How Much Are Free Vector Database Credits Worth?
Free vector database credits range from $300 evaluation trials up to $100,000 grants, and because the category is crowded with well funded competitors, the offers are unusually generous relative to what the infrastructure actually costs.
Vector search is one of the few AI infrastructure bills a startup can cover almost entirely with credits in its first year. There are more vendors chasing the same buyers than the market can support, and that competition shows up as free credits.
AI Perks tracks the current terms across the category alongside $7.7M in credits from 194 companies.
| Provider | Free credits | What the credits buy |
|---|---|---|
| Weaviate | Up to $100,000 | Managed vector and hybrid search on Weaviate Cloud |
| Neon | Up to $100,000 | Serverless Postgres, vector search via pgvector |
| MongoDB Atlas | Up to $25,000 | Document store plus vector and full text search |
| Redis Cloud | Up to $10,000 | In-memory store with vector search built in |
| Elastic Cloud | Up to $5,000 | Search, log analytics and vector retrieval |
| Qdrant Cloud | $2,000 | Managed hosting for the open source Qdrant engine |
| Pinecone | $300 | Full feature evaluation trial |
| Supabase | $300 | Postgres with pgvector, plus auth and storage |
Headline total across the category: more than $240,000.
Those numbers are not directly comparable. A large grant with a provider whose list prices are high can buy fewer stored vectors than a smaller grant somewhere cheaper. Read the figures as months of runway, not as value.
What a Vector Database Is Actually For
A vector database stores embeddings and answers one question quickly: what in my corpus is most similar to this? That is the retrieval half of every RAG system, semantic search feature and recommendation engine.
A language model knows nothing about your documents, your tickets or your product catalogue. Retrieval is the mechanism that puts your data in front of the model at query time, and the vector database is where that retrieval happens.
Three workloads account for most real usage:
Retrieval augmented generation. You chunk documents, embed them, and at query time fetch the handful of chunks most relevant to the user's question so the model answers from your data instead of guessing.
Semantic and hybrid search. Users search by meaning rather than exact keywords. Hybrid search blends that with traditional keyword matching, which matters more than most teams expect once real users type product codes and proper nouns.
Deduplication, clustering and recommendations. Similarity search is the same primitive whether you are matching a question to a document or a user to a product.
The uncomfortable truth is that none of these need a dedicated vector database at small scale. A few thousand vectors fit in memory and brute force similarity is fast enough. The database earns its cost somewhere past that point.
How Vector Database Costs Behave at Scale
Vector database pricing is driven mainly by how much index sits resident in memory, not by how many queries you run, which makes the bill a standing monthly floor rather than a usage curve that follows your traffic.
This is the single biggest difference from model API billing, where a quiet week costs you nothing. A vector index costs the same on a quiet week.
Three variables drive the floor:
Vectors multiplied by dimensions. This is the raw size of the index. Ten million vectors at 1536 dimensions is roughly four times the footprint of the same corpus at 384 dimensions.
Replicas and availability. Production usually means at least two replicas, which doubles the resident footprint before you have served a single extra query.
Index type. Approximate indexes trade recall for memory and speed. The defaults are rarely the cheapest configuration for your recall target.
The trap that catches teams is embedding dimension. Choosing a 3072 dimension model over a 768 dimension one quadruples your storage bill permanently, for a recall improvement that is often marginal on domain specific corpora. Several modern embedding models support truncation to smaller dimensions with modest quality loss, and testing that before you load ten million vectors is the highest leverage hour in the whole project.
How to Choose Between Pinecone, Weaviate, Qdrant and pgvector
Under roughly a million vectors with Postgres already in production, pgvector is usually the right answer. A dedicated vector database earns its cost past that scale, or earlier if you need serious metadata filtering or hybrid search.
A short decision guide:
Already on Postgres, under about a million vectors. Use pgvector on Neon or Supabase. One database, one backup story, and you can join vector results against relational data in a single query. That last point is worth more than most benchmarks.
Heavy metadata filtering. If most queries are "similar to this, but only from this tenant, in this language, updated this year", purpose built engines like Qdrant and Weaviate handle filtered search far better than a bolt-on index. Naive filtering destroys recall, and this is where general purpose databases struggle first.
Hybrid search is central to the product. Weaviate, Elastic and MongoDB Atlas all combine keyword and vector retrieval natively, which saves you from building and tuning your own fusion layer.
You already pay for Redis or MongoDB. Both now carry vector search. Adding vectors to infrastructure you already run and already staff is cheaper than adding a ninth service to your architecture.
You want zero operational surface. Pinecone is the most managed option in the category, which is exactly why its free offer is a short evaluation trial rather than a large grant.
AI Perks lists which of these currently have open programs and what each one requires.
What Order to Apply In
Apply for the database you already run first, the large category grants second, and short evaluation trials last, because trials start burning the moment you accept them whether or not you are ready to use them.
First, the database you already use. If you are on Supabase, Neon, MongoDB or Redis, the credits attach to a bill you are already paying and require no migration. This is free money with no architectural cost attached. Start at getaiperks.com and filter to the database category.
Second, the large category grants. Weaviate and Neon sit at the top of the range. These are worth applying for even if you have not committed to them, because approval rates vary and having an approved grant in hand is a legitimate input into the architecture decision.
Third, adjacent infrastructure. MongoDB Atlas, Redis Cloud and Elastic Cloud all bill separately from your cloud provider, which means these credits stack with AWS, Google Cloud or Azure grants rather than overlapping them.
Last, evaluation trials. Pinecone's $300 is designed for a proper benchmark against your own data, not for running production. Trigger it in the week you actually plan to run that benchmark.
Eligibility depends on stage and funding and differs for every program in the table above. The current requirements for each are listed at getaiperks.com.
What Founders Get Wrong About Vector Database Credits
The most expensive mistake is choosing the vector database first and the embedding model second. The embedding model determines your index size, your recall ceiling and your migration cost, and the database is the easier half to change.
Four other patterns worth avoiding:
Treating credits as the reason to choose. A $100,000 grant that pushes you onto infrastructure that fits your workload badly costs more in engineering time than it saves in hosting. Choose on fit, then fund the choice.
Forgetting the embedding bill. Vector database credits cover storage and search. They do not cover generating the embeddings, which is a model API bill and needs its own credits.
Underestimating re-embedding. Migrating between vector databases is easy. Migrating between embedding models means regenerating every vector in your corpus, which is the real lock-in.
Starting the clock before there is a workload. Time boxed credits granted before you have data to index waste most of their value on an empty index.
Founders who fund a full stack rather than one layer end up furthest ahead. AI Perks tracks $7.7M in credits across 194 companies so the database, the compute and the model calls can all be covered at once.
Frequently Asked Questions
Which vector database gives startups the most free credits?
Weaviate offers the largest dedicated vector database grant in the category at up to $100,000, with Neon matching that figure for Postgres and pgvector. MongoDB Atlas follows at up to $25,000. The right choice depends on your workload rather than the headline number. Current terms are tracked at getaiperks.com.
Do I need a vector database, or is pgvector enough?
For most startups under roughly a million vectors, pgvector inside your existing Postgres is enough and considerably simpler. You gain one backup story instead of two and can join vector results against relational data directly. Dedicated engines win on heavy metadata filtering, hybrid search and scale past that point.
Can I stack vector database credits with OpenAI or Anthropic credits?
Yes, and you should. They cover different bills. Vector database credits pay for storing and searching embeddings, while model credits pay for generating those embeddings and for the answers built on top of retrieval. Holding both is how teams cover a full year at getaiperks.com.
What does a vector database actually cost without credits?
A small index typically costs tens of dollars a month, rising into the hundreds or low thousands once you hold tens of millions of vectors with replicas for availability. List prices vary widely by provider and change often, so benchmark on your own corpus rather than trusting a pricing page comparison.
Do vector database credits cover the cost of embeddings?
No. Generating embeddings is a model API call billed by your model provider, not by the vector database. A team with only database credits still pays to embed its corpus, and re-embedding after a model change is often the larger bill. Fund both layers at getaiperks.com.
Is it hard to migrate between vector databases later?
Migrating databases is straightforward, since you are moving vectors you already have. The hard migration is changing embedding models, which invalidates every stored vector and requires regenerating the whole corpus. Choose the embedding model carefully and treat the database as the reversible decision.
Store the vectors. Let someone else pay for the index.