Pinecone’s integrated inference capabilities streamline the process of generating vector embeddings, eliminating the need for complex setups like hosting models or provisioning servers, by allowing users to index and query data with a single API call. Although this feature simplifies embedding pipelines, it can lead to challenges when large amounts of metadata are involved, as encountered by a customer who exceeded the 40KB metadata size limit due to automatic inclusion of text fields by the upsert_records() method. The solution involved using Pinecone's Inference API directly, which offers more control over metadata by allowing embeddings to be generated without automatically adding text fields. This approach requires an extra step but avoids metadata bloat and size errors, making it suitable for large-scale workloads. Pinecone's integrated inference thus offers a choice between ease of use with upsert_records() for quick setups and the flexibility of the Inference API for managing complex metadata, all while removing the burden of model hosting and scaling.