Compare OpenPipe vs. dstack

OpenPipe offers an efficient platform for developers to fine-tune their models. It allows you to keep your datasets, models, and evaluations organized in a single location. You can train new models effortlessly with just a click. The system automatically logs all LLM requests and responses for easy reference. You can create datasets from the data you've captured, and even train multiple base models using the same dataset simultaneously. Our managed endpoints are designed to handle millions of requests seamlessly. Additionally, you can write evaluations and compare the outputs of different models side by side for better insights. A few simple lines of code can get you started; just swap out your Python or Javascript OpenAI SDK with an OpenPipe API key. Enhance the searchability of your data by using custom tags. Notably, smaller specialized models are significantly cheaper to operate compared to large multipurpose LLMs. Transitioning from prompts to models can be achieved in minutes instead of weeks. Our fine-tuned Mistral and Llama 2 models routinely exceed the performance of GPT-4-1106-Turbo, while also being more cost-effective. With a commitment to open-source, we provide access to many of the base models we utilize. When you fine-tune Mistral and Llama 2, you maintain ownership of your weights and can download them whenever needed. Embrace the future of model training and deployment with OpenPipe's comprehensive tools and features.

It enhances the efficiency of both development and deployment processes, cuts down on cloud expenses, and liberates users from being tied to a specific vendor. You can set up the required hardware resources, including GPU and memory, and choose between spot instances or on-demand options. dstack streamlines the entire process by automatically provisioning cloud resources, retrieving your code, and ensuring secure access through port forwarding. You can conveniently utilize your local desktop IDE to access the cloud development environment. Specify the hardware configurations you need, such as GPU and memory, while indicating your preference for instance types. dstack handles resource provisioning and port forwarding automatically for a seamless experience. You can pre-train and fine-tune advanced models easily and affordably in any cloud infrastructure. With dstack, cloud resources are provisioned based on your specifications, allowing you to access data and manage output artifacts using either declarative configuration or the Python SDK, thus simplifying the entire workflow. This flexibility significantly enhances productivity and reduces overhead in cloud-based projects.