The 50 Fiverr Gig Examples Behind Our Own Site: A Full Price, Delivery, and Rating Breakdown (2026)

Most of the data posts on this site are built from a large, real catalog — the 323 freelancer profiles behind our hire-guide pages, or the 137 services behind our price index. This post is different, and we want to be upfront about that difference before a single number shows up.

fiverr-gigs.ts is a much smaller file: 50 example gig listings, organized into 10 categories of 5 gigs each (Bloxburg Builders, Roblox Scripters, Discord Bot Developers, Logo Designers, YouTube Thumbnail Designers, AI Chatbot Developers, Video Editors, Shopify Developers, VRChat Avatar Creators, and Website Builders/WordPress). It's the data behind the "Top Rated [Service] on Fiverr" gallery section you'll see on our DIY guides, our category pages, and a rotating "featured gigs" strip on the homepage.

A note on what this actually is: these are illustrative example gigs, not a live or scraped snapshot of real Fiverr listings. Three things in the file itself make that clear once you look — every "View on Fiverr" link in this dataset points to a generic Fiverr search-results query (e.g. fiverr.com/search/gigs?query=bloxburg+builder), not a permalink to a specific gig page; none of the 50 entries has a populated imageUrl, so every card on the live site falls back to a gradient-and-initial placeholder rather than a real gig thumbnail; and, as you'll see below, the rating field follows an identical pattern in all 10 categories in a way that real, independently-collected reviews essentially never do. We're analyzing this file because it's ours, we can show you every row of it, and running the numbers honestly is more useful than pretending it's something it isn't.

We read fiverr-gigs.ts in full and counted every entry ourselves rather than trusting an assumed count. It holds exactly 50 gigs across 10 category collections, 5 gigs per category, with no more and no fewer. Each gig entry carries a title, seller name, rating, review count, price (as a string, e.g. "$15"), delivery time in days, a short tag list, and a Fiverr URL — plus an optional gig-thumbnail image field that is present in the schema but empty on every single one of the 50 rows.

For each of the 10 categories we computed the mean price and mean delivery time across its 5 gigs, plus the min/max range. We did not drop, merge, or re-weight any entries, and we did not extrapolate these numbers to "what Fiverr charges" in general — with 5 gigs per category, this is a look at one specific example set, not a market survey.

Every category in the file has exactly 5 gigs, so comparing average price category-to-category is at least apples-to-apples within this dataset. Sorted from most to least expensive:

The gap between the cheapest and priciest category average is 4.8x ($65.00 vs $13.60) — AI Chatbot Developers is the most expensive category in the file, YouTube Thumbnail Designers the cheapest. At the individual-gig level, the single cheapest listing in the whole file is a $5 YouTube thumbnail gig, and exactly two gigs tie for the most expensive at $100 (a "custom GPT training" gig and a WooCommerce ecommerce build) — one from each of the two priciest categories.

Interestingly, that $5 thumbnail gig also carries the highest review count of any of the 50 entries (5,632), the fastest delivery time in the file (1 day), and a perfect 5.0 rating. Cheap, fast, and heavily reviewed lining up on the same example gig is a plausible real-world pattern — low-friction, high-volume categories genuinely do rack up more completed orders — but it's one example gig in a 50-row illustrative file, not proof of a market-wide rule.

The three cheapest categories (YouTube Thumbnails, Bloxburg Builds, Logo Design) are also the three fastest to deliver, in the same order. If the data formed a perfect staircase, that pattern would hold all the way up — it doesn't.

This is the part of the file that most clearly signals "illustrative example," not "independently collected reviews." Every one of the 50 ratings falls in a narrow 0.3-star band — between 4.7 and 5.0 — which alone would just mean ratings compress near the top, something we've also documented in our real, much larger freelancer-profile catalog. But this file goes a step further.

Look at the rating set within each of the 10 categories, regardless of order: every category contains exactly one 5.0 and exactly two 4.9s. The remaining two ratings are either one 4.8 and one 4.7 (7 of the 10 categories) or two 4.8s (the other 3: Logo Designers, Shopify Developers, and Website Builders). Across all 50 entries, only four distinct rating values exist at all: 5.0 appears exactly 10 times (once per category), 4.9 appears exactly 20 times (twice per category, without exception), 4.8 appears 13 times, and 4.7 appears 7 times.

If you want the version of this finding built from real listings instead, we've already published it: 99.7% of the 294 rated profiles in our own 323-profile freelancer catalog score between 4.8 and 5.0. That's a genuinely collected dataset showing genuine rating compression — a different, real-world reason star ratings barely differentiate sellers. The pattern in this 50-gig illustrative file is more extreme and more mechanical (the exact same shape, every category, no exceptions), which is itself the tell that we're looking at placeholder data here, not another real sample.