When not to use SmartMemory
The jobs SmartMemory is built for, and the ones where the vector store you already run is the better tool.
SmartMemory is built for memory that changes: things an assistant or an agent learns one at a time, links to what it already knows, and corrects later. It is not a bulk loader. If your job looks like one of the cases below, use the vector store or database you already run.
Bulk catalogs reloaded nightly
Every sm add or memory.ingest() call runs the full pipeline for one item: classify, extract, enrich, link and store. In our own timing with no LLM provider configured, a single sm add took about 210 ms. At that rate 2 million rows take about 117 hours on one process. The Python add() call stores an item without extraction, linking or enrichment, but then you get little more than a vector store with a local embedding model, which is what you may already run.
A catalog that is replaced in place every night has no changing beliefs to supersede and no decisions to record. Your database is the source of truth, so keep it there.
Static corpora
A fixed set of documents that you index once and query many times does not need a memory layer. Memory earns its place when facts arrive over time, contradict each other, and need to be traced back to where they came from. For a static corpus, a plain vector index is simpler and cheaper.
A single reader
If one chatbot reads one table, there is nobody to share memory with and nothing to carry between sessions. SmartMemory pays off when several people, agents or sessions need the same memory, or when one assistant must remember across days.
Distributed workers that must share one store
Lite runs on one machine and has no shared store for many workers. For a shared store, use the managed Service. The self-serve plans have memory limits: Free is 10,000 memories, Pro is 100,000, and Team is 100,000 per seat, pooled. See pricing for the current figures. A workload far above those limits is a conversation about Enterprise terms, not a self-serve fit.
Where it does fit
Use SmartMemory when an assistant should remember decisions and preferences across sessions, when agents share what they learn, or when you need to ask why something is believed. Start with the Quickstart.