> ## Documentation Index > Fetch the complete documentation index at: https://docs.conare.ai/llms.txt > Use this file to discover all available pages before exploring further. # Embeddings > Retrieval embeddings from the same model Conare indexes with — served as a hosted API today, or fine-tuned on your corpus and evaluation-gated. Most teams use a generic embedding API and hope it understands their domain. Conare's position is different: **the embedding model is part of the retrieval stack, not a commodity input** — and the strongest version of it is one trained on your own data. Conare Embeddings comes in two layers: `POST /api/v1/embeddings` — document-space vectors from the exact self-hosted model that indexes and retrieves Conare memories. Available today on every plan. An embedding model fine-tuned on your data, acceptance-gated on your real queries, served with its own reranker. Sales-led. ## Why same-model vectors matter Retrieval quality degrades silently when the vectors in your index and the vectors at query time come from different representations. The hosted API returns vectors from **the same model Conare retrieves with**, which makes it the right choice when: * you're building your own vector features (clustering, dedup, similarity heuristics) next to Conare memory or [ConareDB](/db/overview), and the representations must agree; * you want [model pinning](/embeddings/api#model-pinning) semantics — an explicit guarantee that vectors from a different model are never silently substituted into your index. ## Why custom models exist A generic model spreads its capacity across every domain on the internet. Your queries live in one domain — your product's vocabulary, your customers' phrasing, your documents' structure. Fine-tuning on that distribution reliably moves the metrics that matter (recall on your real queries, ranking quality, knowing when there is no answer) — and Conare only ships a custom model when it **provably beats the incumbent on your own evaluation set**, never on faith. Read [how the engagement works](/embeddings/custom-models).