-served by Indeed, Monster and ZipRecruiter add value.

Which board has the best salary fill-rate?

ZipRecruiter has the highest published-salary rate on US listings (roughly 65%), followed by Monster (50-55%) and Indeed (40-45%). For salary-band analysis or recruiter offer calibration, start with ZipRecruiter and supplement with the others. None of the three are at 100% — the fundamental limit is whether each employer chooses to publish a band, not the scraper.

Are descriptions returned in the same format across boards?

Yes. All three Thirdwatch scrapers return description as full HTML-stripped text in a description field. Lengths vary by board — Monster averages 2,000-3,000 characters per description, Indeed 1,500-2,500, ZipRecruiter 1,800-2,800. Format-wise they're identical from the consumer's perspective, which simplifies LLM-matcher pipelines that ingest from all three.

What's the cost-per-job comparison?

All three sit at $0.008 per result on the FREE tier ($0.004 GOLD). The unit cost is the same because all three use the same Camoufox stealth-browser architecture for DataDome bypass. A multi-source aggregator pulling 1,000 jobs per board per day costs $24/day at FREE pricing or $12/day at GOLD across all three.

How much overlap is there in actual listings?

Estimated 35-45% overlap across any pair of the three boards on common queries. The same employer often posts to multiple boards. Naive aggregation triples listing counts; the canonical 4-tuple dedupe key (title-norm, company-norm, location-norm, salary_min) catches 85-90% of cross-source duplicates.

If I can only choose one, which should I pick?

Depends on use case. Aggregator builders should pick all three for coverage. Recruiter pipelines targeting a specific vertical should pick the board with best coverage in that vertical — Indeed for tech-mainstream, Monster for mid-market healthcare and manufacturing, ZipRecruiter for hourly and gig roles. For salary research specifically, ZipRecruiter's higher fill-rate makes it the best single source.

Run the Monster Scraper on Apify Store — pay-per-job, free to try, no credit card to test.