A Weaviate employee announced the availability of a 'free forever' option for their cloud service, replacing the previous 14-day trial sandbox.

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  "detected_at": "2026-09-04T10:46:28+00:00",
  "company": {
    "name": "Weaviate",
    "domain": "weaviate.io"
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    "post_id": "1w70wby",
    "summary": "A Weaviate employee announced the availability of a 'free forever' option for their cloud service, replacing the previous 14-day trial sandbox.",
    "category": "packaging",
    "comments": [
      {
        "url": "https://www.reddit.com/r/vectordatabase/comments/1w70wby/comment/p7r5vtm/",
        "depth": 0,
        "score": 5,
        "author": "tenequm",
        "excerpt": "Small dataset and simple task - sqlvec.\n\n More complex stuff - Lancedb.",
        "posted_at": "2026-09-04T10:50:00.000Z",
        "author_url": "https://www.reddit.com/user/tenequm/"
      },
      {
        "url": "https://www.reddit.com/r/vectordatabase/comments/1w70wby/comment/p7snn08/",
        "depth": 0,
        "score": 2,
        "author": "BosonCollider",
        "excerpt": "Postgres by default, bring in pgvectorscale or vectorchord if plain pgvector isn't enough",
        "posted_at": "2026-09-04T15:24:32.000Z",
        "author_url": "https://www.reddit.com/user/BosonCollider/"
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      {
        "url": "https://www.reddit.com/r/vectordatabase/comments/1w70wby/comment/p8df510/",
        "depth": 0,
        "score": 2,
        "author": "Einar_Son_of_Bjorn",
        "excerpt": "The grouping is the useful part: embedded library vs self-hosted server vs metered cloud.\n\n For a small RAG app those are different products, not different free tiers of the same thing.If the embeddings sit next to the rows the agent already queries (user, session, doc chunk, metadata filters), I would not start with a dedicated store. pgvector is the common answer on Postgres for exactly that rea",
        "posted_at": "2026-09-07T15:50:17.000Z",
        "author_url": "https://www.reddit.com/user/Einar_Son_of_Bjorn/"
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      {
        "url": "https://www.reddit.com/r/vectordatabase/comments/1w70wby/comment/p7tpvyg/",
        "depth": 0,
        "score": 1,
        "author": "DudaFromWeaviate",
        "excerpt": "Hi there! Duda from Weaviate here :)\nWe now have a free forever option.\n\n Check it out: https://weaviate.io/pricing",
        "posted_at": "2026-09-04T18:10:05.000Z",
        "author_url": "https://www.reddit.com/user/DudaFromWeaviate/"
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        "author": "Talmondrlm",
        "excerpt": "github.com/remade-with-rust/spacedb\n\n This is my go to for my mata project. Cloud/Local DB for building apps, but also setup for sharding with distributed clouds in the future.\n\n https://iamhuman.mata.network/farmer_timmm/23132ENt9jhCLxpLxab6Ww4FRXr3DgBaqiMC3KugZMwM",
        "posted_at": "2026-09-04T18:35:49.000Z",
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        "author": "drrtuy-b",
        "excerpt": "PG makes sense if you deliberately wants to go with a slower vector index implementation or very much into PG SQL dialect already. MariaDB Vector Index can offer better speed according to Mark Callaghan's results here:\nhttps://smalldatum.blogspot.com/2025/01/evaluating-vector-indexes-in-mariadb.html\nHere are some notes about MariaDB Vector in general:\nhttps://mariadb.org/projects/mariadb-vector/",
        "posted_at": "2026-09-05T09:27:45.000Z",
        "author_url": "https://www.reddit.com/user/drrtuy-b/"
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        "depth": 0,
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        "author": "DawgBawb",
        "excerpt": "Big fan of Lance. You can do the local option and then later on go hosted if you want something cloudy. It's cheap (free), it's fast, it's lightweight. You can use sql through duckdb.\n\n I like it so much that i created a database tool for it at github",
        "posted_at": "2026-09-06T14:31:42.000Z",
        "author_url": "https://www.reddit.com/user/DawgBawb/"
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        "depth": 0,
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        "author": "inkaliyuga",
        "excerpt": "Amazing use",
        "posted_at": "2026-09-22T05:44:11.000Z",
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      "[comment u/DudaFromWeaviate] Hi there! Duda from Weaviate here :) We now have a free forever option."
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    "post_date": "2026-09-04T10:46:28.000Z",
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    "post_text": "I’ve been comparing free vector databases for AI agent and RAG projects and have realized the free-tier label hides many differences. It helps to group them by model first.\n\nLibraries like Chroma and LanceDB have no service to meter, so the limits are really your own disk and infrastructure. Open-source servers like Qdrant, Weaviate, Milvus, and pgvector are uncapped when you self-host and only metered when you use a managed version. Pinecone is managed only. The Starter plan gives you 2GB of index storage, five indexes, 2M write units, and 1M read units a month, all in us-east-1. There's no self-hosted option at all, so if data can't leave your infra, it's out of the picture before storage enters the full picture.\n\nFrom that angle, the trade-offs start to make more sense. Weaviate Cloud sandbox is great for quick experiments but expires after 14 days. Zilliz free gives you up to 2 collections and 1M vectors, which is enough for many prototypes. Quadrant Cloud is free with 1 GB RAM, 4 GB disk, and a single node works well up to roughly 1M vectors at 768d before you feel the limits. Pinecone is fully managed, so it’s a good fit if you’re okay staying in their cloud and don’t need self-hosting.\n\nA couple of practical notes I ran into. With Chroma, deletes don’t shrink the HNSW index, so heavy write-and-evict workloads can lead to extra compaction work down the line. With...",
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    "event_date": "2026-09",
    "post_title": "What’s your go‑to free vector DB for AI agent projects right now?",
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curl https://signals.autobound.ai/v1/signals/41ac2802-a162-546e-adaf-652062bb424b \
  -H "X-API-KEY: $AUTOBOUND_API_KEY"

curl -X POST https://signals.autobound.ai/v1/companies/enrich \
  -H "X-API-KEY: $AUTOBOUND_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"domain":"weaviate.io","limit":20}'