Unit economics that scale
A decentralized GPU network reports inference cost reductions of up to 20× versus traditional cloud. That is the difference between freemium being a marketing expense and being a growth engine.
deAPI is one unified API for image, video, audio and multimodal models — running on a decentralized GPU network. If you have outgrown Replicate's per-second billing, this page is for you.
Four structural differences that tend to force the decision.
A decentralized GPU network reports inference cost reductions of up to 20× versus traditional cloud. That is the difference between freemium being a marketing expense and being a growth engine.
Same request/response shape for txt2img, img2video, txt2speech. One retry handler, one webhook consumer, one SDK surface.
Mainstream image and video models stay warm across the network, so users clicking "generate" do not wait for a container boot. Interactive UX stays interactive.
First-party llms.txt, MCP server, consistent slugs across modalities. Claude Code, Cursor or Cline can wire up image, video and audio in a single session.
You already know which models you want to run and now need to scale them cost-efficiently.
Your product calls more than one modality — image, video, speech, music — and you are tired of wrapping three different schemas.
Freemium or free-trial generation is part of your acquisition loop, and the GPU-second meter is eating the funnel.
You care about cold-start latency for interactive UX — users clicking "generate" expect output in seconds, not after a container boot.
Your team is small and you want an agent-friendly API (llms.txt, MCP, consistent slugs) so Claude Code or Cursor can wire things up without hand-holding.
You are building a brand-new model and need to push a custom Cog container tomorrow.
Your workflow depends on fine-tuning — SDXL, Flux or custom LoRA training — integrated into the same product.
You specifically need a long-tail community model that only exists as a Replicate-hosted version.
You are at prototype stage and predictability of per-GPU-second billing matches how your team thinks about cost.
The scannable version. Every claim verified against public product docs as of April 2026.
Both products iterate frequently — pricing numbers intentionally omitted. Always verify current capabilities on each vendor's live docs.
Same async + polling pattern you already use on Replicate. Just a different base URL, auth header, and model slug. Your webhook consumer and retry logic do not change.
Pull GET /api/v1/client/models once and map your Replicate versions to deAPI slugs (for example FLUX Schnell → Flux1schnell).
Submit to POST /api/v1/client/txt2img (or img2video, txt2video, …). You will receive a request_id.
Poll GET /api/v1/client/request-status/{request_id} — or pass a webhook_url on the submit call to have deAPI push the result.
curl · deAPI txt2img
curl -s -X POST https://api.deapi.ai/api/v1/client/txt2img \
-H "Authorization: Bearer $DEAPI_TOKEN" \
-H "Content-Type: application/json" \
-d '{
"model": "Flux_2_Klein_4B_BF16",
"prompt": "Futuristic city at sunset",
"width": 1536,
"height": 896,
"steps": 4,
"seed": 42
}'
api.replicate.com to api.deapi.ai and map your Replicate model versions to deAPI slugs returned by /api/v1/client/models. Auth header format is the same (Bearer), so your HTTP client config does not change. Polling and webhook handlers keep working because deAPI keeps the same response shape across every modality — one handler covers image, video, speech and music.
llms.txt index, an MCP server, and a consistent schema across modalities so agents such as Claude Code, Cursor or Cline can wire up image, video and audio generation in a single session — no per-model wrappers required.