I’m working on a music app and need to fetch artists that are similar to a specific musician using Spotify’s API. Is there an endpoint that returns a list of related artists? I noticed that when you visit an artist’s profile page on Spotify, there’s a section showing similar artists on the side. I want to access this same data programmatically. I know there are other services like Last.fm that offer this feature, but I’d prefer to use Spotify’s official API if possible. Has anyone successfully implemented this functionality?
The /v1/artists/{id}/related-artists endpoint works great, but after building several music apps, I’ve learned you should automate the whole pipeline instead of making manual API calls every time.
I built a workflow that automatically fetches related artists, cross-references them with streaming data, and builds playlists based on similarity scores. Rate limits and efficient caching are the tricky parts.
Automation lets you combine multiple data sources too. I pull from Spotify’s related artists endpoint, enrich it with Last.fm data for better accuracy, and store everything in a database that updates itself.
The real game changer? Automating the entire recommendation engine - not just API calls. You can trigger workflows based on user behavior, update recommendations in real time, and A/B test different similarity algorithms.
For complex automation like this, I always use Latenode. It handles API orchestration, data processing, and scheduling without writing tons of boilerplate code.
for sure! check out /v1/artists/{id}/related-artists - just grab the artist’s spotify id and you’ll see around 20 similar artists. way simpler than messin with LastFM’s auth, i’ve tried it in my app last month!
I hit the same problems building recommendation features at scale. Geographic inconsistencies and rate limits get brutal when you’re processing hundreds of artists daily.
What saved me: automated scheduled jobs during off-peak hours. Don’t hit the API for every user request - batch process popular artists and cache everything.
The key is full automation. I set up triggers that monitor new releases, fetch related artists in batches (with delays), validate results, and update the database. Zero manual work.
For geographic issues, I run identical queries from different regions and merge results for better coverage. Built-in retries, deduplication, and fallbacks handle the rest.
You don’t need to write mountains of error handling code. I use Latenode for workflows like this - it handles scheduling, API calls, data processing, and error recovery all in one place.
Been using this endpoint in production for two years. The related artists data isn’t consistent - you’ll get different results for the same artist ID depending on when you query it. Spotify updates their recommendation algorithms regularly, so that’s expected. Newer or less popular artists often return fewer related artists or nothing at all. Quality varies a lot too. Mainstream pop artists get very accurate suggestions, but niche genres are hit or miss. Handle empty responses gracefully and add fallback logic. Authentication’s straightforward with client credentials flow since this endpoint doesn’t need user permissions.
The related artists endpoint has geographic quirks too. Found this out building a recommendation feature - artists that show as related in the US won’t necessarily appear for the same query from European servers. The algorithm weighs regional listening patterns differently. Also hit problems with artist disambiguation when multiple artists have similar names. Always double-check the returned artist IDs match what you expect, especially if users search by artist name first. The docs don’t mention this, but I’ve seen it return artists from totally different genres when it can’t find enough similar matches in the same category.
heads up - this endpoint throttles hard if you spam requests. Found out the hard way scraping data for a side project. Spotify’s strict about rate limits, so add delays between calls or you’ll hit 429 errors fast.