LLMs, APIs, hosting Β· 36 tools
Models and platforms are the substrate everything else runs on. Choosing the right model, API, or hosting affects cost, latency, quality, and compliance β and the trade-offs shift every few months.
For prototyping, managed APIs are fastest; for scale or privacy, self-hosting or private deployment may win. Compare on the metrics that matter to your use case: quality, speed, price, and data residency.
Ranked by recommendation index and composite score. The full ranking is on the AI Models & Platforms Rankings page.
Developer console and API keys for the Claude model family
Node-based local interface for running image and video diffusion models
Browser playground for prompting and prototyping with Gemini models
Hosted Jupyter notebooks with optional free GPU and TPU runtimes
Where most of the open machine learning world lives
Datasets, competitions and hosted notebooks for machine learning
Open-source framework for building applications on language models
Runs open-weight language models locally behind a local API
API access to OpenAI models for text, image, audio and embeddings
Image generation models offered as hosted API and open weights
Managed access to foundation models from several providers on AWS
Fast inference API served from wafer-scale AI hardware
Community library of image models with browser-based generation
Enterprise language models for generation, embeddings and reranking
Serverless GPU endpoints for image, video and audio generation
Inference and fine-tuning platform for open-weight models
Google Cloud platform for training, tuning and serving models
Python library for building quick web demos of ML models
Inference API running open models on custom LPU hardware
Desktop app for running local LLMs with a chat UI and API
Framework for connecting LLMs to your own documents and data
Microsoft platform for building, evaluating and deploying AI apps
European model lab offering open weights and a hosted API
Single API that routes requests across many model providers
Managed vector database for semantic search and RAG
Run open-source models through a hosted API with one call
Rental GPU cloud for training, fine-tuning and inference
Hosted inference and GPU clusters for open-source models
Open-source vector database with hybrid search built in
Experiment tracking and model monitoring for ML teams
C/C++ inference engine for running LLMs on ordinary hardware
High-throughput serving engine for open-weight language models
Developer platform for Jurassic and Jamba language models
Deploy custom models and AI workloads from your own code
Low-cost serverless inference for open models
Serverless cloud for running Python code on demand GPUs
Managed is faster to start; self-host helps with privacy, cost at scale, and customization.
Match the model to your task and compare on the metrics you care about, not just leaderboard scores.
Review each provider's data-use and retention policy; use enterprise tiers for sensitive data.