The latest articles on DEV Community by Yiğit Efe Erdoğmuş (@efeerdogmus0). https://dev.to/efeerdogmus0 https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F3132198%2F05350454-ee97-42d2-a7c6-fe40f8dd1881.jpg https://dev.to/efeerdogmus0 en Yiğit Efe Erdoğmuş Mon, 27 Jul 2026 15:36:27 +0000 https://dev.to/efeerdogmus0/we-kept-the-image-api-experience-and-changed-the-economics-underneath-it-40pn https://dev.to/efeerdogmus0/we-kept-the-image-api-experience-and-changed-the-economics-underneath-it-40pn <div class="highlight js-code-highlight"> <pre class="highlight plaintext"><code>Disclosure: I am one of the founders of Kavram. AI image products tend to inherit a business decision they did not mean to make. Either they keep paying an inference API markup as volume grows, or their team starts operating GPUs, drivers, containers, model caches, queues, and capacity. We are building a third path: keep one normal async image-generation API while the compute layer is designed around compatible distributed GPU capacity. The developer flow is intentionally boring: 1. Choose a model and submit a prompt. 2. Receive a job ID. 3. Observe queued, processing, and completed states. 4. Poll or consume the completion path. 5. Keep the same integration shape across the image catalog. The economics are the part we want challenged. Our current dated reference is FLUX.1 Schnell at 1 MP: $0.0012 per completed image on Kavram. fal.ai currently lists $0.003 per megapixel for the comparable model reference. That does not prove every output will be accepted, every latency profile will match, or every model will have the same ratio. The useful comparison for a production team is: - same model and settings - accepted outputs, not only submitted jobs - p50 and p95 latency - failures and retry behavior - unit cost at the team’s actual volume We have a working playground and API flow, and we are opening the next early-access cohort. The question for developers here: Which artifact would make this credible enough to evaluate—an open benchmark harness, public latency distributions, webhook/retry examples, or a migration sample? If you already have an image workload, send one real prompt with your model and resolution: https://kavram.ai/en/request-access?utm_source=devto&amp;utm_medium=community&amp;utm_campaign=yc_f26_20h&amp;utm_content=showdev_build_story&amp;utm_term=image_api_developers Developer docs: https://kavram.ai/en/docs </code></pre> </div> ai api infrastructure startup