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A centralized adapter registry for foundation models

Discover production-ready fine-tunes, test them instantly in the browser, and deploy them to serverless API endpoints in one click.

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191 adapters match your search
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3 steps. Discover, test, and deploy production-ready fine-tunes in minutes.

Discover specialized adapters
Step 01 / Discover

Discover

Find highly specialized adapters for coding, customer support, data extraction, and more, all built by top creators.

A/B test adapters in side-by-side playground
Step 02 / Test

Test

Stop guessing. Use our side-by-side Playground to instantly A/B test an adapter against its base model before you deploy.

One-click deploy to serverless endpoint
Step 03 / Deploy

Deploy

One click to push any adapter to a production-ready, serverless API endpoint. No GPU provisioning required.

Browse a registry of thousands of fine-tunes across a range of model families

Instantly filter, benchmark, and deploy custom adapter weights. Discover task-specific configurations built by top creators or publish your own internal fine-tunes.

Easily deploying endpoints at scale

Router Layer

Dynamic header-based routing matches incoming API requests to adapter registers. Weights swap dynamically inside warm GPU checkpoint clusters in less than 1.2ms, completely eliminating container initialization.

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infrastructure / deployments

Active Endpoints

3 endpoints routing 14 active adapters
Endpoint Base Model Avg Latency Status
api.aptai.dev/v1/chat 8 adapters
Mistral-7B 1.12ms Active
api.aptai.dev/v1/code 2 adapters
CodeLlama-34B 3.45ms Active
api.aptai.dev/v1/extract 4 adapters
Llama-3-8B 0.84ms Active
api.aptai.dev/v1/embeddings 12 adapters
Nomic-Embed 0.45ms Active
api.aptai.dev/v1/vision 1 adapter
Llava-1.5 — Deploying

Manage Endpoints

Manage and monitor your active deployment routes.
/v1/chat
Llama-3-8B • 8 adapters
142 req/s • 1.12ms
/v1/code
Mistral-7B • 2 adapters
24 req/s • 3.45ms
/v1/vision
Llama-3-Vision • 4 adapters
68 req/s • 1.84ms
/v1/embeddings
Nomic-Embed • 12 adapters
890 req/s • 0.85ms
/v1/extract
Qwen-2-7B • 0 adapters
Offline
/v1/translate
Gemma-2B • 3 adapters
51 req/s • 4.10ms
/v1/chat
Llama-3-8B • 8 adapters
142 req/s • 1.12ms
/v1/code
Mistral-7B • 2 adapters
24 req/s • 3.45ms
/v1/vision
Llama-3-Vision • 4 adapters
68 req/s • 1.84ms

Build locally. Scale globally. The drop-in router for agentic workflows.

The drop-in router for agentic workflows. AptAI acts as an OpenAI-compatible proxy. Point your existing frameworks (OpenHands, AutoGen, CrewAI, Aider) directly to your AptAI cloud endpoints or local CLI. We intercept the requests, read the context, and dynamically hot-swap the right adapters into memory in milliseconds. Zero code rewrites required.

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# 1. Point ANY agent framework to your AptAI endpoint client = OpenAI( base_url="https://api.aptai.dev/v1", # Drop-in replacement api_key="apt_live_x89jFks..." ) # 2. Routing works automatically via inline prompt tags response = client.chat.completions.create( model="meta-llama/Llama-4-Scout-17B-16E-Instruct", messages=[{ "role": "user", "content": "@webdev Build a sleek, dark-mode landing page component..." }] )
user — aider — 80x24
user@MacBook-Pro ~/project % aider --openai-api-base https://api.aptai.dev/v1 --openai-api-key apt_live_... --model openai/meta-llama/Llama-4-Scout-17B-16E-Instruct
Aider v0.86.2
Main model: openai/meta-llama/Llama-4-Scout-17B-16E-Instruct via https://api.aptai.dev/v1
Git repo: .git
Repo map: using 1024 tokens
Use /help to see commands, Enter to submit.
>
user — -zsh — 80x24
█████ ██████ ████ ██ ██ ██ ██ ██████ █████ ██████ ███████████████████████████████ ██████ █████ ██ █ ███ ██████ █████ ████
user@MacBook-Pro ~ % aptai top

Get paid for your fine-tunes.

Turn your specialized datasets and domain expertise into recurring revenue. AptAI provides the infrastructure to host, protect, and monetize your custom models. Set your own price per token and let thousands of developers route traffic to your endpoint.

70 / 30 Revenue Share

You keep 70% of all inference revenue generated by your adapter. We handle the billing and infrastructure.

Protect Your IP

We serve your adapter on managed endpoints. Your proprietary .safetensors weights are never exposed for public download.

Flexible Pricing

Set your own price per 1M tokens based on the complexity and value of your fine-tune.

Train in minutes. Serve in milliseconds.

Stop wrestling with complex infrastructure. AptAI utilizes state-of-the-art optimizations to make the entire model lifecycle seamless. By leveraging Unsloth-optimized kernels for rapid fine-tuning, and high-density multi-adapter serving (LoRAX) on scalable serverless clusters, we deliver the performance of dedicated GPUs at a fraction of the cost.

Infrastructure Metric AptAI Serverless Traditional Hosting
Fine-tuning speed 2x-5x faster (Unsloth-optimized) Hours to days
Adapter density 100+ adapters per base model 1 model per dedicated GPU
GPU Memory Usage Shared base VRAM (Dynamic swapping) Duplicated base footprints
Base hosting costs Pay-per-inference $150+ / mo per dedicated GPU
AptAI AptAI
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studio / fine-tuning / create
Unsaved draft

Fine-Tuning Configuration

Advanced mode: Explicit control over adapter matrices, learning rates, and target layers.

Foundation Model

LoRA Hyperparameters

q_proj k_proj v_proj o_proj gate_proj up_proj down_proj

Dataset

82,419 rows
train_data_v2.jsonl
Max Sequence Length 4096
Pack Sequences

Est. Cost & Time

Hardware 1x H100
Est. Time ~ 34 mins
Total $ 2.45
Run: adpt-llama-8b-vx
Step 120 / 1000
Est. 00:34:00 remaining
Train Loss
0.8423
Eval Loss
0.9104
Learning Rate
1.8e-4
Gradient Norm
2.41
Throughput
14k
GPU VRAM
64.2
Live Adapter Evaluation
Previewing responses with current adapter weights
Updating every 50 steps
"Explain quantum computing in one sentence."
Quantum computing harnesses the laws of quantum mechanics to solve problems too complex for classical computers. It uses qubits which can exist in multiple states simultaneously, allowing for exponential scaling in computational power for specific tasks like prime factorization and complex system simulation.

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