The AI community building the future. Build, train and deploy state of the art models powered by the reference open source in machine learning.
I recall seeing a fee of nine dollars, and there's also an enterprise option priced at twenty dollars per month.
So, it's requires expensive machines to open services or open LLM models.
I recall seeing a fee of nine dollars, and there's also an enterprise option priced at twenty dollars per month.
So, it's requires expensive machines to open services or open LLM models.
Google AI Platform is a managed service that enables you to easily build machine learning models, that work on any type of data, of any size. Create your model with the powerful TensorFlow framework that powers many Google products, from Google Photos to Google Cloud Speech.
The price of the solution is competitive.
For every thousand uses, it is about four and a half euros.
The price of the solution is competitive.
For every thousand uses, it is about four and a half euros.
All the latest open-source 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.
Run leading open-source models like Llama-2 on the fastest inference stack available, up to 3x faster1 than TGI, vLLM, or other inference APIs like Perplexity, Anyscale, or Mosaic ML.
Together Inference is 6x lower cost2 than GPT 3.5 Turbo when using Llama2-13B. Our optimizations bring you the best performance at the lowest cost.
We obsess over system optimization and scaling so you don’t have to. As your application grows, capacity is automatically added to meet your API request volume.
GroqCloud Platform manages large-scale data processing tasks efficiently, making it suitable for AI and machine learning applications. Users appreciate its scalability, speed, and seamless integration capabilities. They value its robust security features, intuitive dashboard, real-time analytics, and efficient workflow automation, while noting the need for better scalability, more robust support, and improved performance optimization.
