Language model applications (LLMs) are released frequently, each promising its own specific speed, cost, and quality advantages. But selecting the best model can be challenging due to the constant influx of new options, manual signups and custom benchmarks, and for some, the output quality and speed are less than satisfactory. In response to these constraints, a new tool called Unify has been developed.
Unify, developed by AI startup, provides a dynamic solution. It offers a single API access to a wide variety of LLMs and compares their performance. By adjusting the user’s preferences for speed, cost, and quality, the tool can automatically find and route to the most suitable model. This feature significantly reduces the time-consuming task of researching and merging separate LLMs.
Unify has a variety of beneficial features. Users can control data routing by selecting specific models and providers, and adjusting parameters such as latency, cost, and quality. As new models are added over time, the application will be automatically enhanced. Users can also compare the performance of different models and service providers according to their specific requirements. Unify ensures fairness by avoiding biased measures of speed, cost, or quality in comparing the models.
Furthermore, Unify integrates all models and providers behind a single endpoint with only one API key, simplifying access and querying. Developers can hence focus on building superior LLM products without having to worry about keeping the models and providers up-to-date. Unify takes care of that.
Unify users can register an account to access all models from all supported providers using a single API key, and the costs align to standard API fees, with one credit equal to one dollar. In addition, new users receive $50 in free credits. Unify’s router operates by balancing throughput speed, cost, and quality according to individual user preferences.
In essence, Unify helps developers focus on creating innovative applications by streamlining the access and selection process for LLMs. With its user-friendly and dynamic comparison engine, developers have the ability to compare aspects such as price, processing speed, and quality of output in order to choose the most suitable LLM for their projects. This could vary from designing unique text formats, translating languages accurately, or creating innovative content.
