The Together Embeddings endpoint offers higher accuracy, longer context, and lower cost than other popular platforms, with 8 leading embedding models available, including top-performing models from the MTEB leaderboard. The endpoint also supports state-of-the-art long-context M2-Retrieval models up to 32k context length, making it suitable for applications such as retrieval augmented generation (RAG), which aims to overcome limitations of generative AI models by finding relevant information from a given knowledge base through embeddings and providing the information to a generative model. The endpoint is fully OpenAI compatible, allowing developers to easily switch between platforms, and offers competitive pricing, with some models up to 4x cheaper than others. It also provides integrations with popular frameworks such as MongoDB, LangChain, and LlamaIndex for RAG, making it an attractive option for building successful AI applications.