How to Scrape Fiverr in 2026 (API & Python)

Fiverr doesn't publish a public API for reading gig listings, search results, or seller profiles — what it does offer is an Affiliate API for tracking referral commissions, which solves a different problem entirely. This guide covers what that gap means in practice, why a DIY scraper against Fiverr's gig and search pages breaks fast, and how to pull gig search, gig detail, and seller profile data through a structured API instead.

Why scrape Fiverr data?

• Freelance market-rate research — benchmark starting prices, package tiers, and delivery times for a skill category (logo design, voiceover, SEO) across live gigs.
• Gig-economy analytics — track how pricing and review volume shift for a category or keyword over time.
• Competitor and seller monitoring — watch a specific seller's rating, level, response time, and gig count without checking their profile by hand.
• AI/LLM pipelines — feed normalized freelance-service data into models that price gig work or match briefs to sellers.

Is it legal to scrape Fiverr?

Option 1: DIY in Python (and why it breaks)

A single gig page is easy to fetch by hand:

import requests
from bs4 import BeautifulSoup

resp = requests.get(
    "https://www.fiverr.com/inshape_studio/do-modern-minimalist-logo-design-for-your-business",
    headers={"User-Agent": "Mozilla/5.0"},
)
soup = BeautifulSoup(resp.text, "html.parser")
title = soup.select_one("h1")

This works exactly once, then degrades fast:

• Heavy client-side rendering. Gig and seller pages hydrate through JavaScript, so a plain requests call often returns a shell without the package tiers, seller stats, or review data you actually want — you need a headless browser to get the rendered DOM.
• Bot detection and rate limiting. Fiverr fronts its pages with active anti-automation defenses; repeated or fast requests from one IP get throttled, CAPTCHA-walled, or blocked outright.
• Search has no stable public endpoint. The gig search results page is disallowed in robots.txt and depends on filters, pagination state, and client-side rendering that changes without notice — there's no documented query parameter contract to build against.
• Layout drift. Fiverr redesigns gig cards and profile layouts periodically; a selector-based scraper silently breaks and returns partial or empty fields until someone notices.

Option 2: No-code / ready-made tools

Generic point-and-click scrapers can pull an individual Fiverr gig or seller page, but they hit the same JavaScript-rendering and anti-bot wall a DIY script does — a visual scraper still has to solve headless rendering and detection avoidance, it just hides the code from you. For anything beyond a handful of pages, a maintained backend that already handles rendering and normalization is less fragile than configuring a generic tool against a marketplace that updates its markup and defenses on its own schedule.

Option 3: A structured Fiverr API

Crawlora's Fiverr API wraps gig search, gig detail, and seller profile pages behind three endpoints and returns normalized JSON — no browser automation or session management on your side.

Search gigs by keyword:

curl "https://api.crawlora.net/api/v1/fiverr/search?q=logo%20design&page=1" \
  -H "x-api-key: $CRAWLORA_API_KEY"

import requests

h = {"x-api-key": "YOUR_API_KEY"}
base = "https://api.crawlora.net/api/v1/fiverr"

search = requests.get(f"{base}/search", headers=h, params={"q": "logo design", "page": 1}).json()["data"]
for gig in search["gigs"]:
    print(gig["title"], gig["starting_price"], gig["rating"])

Response shape (real example):

{
  "code": 200,
  "msg": "OK",
  "data": {
    "query": "logo design",
    "page": 1,
    "gigs": [
      {
        "id": "24460733",
        "title": "do modern minimalist logo design for your business",
        "url": "https://www.fiverr.com/inshape_studio/do-modern-minimalist-logo-design-for-your-business",
        "seller_username": "inshape_studio",
        "seller_country": "Bangladesh",
        "seller_level": "level_two_seller",
        "rating": 4.9,
        "review_count": 3421,
        "starting_price": 10,
        "image": "https://fiverr-res.cloudinary.com/gig.jpg",
        "category_id": 2,
        "sub_category_id": 65
      }
    ]
  }
}

Pull the full detail for one gig by username and slug (both come straight from a search result's url):

gig = requests.get(f"{base}/gig/inshape_studio/do-modern-minimalist-logo-design-for-your-business", headers=h).json()["data"]
for pkg in gig["packages"]:
    print(pkg["tier"], pkg["price"], pkg["delivery_days"])

That returns package tiers (Basic/Standard/Premium with price and delivery days), the full description, tags, gallery images, orders in queue, and an embedded seller summary.

Look up a seller's profile by username:

seller = requests.get(f"{base}/seller/inshape_studio", headers=h).json()["data"]
print(seller["level"], seller["hourly_rate"], seller["approved_gigs_count"])

That returns seller level, verification status, hourly rate, languages, join date, and the list of gig ids attached to that profile.

What you can collect

• Search results — gig id, title, url, seller username/country/level, rating, review count, starting price, and category/subcategory ids.
• Gig detail — full description, category and subcategory, rating, review count, orders in queue, package tiers (name, description, price, delivery days), tags, and gallery images.
• Seller profiles — display name, one-liner title, description, country, seller level, verification status, hourly rate, languages, join date, and approved gig count with gig ids.

Limitations

• Public data only. This covers what's visible on public search, gig, and seller pages — no buyer-side messaging, order history, or private seller analytics.
• Slug-based gig lookup, not open crawling. Gig detail takes a specific username and slug pair; discover them through search first, then fetch detail for the ones you need.
• Search fields are lighter than gig detail. The search endpoint returns a summary (title, starting price, rating); pull the gig detail endpoint for full package pricing and delivery times.
• Marketplace snapshots, not live order queues. Orders-in-queue and review counts reflect the page at fetch time — Fiverr doesn't expose live transaction data through public pages.

Where this gets used

• Freelance service pricing research — benchmark starting prices and package tiers by category to price your own gigs or a client's.
• Gig-economy market research — track category demand and pricing trends over time.
• Seller due diligence — screen sellers by level, rating, and review count before outsourcing work.
• AI/LLM training and matching data — normalized gig and pricing data for models that price freelance work or match briefs to sellers.

Sources

Start collecting

Try it first, free: run any public URL through the Free Web Scraper, or check whether a site blocks bots with the Anti-Bot Checker — no signup.

Test the search, gig, and seller endpoints in the Playground, check the schema in the API docs, and review pricing. Fiverr is the direct freelance-marketplace peer to Upwork — pair the two if you're benchmarking gig-based pricing against hourly and fixed-price project rates for the same skills. If you're tracking traditional hiring alongside freelance demand, how to scrape job postings covers the job-board side, and how to scrape GitHub covers sourcing developers by their public activity. For the legal framework behind all of this, see is web scraping legal.

Part of our how-to-scrape guide series — every platform we cover, in one index.