generative media API image generation API video generation API unified media API AI media API GenRelay

What Is a Generative Media API? A Developer's Guide

Aug 31, 2026·7 min read

You want to add AI-generated images to your product. You start reading documentation — one provider uses API keys in a custom header, another uses Bearer tokens; one returns a base64-encoded image immediately, another returns a job ID you poll for 30 seconds. If you need two image models for different use cases, you end up writing two separate clients, managing two credential sets, and debugging two different error formats. A generative media API exists to remove this fragmentation and give your application one consistent interface to the entire model ecosystem.

What Does "Generative Media API" Mean?

A generative media API is an HTTP interface that accepts text prompts — or reference images — as input and returns AI-generated media as output: images, video clips, or audio. "Generative" refers to the model class: these APIs wrap foundation models that synthesize new content rather than classify or retrieve existing content.

The term covers a spectrum:

Single-model APIs: one endpoint, one model, one provider — calling only that provider's proprietary model.
Unified multi-model APIs: one endpoint that routes requests to many models, so you switch models by changing a model parameter rather than rewriting your HTTP client.

GenRelay is a unified generative media API — it exposes image models (Nano Banana Pro, Nano Banana 2, GPT-image-2) and video models (Veo 3.1, Grok Imagine 1.0 & 1.5, Gemini Omni Flash) under a single OpenAI-compatible interface. You authenticate once with a single Bearer token and call any model on the platform.

Why Developers Choose a Unified API Over Direct Integrations

The alternative to a unified API is calling each model provider's own endpoint directly. That approach works for a single model, but compounds quickly when you need flexibility:

Problem	Direct integrations	Unified API
Authentication	One credential system per provider	Single Bearer token
Endpoint formats	Different structure per provider	Consistent request schema
Billing	Multiple invoices and dashboards	One account
Model switching	Rewrite client code	Change the `model` field
Error handling	Different error codes per provider	Normalized error responses
Rate limit tracking	Per-provider dashboards	Single quota view

As of August 2026, GenRelay's image and video model catalog lets you switch from Nano Banana Pro to GPT-image-2 to Veo 3.1 by changing one field in your request — no new client, no new credential management.

What the Request/Response Cycle Looks Like

Synchronous: image generation

For image generation, the cycle is straightforward:

Your server sends a POST with model, prompt, and optional resolution parameters.
The API returns an image URL (or base64-encoded image) in the response body.
You store or stream the URL to your client.

Here is a minimal working example:

import requests

API_KEY = "YOUR_GENRELAY_KEY"

response = requests.post(
    "https://genrelay.ai/v1/images/generations",
    headers={"Authorization": f"Bearer {API_KEY}"},
    json={
        "model": "nano-banana-pro",
        "prompt": "A minimalist product shot of a glass water bottle on a white background",
        "size": "1024x1024"
    }
)

data = response.json()
image_url = data["data"][0]["url"]
print(image_url)

Switch to GPT-image-2 for instruction-following and in-painting by changing one line:

json={
    "model": "gpt-image-2",
    "prompt": "...",
    "size": "1024x1024"
}

Everything else — auth header, base URL, response parsing — stays identical.

Asynchronous: video generation

Video generation takes 15–120 seconds, which is too long for a synchronous HTTP response. The API uses a job pattern:

Submit a POST — receive a job_id immediately.
Poll GET /v1/videos/generations/{job_id} at intervals until status equals "completed".
Retrieve the video URL from the completed response.

import requests, time

API_KEY = "YOUR_GENRELAY_KEY"

# Submit the job
job_resp = requests.post(
    "https://genrelay.ai/v1/videos/generations",
    headers={"Authorization": f"Bearer {API_KEY}"},
    json={
        "model": "veo-3-1-lite",
        "prompt": "A calm ocean wave rolling onto a sandy beach at golden hour",
        "duration": 8,
        "resolution": "720p"
    }
)
job_id = job_resp.json()["id"]

# Poll until done
while True:
    status_resp = requests.get(
        f"https://genrelay.ai/v1/videos/generations/{job_id}",
        headers={"Authorization": f"Bearer {API_KEY}"}
    )
    result = status_resp.json()
    if result["status"] == "completed":
        print(result["data"][0]["url"])
        break
    elif result["status"] == "failed":
        raise Exception(result.get("error", "Generation failed"))
    time.sleep(5)

What Models Are Available and What Do They Cost?

Image models

Model	Best for	1K price	4K price
Nano Banana Pro	High-resolution output, accurate detail	$0.030	$0.042
Nano Banana 2	Cost-efficient image generation	$0.020	$0.036
GPT-image-2	Instruction-following, in-painting, editing	$0.014 (flat)	$0.014 (flat)

Nano Banana Pro and Nano Banana 2 are tiered by resolution: 1K and 2K images cost the same, while 4K incurs a higher rate. GPT-image-2 charges a flat $0.014 per image regardless of resolution.

Video models

Model	Billing model	720p rate	1080p rate
Veo 3.1 Lite	Per second	$0.060/s	$0.120/s
Grok Imagine 1.0	Per second	$0.010/s	—
Grok Imagine 1.5	Per second	$0.022/s	—
Gemini Omni Flash	Per generation	$0.10 flat	$0.15 flat

Video APIs use two distinct billing patterns. Per-second models (Veo 3.1, Grok) charge proportionally to output duration — an 8-second Veo 3.1 Lite 720p clip costs 8 × $0.060 = $0.48. Per-generation models (Gemini Omni Flash) charge a flat rate per job submission regardless of output length, which makes cost per clip predictable when clip duration varies.

How Is the API Structured?

GenRelay follows an OpenAI-compatible API design. Key endpoints:

POST /v1/images/generations — text-to-image
POST /v1/images/edits — image editing (GPT-image-2)
POST /v1/videos/generations — text-to-video and image-to-video
GET /v1/videos/generations/{id} — poll video job status

Request bodies use JSON. Responses follow a consistent { "data": [...], "status": "..." } envelope. Error responses include a machine-readable code field alongside the human-readable message.

Who Uses Generative Media APIs?

SaaS product builders embed AI-generated visuals into a product feature — a social media content generator, a marketing asset tool, a design automation workflow — without operating GPU infrastructure themselves. The API call sits inside a backend route; the generated URL is stored in their database.

Content automation pipelines batch-generate images for e-commerce product listings, localized ad creative, or SEO-optimized article thumbnails. These are typically script-driven Python jobs running on a schedule.

App developers expose on-demand generation directly in a mobile or web interface, where users trigger generation through a UI action and see results within seconds (image) or under two minutes (video).

In all three cases, the generative media API is an infrastructure dependency — it handles model serving, scaling, and billing so your application handles the product logic.

Frequently Asked Questions

What is the difference between a generative media API and calling a model directly?
Calling a model directly means integrating with a single provider's proprietary endpoint. A generative media API is a layer above that — it may expose many models under a shared interface. GenRelay is a generative media API; Veo 3.1 is a model. The distinction matters when you want to add a second model to your app without rewriting your HTTP client.

Do generative media APIs support image editing, not just text-to-image?
Yes, if the underlying model supports it. GPT-image-2 supports in-painting — editing a region of an existing image using a text description and an edit mask. The request goes to /v1/images/edits and includes a mask field alongside the original image.

Is the output resolution fixed?
No. Image models support resolution tiers (1K, 2K, 4K). Video models accept a resolution parameter (720p, 1080p). Higher resolutions increase compute cost, which is why pricing is tiered by resolution.

What is a job ID and why do video APIs use it?
Video generation takes 15–120 seconds — too long for a synchronous HTTP response. The API returns a job ID immediately so your request doesn't time out, then you poll a status endpoint. When status reaches "completed", the response body includes the video file URL.

Can I call both image and video models from the same codebase?
Yes. With a unified API like GenRelay, you authenticate once. Your auth headers and base URL stay the same across image and video calls; only the endpoint path and model field change.

For a deeper comparison of unified APIs versus building direct model integrations yourself, see the unified AI media API guide.