Supabase's Vector Buckets

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  "detected_at": "2026-01-12T18:27:44+00:00",
  "company": {
    "name": "Supabase",
    "domain": "supabase.com"
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  "data": {
    "url": "https://www.i-programmer.info/news/84-database/18585-supabases-vector-buckets.html",
    "title": "Supabase's Vector Buckets",
    "excerpt": "| Supabase's Vector Buckets |.\n\n| Written by Nikos Vaggalis |\n| Monday, 12 January 2026 |\n| Supabase has released Vector Buckets, specialized storage containers optimized for vector data. This is welcome as it expands your options for storing vectors.\n\nIn Amazon S3 Vectors Or PostgreSQL- Is This The End Of Specialized Vector Stores? we explored the idea that since S3 Vectors take care of your data lakes and PostgreSQL looks after vector and relational data at the same time,\nhad specialized Vector stores reached the end of the road?\n\nAt that time the choice appeared to be one OR the other; pgvector or S3 vectors. Not any more.\n\nHere comes Supabase with a proposition that utilizes both under the same roof. The introduction of Vector Buckets gives you the durability and cost efficiency of Amazon S3 with built-in similarity search. With that, you can now have more options for how you store vectors:\n\n* Use pgvector for smaller, latency-sensitive datasets that belong tightly in your database.\n* Use Vector Buckets when you need to store a large amount of vectors - up to tens of millions - on a durable storage layer with similarity search built in.\n\nDigging into it, the main issue here has to do with latency and real time performance. Therefore it is recommended to store hot vectors in pgvector for the highest-traffic, most latency-sensitive queries and warm or cold vectors in Vector...",
    "product": "Vector Buckets",
    "summary": "Supabase, Inc. launches Vector Buckets.",
    "category": "launches",
    "found_at": "2026-01-12T18:27:44Z",
    "planning": false,
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    "product_data": {
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    "published_at": "2026-01-12T18:27:44Z",
    "article_sentence": "| Supabase has released Vector Buckets, specialized storage containers optimized for vector data."
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curl https://signals.autobound.ai/v1/signals/af515349-4b3f-4ae2-8f14-cc95cd635b14 \
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curl -X POST https://signals.autobound.ai/v1/companies/enrich \
  -H "X-API-KEY: $AUTOBOUND_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"domain":"supabase.com","limit":20}'