Generated image: an abstract painting of a sunriseGenerated image: an illustration of a dog jumpingGenerated image: a futuristic robot looking into the distance

Run AI
with an API.

Build and scale AI products. Run and fine-tune models. Deploy custom models. All with one line of code.

Get started for free

import Replicate from
"replicate"
;
const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN
})
const model = 
const input = {
  prompt: 
};
const [output] = await replicate.run(model, { input });
console.log(output);

With Replicate you can

fermatresearch
meta
laion-ai
vaibhavs10
prompthero
stability-ai
google-research
jbilcke
microsoft
tomasmcm
pollinations
pixray
tstramer
mcai
pagebrain
lucataco
zylim0702

Thousands of models contributed by our community

All the latest models are on Replicate. They’re not just demos — they all actually work and have production-ready APIs.

AI shouldn’t be locked up inside academic papers and demos. Make it real by pushing it to Replicate.

Explore models
Push a model

How it works

You can get started with any model with just one line of code. But as you do more complex things, you can fine-tune models or deploy your own custom code.

Run models

Our community has already published thousands of models that are ready to use in production. You can run these with one line of code.

Explore models
import replicate

output = replicate.run(
  "black-forest-labs/flux-dev",
  input={
    "aspect_ratio": "1:1",
    "num_outputs": 1,
    "output_format": "jpg",
    "output_quality": 80,
    "prompt": "An astronaut riding a rainbow unicorn, cinematic, dramatic",
  }
)

print(output)

Fine-tune models with your own data

You can improve models with your own data to create new models that are better suited to specific tasks.

Image models like SDXL can generate images of a particular person, object, or style.

Fine-tune image models

Train a model:

training = replicate.trainings.create(
  destination="mattrothenberg/drone-art"
  version="ostris/flux-dev-lora-trainer:e440909d3512c31646ee2e0c7d6f6f4923224863a6a10c494606e79fb5844497",
  input={
    "steps": 1000,
    "input_images":
https://example.com/images.zip
,
    "trigger_word": "TOK",
  },
)

This will result in a new model:

drone-art

mattrothenberg/drone-art

Fantastical images of drones on land and in the sky

0 runs

Then, you can run it with one line of code:

output = replicate.run(
  "mattrothenberg/drone-art:abcde1234...",
  input={"prompt": "a photo of TOK forming a rainbow in the sky"}),
)

Deploy custom models

You aren’t limited to the models on Replicate: you can deploy your own custom models using Cog, our open-source tool for packaging machine learning models.

Cog takes care of generating an API server and deploying it on a big cluster in the cloud. We scale up and down to handle demand, and you only pay for the compute that you use.

Learn more

First, define the environment your model runs in with cog.yaml:

build:
  gpu: true
  system_packages:
    - "libgl1-mesa-glx"
    - "libglib2.0-0"
  python_version: "3.10"
  python_packages:
    - "torch==1.13.1"
predict: "predict.py:Predictor"

Next, define how predictions are run on your model with predict.py:

from cog import BasePredictor, Input, Path
import torch

class Predictor(BasePredictor):
  def setup(self):
      """Load the model into memory to make running multiple predictions efficient"""
      self.model = torch.load("./weights.pth")

  # The arguments and types the model takes as input
  def predict(self,
        image: Path = Input(description="Grayscale input image")
  ) -> Path:
      """Run a single prediction on the model"""
      processed_image = preprocess(image)
      output = self.model(processed_image)
      return postprocess(output)

Scale on Replicate

Thousands of businesses are building their AI products on Replicate. Your team can deploy an AI feature in a day and scale to millions of users, without having to be machine learning experts.

Learn more about our enterprise plans
BuzzfeedLabelboxUnsplashCharacter.aiPhoto.aiTavusHeadshot ProMagnific

Automatic scale

If you get a ton of traffic, Replicate scales up automatically to handle the demand. If you don't get any traffic, we scale down to zero and don't charge you a thing.

  • CPU$0.000100/sec
  • Nvidia T4 GPU$0.000225/sec
  • Nvidia L40S GPU$0.000975/sec
  • 2x Nvidia L40S GPU$0.001950/sec
  • Nvidia A100 (80GB) GPU$0.001400/sec
  • 8x Nvidia A100 (80GB) GPU$0.011200/sec
  • Learn more about pricing

Pay for what you use

Replicate only bills you for how long your code is running. You don't pay for expensive GPUs when you're not using them.

Abstract square illustration

Forget about infrastructure

Deploying machine learning models at scale is hard. If you've tried, you know. API servers, weird dependencies, enormous model weights, CUDA, GPUs, batching.

Prediction throughput (requests per second)

Logging & monitoring

Metrics let you keep an eye on how your models are performing, and logs let you zoom in on particular predictions to debug how your model is behaving.

Imagine what you can build

With Replicate and tools like Next.js and Vercel, you can wake up with an idea and watch it hit the front page of Hacker News by the time you go to bed.

Get started