Hugging Face vs Replicate
Both are ai tools. Here is what separates them.
| Hugging Face | Replicate | |
|---|---|---|
| Category | AI | AI |
| Pricing model | freemium | paid |
| Price from | Freemium | Paid |
| Upvotes | 8 | 12 |
| What it does | The model hub. Weights, datasets and demos for most of open-source ML. | Run open-source models behind an API without touching a GPU. |
What Hugging Face is
Hugging Face is a hosting platform for machine learning work: model weights, datasets and runnable demos, held in repositories on a public hub. Using the Hub on the free tier costs nothing. A PRO account is $9 per month, a Team seat $20 per user per month and an Enterprise seat $50 per user per month. Storage is where the tiers separate. A free user or organisation gets 100GB of private storage. PRO raises that to 1TB private alongside up to 10TB public, a Team organisation starts at 12TB public plus 1TB per seat, and Enterprise at 200TB plus 1TB per seat. Beyond the allowance, public storage is $12 per TB per month and private storage $18, with the private rate tapering to $16 above 50TB, $14 above 200TB and $12 above 500TB. Hard limits apply on any plan: no single file may exceed 500GB, the vendor recommends splitting into chunks under 200GB, a single folder cannot hold more than 10,000 files, and the recommended ceiling is under 100,000 files per repository. Spaces host demos on rented hardware. CPU Basic is free, a CPU upgrade is $0.03 per hour, an NVIDIA T4 small $0.40 and an NVIDIA L4 $0.80. ZeroGPU is the alternative for intermittent workloads: it works only with the Gradio SDK, and a decorated function defaults to a 60 second maximum runtime unless a longer duration is declared. Daily ZeroGPU quota is 2 minutes unauthenticated, 5 minutes on a free account, 40 minutes for PRO and Team members and 60 minutes for Enterprise members, after which usage is billed against pre-paid credits at $1 per 10 minutes of GPU time. The large size is backed by half an NVIDIA RTX Pro 6000 Blackwell with 48GB of VRAM and xlarge by the full card with 96GB. Inference Providers routes API calls out to third-party hosts including Baseten, Cerebras, Cohere, DeepInfra, Fal AI, Fireworks, Groq, Novita, Replicate, Scaleway, Together and Z.ai. The router exposes an OpenAI compatible endpoint at router.huggingface.co/v1, and appending a fastest, cheapest or preferred suffix to a model id changes which provider serves the request. Provider rates are passed through without a markup. Monthly credit is $0.10 for free users, $2.00 for PRO and $2.00 per seat for Team and Enterprise organisations, and a request made with your own provider key is billed by that provider with the credits not applying. Enterprise adds identity provider connection over SAML or OIDC, SCIM user provisioning, audit logs and the ability to pin repository data to a US or EU region. Hugging Face holds SOC 2 Type II certification and states GDPR compliance.
What Replicate is
Replicate runs machine learning models behind a cloud API, so the user never provisions or manages a GPU. Models are packaged with Cog, an open source tool that wraps a model in a container with a generated API server. A deployment scales with demand and scales down to zero when it is idle. The HTTP API sits at https://api.replicate.com/v1 and authenticates with an API token sent in the Authorization header using the Bearer scheme. Endpoints cover predictions, models, model versions, deployments, trainings, hardware and account, and a running prediction or training can be stopped through a dedicated cancel endpoint. Webhooks give asynchronous completion notifications and streaming output is supported. Client libraries are published for Python and Node.js alongside the plain HTTP API. Creating a prediction is rate limited to 600 requests per minute and every other endpoint to 3,000, with an HTTP 429 returned past either limit. Fine tuning is done by calling the trainings API with a base model and a training dataset, which produces a new deployable model version. Model output files are served from the replicate.delivery domain and its subdomains, and prediction inputs, outputs and logs created through the API are deleted after one hour by default. Billing is per second of compute at a rate set by the hardware chosen. Standard CPU compute is $0.0001 per second, or $0.36 an hour, an Nvidia T4 is $0.000225 or $0.81 an hour, an Nvidia L40S is $0.000975 or $3.51 an hour, and a single Nvidia H100 is $0.001525 or $5.49 an hour. An Nvidia A100 with 80GB of memory costs $5.04 an hour, and an eight way H100 configuration costs $0.0122 per second, which works out at $43.92 an hour. Some models are priced per unit of output instead: Flux 1.1 Pro at $0.04 per generated image, Flux Schnell at $3.00 per thousand images, and Wan 2.1 image to video at 720p at $0.25 per second of output. Private models are billed for their entire running time, including boot and idle, while fast booting fine tunes are billed only for active processing.
We are not affiliated with either product. Upvote counts come from the published formula and neither position was paid for.