Modal vs Replicate for ML Inference

When you need to run ML models in production without managing your own GPU infrastructure, Modal vs Replicate is one of the first decisions you'll face. Both platforms let you deploy models and call them via API, but they're built for different use cases and different teams.

This comparison covers what actually matters for AI product teams: cold start performance, custom model support, pricing at scale, and the developer experience of shipping real workloads.

What Each Platform Is For

Modal is a serverless compute platform for Python workloads that happen to run on GPUs. You write Python functions, decorate them with @app.function(gpu="A100"), and Modal handles containerization, GPU provisioning, and scaling. It's a general-purpose serverless platform with excellent GPU support — not specifically an ML inference service.

Replicate is a model hosting marketplace and inference API. It has a library of hundreds of pre-packaged models (Stable Diffusion, Whisper, LLaMA variants, SDXL, and more) that you can call via a simple API. You can also deploy custom models using the Cog packaging format.

The key distinction: Replicate is optimized for consuming existing models quickly. Modal is optimized for running any Python code on GPUs, including custom training, preprocessing, inference, and everything in between.

Cold Start Performance

Cold starts are the Achilles heel of serverless GPU platforms. When no instance is warm, your request has to provision a GPU, load the container, and load the model weights before returning a response. For user-facing applications, this matters enormously.

Modal:

Cold starts typically 2–8 seconds depending on image size and model load time
Supports keep_warm=N to maintain N warm instances, eliminating cold starts for production traffic
Memory snapshot feature: pre-load model weights into a memory snapshot so "cold" starts skip weight loading entirely — reducing effective cold start to 1–2 seconds
You control the container image, so a lean image means a faster cold start

Replicate:

Cold starts on popular models are often 10–30 seconds; less popular models can be 60+ seconds
Replicate's "Deployments" product lets you configure minimum instances for warm keep-alive — but this costs as much as reserved instances
For community models (the free tier), there's no SLA on cold start; you're sharing capacity with everyone else

For user-facing applications where latency matters, Modal's memory snapshot feature and keep_warm are meaningful advantages.

Custom Models and Flexibility

Modal: This is where Modal shines. You can run literally any Python code — custom model architectures, multiple GPU workers, pre/post-processing pipelines, fine-tuning jobs, data preprocessing, and inference in the same platform. If you've trained a custom model that isn't a standard HuggingFace architecture, Modal handles it without any special packaging. You can also mount cloud storage (S3, GCS) for large model weights and share them across instances.

Replicate: Custom model deployment requires packaging your model with Cog, Replicate's open-source model packaging tool. Cog generates a Docker container from a cog.yaml config. It works well for standard inference use cases but adds friction for custom architectures, unusual dependencies, or non-standard preprocessing. If your model fits the standard "input → model → output" pattern, Cog is straightforward. If you need a multi-stage pipeline with intermediate caching, Modal's general Python execution model is simpler to work with.

Pricing Model

Both platforms charge for GPU compute time, but the pricing structure differs in important ways.

Modal pricing:

Pay per second of GPU compute: A10G at ~$0.000306/second (~$1.10/hour), A100 40GB at ~$0.000583/second (~$2.10/hour)
No charge when idle (true serverless)
Minimum charge per invocation is very low (millisecond granularity)
Free tier: $30/month of compute credits

Replicate pricing:

Community models: priced per prediction (varies by model and hardware)
Custom model deployments: charged per second, similar to Modal GPU rates
Replicate takes a margin on community model predictions since they're running shared infrastructure
Private model deployments require their paid Deployments product

For high-volume inference of community models, Replicate's per-prediction pricing on shared hardware can be cost-effective at low scale. At high volume, Modal's direct GPU-second billing with no platform margin becomes cheaper.

The Developer Experience

Modal:

import modal

app = modal.App("my-inference-app")
image = modal.Image.debian_slim().pip_install("transformers", "torch")

@app.function(gpu="A10G", image=image)
def run_inference(prompt: str) -> str:
    from transformers import pipeline
    pipe = pipeline("text-generation", model="mistralai/Mistral-7B-v0.1")
    return pipe(prompt)[0]["generated_text"]

You write Python, run modal deploy, and it's an API. The DX is excellent for Python engineers — no YAML configs, no Docker knowledge required (though you can use it if you want). Local development feels like local development; you can run Modal functions locally and in the cloud with the same code.

Replicate:

import replicate

output = replicate.run(
    "stability-ai/sdxl:version-hash",
    input={"prompt": "A photo of a mountain at sunset"}
)

Replicate's DX for consuming existing models is hard to beat — a few lines of Python to call any model in their library. For rapid prototyping with community models, this is genuinely fast. The complexity arrives when you need to deploy and manage your own custom models.

When to Choose Modal

You're running custom models or custom architectures not available on Replicate
You need fine-grained control over the execution environment (specific CUDA versions, custom dependencies)
You're building pipelines that combine inference with other compute (preprocessing, postprocessing, training jobs)
You want predictable GPU-second billing without platform margin
Your team writes Python and wants the infrastructure to feel like Python

When to Choose Replicate

You're prototyping with existing open-source models (image generation, speech-to-text, text-to-image)
You want the fastest path to calling a model with minimal infrastructure setup
Your team is non-technical on the infrastructure side and needs a simple API
You don't have custom model requirements

Comparison Summary

| | Modal | Replicate | |---|---|---| | Best for | Custom models, production inference, pipelines | Existing OSS models, rapid prototyping | | Cold start | 1–8s (with memory snapshots) | 10–60s (community); better with Deployments | | Custom models | Any Python code | Requires Cog packaging | | Pricing | GPU-seconds, no platform margin | Per-prediction + Deployment tiers | | DX | Python-native, excellent for engineers | API-first, great for quick integration | | Scaling | Manual keep_warm or autoscale | Managed autoscale (Deployments) |

For AI product teams building production systems with custom or fine-tuned models, Modal tends to win on flexibility and cost at scale. For rapid prototyping and standard model access, Replicate removes more friction upfront. Many teams start with Replicate for validation, then move to Modal as they productionize.

This infrastructure decision often comes alongside choices about your overall deployment stack — see our Vercel vs AWS comparison for how inference hosting fits into the broader picture. And if you're deciding whether to build custom models at all, our build vs buy AI MVP guide covers the tradeoffs.

Deploying ML models and not sure which platform to use? Our team has shipped on both. Book a 15-min scope call →

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Modal vs Replicate for ML Inference