Novita AI API alternative for video and image jobs: Sume

Is Sume an alternative to Novita AI?

Only for part of what Novita does. Novita's introduction frames three things: access AI models, run code in agent sandboxes, or deploy on GPUs. Sume covers the first for media generation and nothing of the other two. You cannot rent a GPU or a sandbox from Sume; you call managed generation and Format endpoints and get files back.

What does Novita list for video and image?

Its documentation index lists a Unified Video Generation reference, Wan 2.6 text-to-video and image-to-video pages, and image pages for Qwen Image and Qwen Image Edit. The introduction mentions API-key access and a billing page with account credit. The index did not include dedicated pages for a task-result endpoint or webhooks, which may live inside the individual API reference pages, so read the model page you plan to use before building polling or callback code.

For Sume, the equivalent surface is POST /v1/videos and POST /v1/images. Wan is in the Sume video catalog as wan-3.0, accepting 2 to 30 seconds per the video guide. Model lists change often, so call GET /v1/videos/models rather than trusting any blog table, including this one.

What does Sume give you for the model-API part?

A single job lifecycle. Submit with an Idempotency-Key, store the job id, poll GET /v1/jobs/:id/status, and read finished files from media.sume.com. Errors use one envelope with a request id, and job failures carry a category, stage and retryability so your client can decide whether to retry. Raw provider URLs and task ids are not part of the public contract, which means you cannot build on a provider's link that may expire.

Capacity is plan-based: Free runs 1 job at a time, Pro 4, Startup 8, Scale 20. Extra valid jobs wait as queued, up to max(3, concurrency x 5), then return 429 queue_full. Balance is reserved on submit, and a shortfall returns 402 insufficient_credits before any provider work begins.

What does a first port look like?

Start with one image call and one video call, because they show you both response shapes. For images, replace the Novita model page's endpoint with POST /v1/images, pick a model slug from GET /v1/images/models, and read the hosted URLs in the data array. For video, call POST /v1/videos, store the polling_url, and poll GET /v1/videos/{jobId} until completed.

Then deal with failure. Sume reports failed jobs with a category such as validation, quota, queue, generation_rejected or generation_timeout, and each has a recommended next action (fix input, add funds, retry later with the same key). If your Novita client has a single generic retry, split it along those lines so you do not retry a rejected input forever or hammer a full queue.

Finally, measure. Run the same ten prompts on both platforms, compare cost per accepted clip and time to completed, and include your retry rate in the figure. Public docs on either side will not tell you that number.

How should you decide?

If you need custom weights, your own containers or GPU time, Novita is the right category and Sume is not. If you only need finished video and image files through a stable API, Sume removes the infrastructure choices. The two also coexist: run bespoke inference on Novita and customer-facing renders on Sume.

Before porting, list which Novita endpoints you actually call. Anything outside generation (sandboxes, GPU pods) has no Sume equivalent, and plan for that gap first.

curl -X POST https://api.sume.com/v1/images \
  -H "Authorization: Bearer $SUME_API_KEY" \
  -H "Content-Type: application/json" \
  -H "Idempotency-Key: novita-port-001" \
  -d '{"model":"sume/auto","prompt":"Product shot of a ceramic mug on a linen cloth"}'