company
Vendor of a managed vector database, sold as a hosted API for storing embeddings and running nearest-neighbour queries over them.
No evidence-backed relationships are recorded.
Password-reset queries made up 38% of one support RAG bot's vector lookups, says a dev.to write-up that moves retrieval behind an HTTP cache. Any saving depends on identical queries repeating within one tenant inside five minutes.
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A dev.to walkthrough replaces a managed vector store and a hosted embedding API with sqlite-vec and a local model on port 11434. Its schema declares float[768], so changing model dimension means re-embedding everything.
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A dev.to post argues that moving embeddings out of Postgres buys a second source of truth, a four-leg query path and 50 to 150 ms of internet latency. Its 8 ms pgvector counter-figure has no benchmark behind it.
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A September 2026 comparison pins the workload at 10 million 1536-dimension vectors and reports Qdrant between $250 and $947 a month, while Pinecone's serverless bill is set by the gigabytes each query scans.
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A dev.to design guide moves JSON documents and vector search into one PostgreSQL instance, and the evidence it offers is operational: deleted ETL pipelines and sync bugs, with no query timings on either side.
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Mem0-style memory replaces embed-and-store with extraction, write-time retrieval, an ADD/UPDATE/DELETE/NOOP decision and consolidation. The similarity scores in the dev.to writeup show why no threshold can make that call for you.
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A consultant's two client failures, a supplier filter and a 10kg shipping quote, both broke on a numeric comparison that top-k similarity does not perform.
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A practitioner's account of a healthcare pipeline puts the dominant cost in orchestration, not inference. His own numbers say the rate limit and the SLA are queueing problems, not capacity ones.
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A dev.to post argues the layers arrived before the problems. Its strongest evidence, grep beating an embedding pipeline, comes with no name and no numbers, which is the part worth testing yourself.
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A dev.to writeup drops vector stores for a SQLite table with tag and timestamp columns. Its own numbers put the practical ceiling at a million rows, not a hundred million.
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A dev.to account of retiring Mongo, Redis, Elasticsearch and a vector store makes its strongest argument on consistency rather than cost, and it arrives without a single figure.
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A dev.to writeup argues tuned SQLite replaces pgvector or Pinecone on a $5 VPS. The connection discipline is the real lesson; the headline count of millions of vectors is not evidenced.
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Vector search ranks by meaning, so literal tokens like part numbers and ticket IDs fall just outside the top results. The fix is lexical plus dense retrieval, not a model upgrade.
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A dev.to writeup drops LangChain, LlamaIndex, hosted vector stores and the cloud model, leaving six files, seven npm packages, and one cloud dependency still in place.
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HNSW indexing runs inside the SQL engine, so there is no second service and no network hop. There is also no index persistence unless you turn on an experimental flag.
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