Co-founder @ Kavram.ai — distributed AI inference. Building across robotics, embedded systems, mechanical design and PCB

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<h3>Image generation without the API markup—or the GPU burden.</h3>AI product teams shouldn’t have to choose between overpaying an inference provider and becoming a GPU infrastructure company.Kavram is the third path: one production image-generation API, with distributed GPU capacity underneath.<h3>One integration. Six image models.</h3>Your application keeps the familiar workflow:<ul><li>One async request format</li><li>Queued → processing → completed job states</li><li>Polling and signed webhooks</li><li>cURL, Python and Node.js integration</li><li>No GPU rental, driver setup or idle capacity bill</li><li>Automatic refunds for capacity-related failures</li></ul>Change the model—not your entire infrastructure.<h3>Same model. Better economics.</h3>For our dated FLUX.1 Schnell · 1 MP reference:<ul><li>Kavram: $0.0012 per completed image</li><li><a target="_blank" rel="noopener noreferrer nofollow ugc" class="break-all link-break text-gray-500 hover:underline" href="http://fal.ai">fal.ai</a>: $0.003 per MP</li></ul>That’s the same model reference at a significantly lower unit price—without asking your product team to manage GPUs.This is a scoped comparison, not a promise that every model or workload has the same ratio. Production teams should measure accepted-output quality, p50/p95 latency, reliability and true cost using their own prompts.<h3>Built for teams shipping real image features.</h3>Kavram is designed for:<ul><li>AI products and creative tools</li><li>Ecommerce and catalog generation</li><li>Games, avatars and personalization</li><li>Agencies and high-volume visual workflows</li><li>ML and platform teams that want API convenience without GPU operations</li></ul><h3>We want real workloads, not vanity signups.</h3>We’re opening Kavram’s next early-access cohort to teams already generating images.Bring one real prompt, your current model and approximate monthly volume. We’ll help determine whether the comparison is worth running.