Faiss is a collection of algorithms for in-memory exact and approximate high-dimensional (e.g., > ~30 dimensional) dense vector k-nearest neighbor, it doesn't add or really consider persistence (beyond full index serialization to an in memory or on disk binary blob), fault tolerance, replication, domain-specific autotuning and the like. The "vector database" companies like Pinecone, Weviate, Zilliz and what not will add these other features to turn them into a complete service, they're not really the same. pgvector seems to be DB-backed IndexFlat and IndexIVFFlat (?) from the Faiss library at present but is of course not a complete service.
However which kind of approximate indexing you want to use very much depends upon the data you're indexing, and where in the tradeoff space between latency, throughput, encoding accuracy, NN recall and memory/disk consumption you want to be (these are the fundamental tradeoffs in the vector search domain), and whether you are performing batched queries or not. To access the full range of tradeoffs you'd need to use all of the options which are available in Faiss or similar low-level libraries which may be difficult to use or require knowledge of underlying algorithms.
(I'm the author of the GPU half of Faiss)
I'm shocked.