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RepDB
RepDB is a one-time-purchase exercise dataset for developers building fitness and workout apps โ not a subscription, not a rate-limited API. You download the data once and own it: JSON (and SQLite on the higher tier), WebP images, and full EN/DE/ES translations, with no per-request billing and no dependency on our servers staying up.
A free tier includes 250 exercises with flat-style 512ร512 images, attribution-licensed for commercial in-app use. The Starter tier ($199) adds the full catalog in classic white-background style. Standard ($399) adds transparent 1024px images, looping animations, exercise relations (similar/progressions/regressions), workout templates, and embeddings โ exclusive to that tier.
Every exercise includes muscle-group highlighting, equipment/muscle icons, MET values, and safety/goal tags. Compared to GIF- or JPG-based competitor APIs, RepDB images are transparent WebP with no watermarks, so they drop into any app UI without a white box around them.
Emergent RepDBRepDB's answer:
RepDB is sold as a one-time download, not a metered API โ you own the JSON/SQLite data and WebP images outright, with no rate limits, no per-request billing, and no risk of the vendor cutting off access. It's also the only dataset in this space with EN/DE/ES translations, transparent (alpha-channel) images with no watermark, muscle-group highlighting, safety/goal tags, and looping animations on the higher tier.
RepDB's answer:
RepDB grew out of a consumer workout app its creator was building solo. Sourcing exercise images and data meant either paying for a subscription API with usage caps and no caching rights, or producing everything from scratch. The illustrated, multi-language dataset was built for us first, then split out as its own product once it became clear other indie developers had the same problem and preferred to buy the data outright rather than rent it through an API.
RepDB's answer:
Most alternatives are subscription APIs โ you pay monthly, you're capped on requests, and ExerciseDB's terms of use explicitly forbid caching or storing the data at all, so every image render is a live paid API call. RepDB is the opposite: pay once, download the files, self-host with zero ongoing dependency. It's also the only option offering true DE/ES localization and transparent images instead of a white box behind every exercise.
RepDB's answer:
Solo developers and small teams building fitness or workout-tracking apps (iOS, Android, web) who need licensed exercise images and structured exercise data, but don't want to build their own media pipeline or depend on a rate-limited third-party API.
I used Emergent to build and prepare a website intended for a live launch. The concept of the platform is promising and the early build experience suggested it could be a very useful tool. However, over several days I encountered repeated technical and deployment problems that ultimately prevented the site from going live.
During development I experienced:
Deployments reporting as successful while requested changes did not appear on the live site
Inconsistent behaviour between preview and production environments
Multiple requests needing to be repeated before partial fixes were applied
Credits being consumed while trying to resolve issues that appeared to be platform-related rather than user error
A major concern was the level of customer support during this period. Responses were often delayed, acknowledgements did not translate into timely resolution, and there were gaps in communication while the work was effectively at a standstill.
Most recently, the build engine itself indicated it was unable to resolve one of the problems, leaving no clear path to complete deployment despite continued attempts.
In preparation for launch, I had already invested in supporting services such as voice generation through 11 Labs and a Canva subscription to create promotional materials linked to the build. These were arranged specifically for rollout, so the inability to achieve a stable deployment resulted in additional wasted expense beyond the platform itself.
As someone trying to move from development to an operational launch, this created a significant setback in both time and cost despite sustained effort to work through the issues.
The overall idea behind the platform is strong, but in my experience the reliability of deployment, consistency of updates reaching production, and speed of support response were not yet dependable enough for a live environment.
โข Slow or inconsistent customer support responses when issues arise. โข Acknowledgement of problems does not always lead to timely resolution. โข Deployment process can report success even when changes have not actually gone live. โข Repeated troubleshooting cycles may consume credits without resolving the underlying issue. โข Communication gaps during fault resolution can leave projects at a standstill. โข Limited transparency on what is happening behind the build/deployment pipeline when errors occur. โข Platform can feel difficult to rely on for time-sensitive or production launches.
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