# Runway Aleph 2.0

> Run Runway Aleph 2.0 by runway online or via API. $0.28/sec, no setup.

**URL:** https://inference.sh/apps/runway/aleph-2
**Last updated:** 2026-08-04

---

Runway Aleph 2.0 — video-to-video transformation with text and keyframe guidance. Supports aspect ratio targeting and up to 5 reference keyframes. 28 credits/second.

- app: `runway/aleph-2`
- maker: [runway](https://inference.sh/apps/runway.md)
- category: [video](https://inference.sh/apps/category/video.md)
- input → output: video → video
- tasks: [video to video](https://inference.sh/apps/tag/video-to-video.md)
- price: $0.28/sec
- runs via: api, javascript and python sdk, belt cli, mcp

## aleph-2 API Guide

### About
runway aleph 2.0 — video-to-video transformation with text and keyframe guidance. supports aspect ratio targeting and up to 5 reference keyframes. 28 credits/second.

### Get an API Key
Create one in [settings → api keys](https://app.inference.sh/settings/keys). The examples read it from `INFERENCE_API_KEY`.

```bash
export INFERENCE_API_KEY="inf_your_key"
```

### 1. Calling the API

#### Install the Client

```bash
pip install inferencesh
```

#### Run and Get Result
Submit a request and wait for the final result.

```python
import os
from inferencesh import inference

client = inference(api_key=os.environ["INFERENCE_API_KEY"])

## version @7wcdm3j1 pinned
result = client.run({
        "app": "runway/aleph-2@7wcdm3j1",
        "input": {
            "video": "https://example.com/file.png"
        }
    })

print(result["output"])
```

#### Stream Live Updates
Get real-time progress updates as the task runs.

```python
import os
from inferencesh import inference

client = inference(api_key=os.environ["INFERENCE_API_KEY"])

## version @7wcdm3j1 pinned
## stream=True yields updates as they arrive
for update in client.run({
        "app": "runway/aleph-2@7wcdm3j1",
        "input": {
            "video": "https://example.com/file.png"
        }
    }, stream=True):
    if update.get("progress"):
        print(f"progress: {update['progress']}%")
    if update.get("output"):
        print(f"output: {update['output']}")
```

#### Run in the Background
Start the task and get its id back right away, then check on it with `GET /tasks/{id}`. It is done when `status` is 10 (completed), 11 (failed) or 12 (cancelled).

```python
import os
from inferencesh import inference, TaskStatus

client = inference(api_key=os.environ["INFERENCE_API_KEY"])

## returns as soon as the task is created
task = client.run({
        "app": "runway/aleph-2@7wcdm3j1",
        "input": {
            "video": "https://example.com/file.png"
        }
    }, wait=False)

## later: read its current state
state = client.tasks.get(task["id"]).data
if state["status"] == TaskStatus.COMPLETED:
    print(state["output"])
```

### 2. Files

#### Automatic Upload

```python
## local file paths are automatically uploaded
result = client.run({
    "app": "runway/aleph-2@7wcdm3j1",
    "input": {
        "image": "/path/to/local/image.png",  # detected & uploaded
        "audio": "https://example.com/audio.mp3",  # url passed through
    }
})
```

#### Manual Upload

```python
## upload and get a hosted URL
file = client.files.upload("/path/to/file.png")
print(file.uri)  # https://cloud.inference.sh/...
```

### 3. Schema

#### Input
- **video** (string) *required*: Input video to transform. Max 16MB.
- **prompt** (string): Text prompt to guide the transformation.
- **target_aspect_ratio** (string): Target aspect ratio for the output video.
- **keyframes** (array): Up to 5 keyframes with reference images at specific timestamps.
- **seed** (integer): Seed for reproducible results.

#### Output
- **video** (string): Transformed video file.

## other video apps

- [VACE](https://inference.sh/apps/pruna/vace.md): $0.40/video
- [P-Video-Animate](https://inference.sh/apps/pruna/p-video-animate.md): $0.03/sec 720p, $0.06/sec 1080p
- [P-Video-Upscale](https://inference.sh/apps/pruna/p-video-upscale.md): Std: $0.01/sec 720p, $0.02 1080p, $0.04 2k, $0.08 4k; high fps 2x
- [P-Video-2-Pro-Avatar](https://inference.sh/apps/pruna/p-video-2-pro-avatar.md): $0.02/sec 480p, $0.04/sec 768p, $0.06/sec 1080p, $0.08/sec 2k, $0.12/sec 4k
- [P-Video-2-Pro-Reference](https://inference.sh/apps/pruna/p-video-2-pro-reference.md): Output: cost $0.015-$0.105/sec, speed $0.02-$0.115/sec. Ref video: $0.03-$0.06/sec. Ref img: $0.005/img. Ref audio: $0.0015-$0.0025/sec

## faq

### how much does Runway Aleph 2.0 cost?

$0.28/sec. you pay per run from your inference shell credits. no subscription is required.

### how do I run Runway Aleph 2.0 via api?

call runway/aleph-2 with the javascript or python sdk, the rest api, or `belt app run runway/aleph-2` from a terminal. the api reference on this page lists every input.

### who makes Runway Aleph 2.0?

Runway Aleph 2.0 is made by runway and runs on inference shell. every runway app is listed at https://inference.sh/apps/runway.

### can my agent use Runway Aleph 2.0?

yes. agents call it as a tool through the inference shell mcp server (https://api.inference.sh/mcp), the belt cli or the sdk, and pay per run like you do.

### what are the alternatives to Runway Aleph 2.0?

other video apps on inference shell include VACE, P-Video-Animate, P-Video-Upscale, P-Video-2-Pro-Avatar. they run through the same api, so switching is a one-line change.