The free tier has become one of the most important battlegrounds in the managed vector database market. For developers building RAG applications, semantic search, recommendation systems, and AI agents, the first question is often not which database scales to billions of vectors? It is much simpler: How far can I get without paying?
That question is surprisingly difficult to answer because “free” means different things across providers. Some offer a genuinely free managed database with a fixed amount of storage. Others provide monthly usage allowances. Some offer a temporary trial rather than a permanent free service. And products such as Supabase and MongoDB offer vector search as part of a broader database platform rather than positioning themselves primarily as vector databases.
As of October 2026, the market has settled into several distinct approaches. Pinecone, Weaviate, Qdrant and Zilliz/Milvus provide particularly clear examples of the vector-native model, while Redis, MongoDB Atlas and Supabase demonstrate how vector search is increasingly becoming a feature of general-purpose managed databases.
The meaning of “free” has changed
A few years ago, choosing a free vector database often meant choosing between an open-source database that you hosted yourself and a cloud service that offered a short trial.
That distinction has blurred.
Several major providers now offer permanent managed free tiers. This matters because running a database yourself is not actually free in operational terms. The software may cost nothing, but the developer still has to provide compute, storage, networking, backups and maintenance.
Qdrant, for example, currently offers a permanently free managed cluster with 0.5 vCPU, 1 GB of RAM and 4 GB of disk. It is explicitly designed for prototypes and testing. Qdrant says this configuration can serve roughly one million 768-dimensional vectors, although the actual capacity depends on the workload and index configuration. Free clusters are automatically suspended after a week of inactivity and deleted after four weeks if they are not reactivated.
Weaviate took a similar step in 2026. In June, the company introduced a free Weaviate Cloud offering, moving its cloud service beyond its previous emphasis on open-source self-hosting. Its current free plan provides one cluster per user, up to 100,000 objects, 1 GB of memory and 10 GB of disk, along with one collection and up to three tenants. It also includes 2,000 embedding requests per day and 1,000 Query Agent requests per month.
That makes the free tier considerably more interesting than a simple demonstration environment. A small RAG application can actually live within these limits.
Pinecone: a managed experience with a real free starting point
Pinecone remains one of the clearest examples of a vector-database company treating the free tier as the beginning of the product journey rather than merely a trial.
Its current Starter plan is free and includes Pinecone Database, Pinecone Inference and Pinecone Assistant usage within specified allowances. The database portion provides up to 2 GB of storage, up to 2 million write units per month, 1 million read units per month and 1 GB of egress. The Starter plan also supports dense, sparse and full-text indexes, with a single project and up to two users.
The interesting part is that Pinecone’s limits are expressed partly through usage rather than simply saying “you get X GB.” That makes the free allowance more useful for some applications and less predictable for others.
For example, a relatively small dataset can still generate substantial read activity if an application is performing many similarity searches. Conversely, a larger collection that is queried infrequently may remain comfortably inside the allowance.
Pinecone’s own examples illustrate this distinction. Its current documentation estimates that a semantic-search workload containing roughly 30,000 documents and using 1,024-dimensional embeddings can support around 15,000 searches per day on the Starter database allowance, subject to the assumptions in its example.
There is another important detail: the free allowance is for Pinecone’s database service, but embedding and reranking usage has its own quotas. Developers therefore should not interpret “free Pinecone” as meaning that every component of an AI retrieval pipeline is unlimited.
For someone learning vector search or building a small RAG prototype, however, the attraction is straightforward: there is very little infrastructure to operate.
Weaviate: one of the most substantial free managed environments
Weaviate’s 2026 free offering is notable because it combines a meaningful amount of storage with access to features that make Weaviate more than a bare vector index.
The free cloud cluster allows 100,000 objects, 1 GB of memory and 10 GB of disk. It supports one collection and up to three tenants. Weaviate also includes a daily allowance for its hosted embedding service and a monthly allowance for Query Agent.
This creates an interesting proposition for developers building applications around Weaviate’s broader AI database functionality. The free tier is not simply a place to upload a few thousand vectors and run a similarity query. It provides enough room to experiment with application architecture, metadata, retrieval and some of Weaviate’s AI-oriented capabilities.
There are still important boundaries. The free cluster is best-effort rather than a production SLA environment, and the number of collections and tenants is deliberately restricted. The paid Flex tier starts at $45 per month, bringing more capacity and production-oriented capabilities.
Weaviate’s change is particularly significant because it removes one of the traditional arguments for choosing its open-source version purely to avoid cloud costs. Developers can now evaluate the managed experience before deciding whether self-hosting or a paid cloud deployment makes more sense.
Qdrant: unusually straightforward limits
Qdrant takes a simpler approach.
Its free cloud cluster is a single-node environment with 0.5 vCPU, 1 GB RAM and 4 GB disk. Qdrant explicitly positions it for testing and prototypes and says it can accommodate approximately one million 768-dimensional vectors under appropriate conditions. It also includes basic monitoring, logs, alerting and standard support.
The limitation that deserves attention is not simply capacity. The free cluster has no dedicated resources, no high availability and limited operational features compared with Standard. It can also disappear if it sits unused for long enough: Qdrant suspends an inactive free cluster after one week and deletes it after four weeks of inactivity.
That makes Qdrant’s free tier particularly well suited to projects where the database is part of development rather than a continuously running production service.
Its pricing model also makes the eventual transition relatively easy to understand. Paid Qdrant Cloud clusters are charged according to resources such as CPU, memory and disk rather than forcing developers to reason primarily about an abstract number of vector queries.
Zilliz Cloud: a generous free cluster built around Milvus
Zilliz Cloud, the managed cloud service associated with Milvus, offers a somewhat different model.
Its permanent free cluster currently provides 5 GB of storage, up to 2.5 million virtual compute units per month and up to five collections. Zilliz says the 5 GB allocation is enough for roughly one million 768-dimensional vectors. No payment information is required to create the free cluster.
This is an important distinction from Zilliz’s separate free trial. The free cluster is an ongoing no-cost environment with basic vector-database functionality, whereas the trial is intended to let users evaluate the larger Serverless and Dedicated offerings.
Zilliz’s model is therefore attractive to developers who want to explore the Milvus ecosystem without immediately committing to paid infrastructure. The five-collection allowance is also useful for experimentation involving multiple datasets or application environments.
The trade-off is that developers need to understand Zilliz’s resource model. Once a workload moves beyond the free cluster, Serverless usage is primarily measured through virtual compute units, while Dedicated deployments use compute resources differently. Storage, data transfer, backups and other services can also contribute to the eventual bill.
Redis: tiny on storage, broad in what it can do
Redis Cloud illustrates why vector databases cannot always be separated cleanly from conventional databases.
Redis is not simply a vector database. It is a general-purpose in-memory data platform that also provides vector search capabilities. Its current free Redis Cloud Essentials database contains only 30 MB of space, which is dramatically smaller than the free allocations offered by Qdrant, Weaviate or Zilliz. Redis documents the free tier as being intended for learning and application prototypes.
The free database also has limits of 30 concurrent connections, 5 GB of monthly network bandwidth and up to 100 operations per second. Only one free database is available per account.
On raw vector capacity, this makes Redis a very different proposition from Qdrant or Zilliz. But storage size alone does not tell the whole story.
A developer who already needs Redis for caching, session management, JSON, search or other application functionality may find value in having vector retrieval in the same platform. Redis Cloud’s free tier therefore makes more sense when vector search is one component of a broader application architecture rather than the sole reason for choosing the database.
MongoDB Atlas: vector search without introducing another database
MongoDB Atlas takes the same broader-database approach.
Atlas Free clusters provide 512 MB of storage, and MongoDB explicitly documents Free and Flex clusters as environments suitable for testing MongoDB Vector Search. The company cautions that these lower-cost tiers can experience resource contention and latency that make them less suitable for production workloads.
The significance here is architectural rather than purely numerical.
If an application already stores users, products, documents, permissions and application state in MongoDB, adding vector embeddings to the same database can be simpler than introducing Pinecone, Qdrant or another specialized service. The free Atlas tier effectively lets developers experiment with that architecture without paying for a separate vector database.
Its 512 MB limit is much less generous than the storage available from several vector-native competitors, but a small RAG application can still fit inside it.
Supabase: the Postgres route to free vector search
Supabase is another important alternative, although calling it a managed vector database would be slightly misleading. Supabase provides managed PostgreSQL, and PostgreSQL can perform vector similarity search through the pgvector extension. Supabase’s documentation explicitly supports storing and querying embeddings with pgvector.
The free Supabase plan currently provides 500 MB of database space per project, 1 GB of file storage and 5 GB of egress. Free projects can be paused after a week of inactivity, and two active free projects are allowed.
For vector workloads, the important number is the 500 MB database quota. Supabase says a free project enters read-only mode once the database exceeds that quota.
This may sound restrictive compared with a dedicated vector service, but it can be extremely practical. If the application’s relational data already lives in PostgreSQL, embeddings can remain alongside the original records. Filtering, joins, authentication and application data do not have to be synchronized between two independent databases.
That architectural simplicity is arguably the biggest “free feature” Supabase offers for AI applications.
The real comparison is not simply storage
Looking at the headline numbers can lead to the wrong conclusion.
Weaviate gives you 100,000 objects, 1 GB of memory and 10 GB of disk. Qdrant gives you 1 GB of RAM and 4 GB of disk. Zilliz provides 5 GB of storage. Pinecone’s Starter plan includes 2 GB of database storage plus read and write allowances. Redis provides just 30 MB, while MongoDB Atlas provides 512 MB.
But these numbers are not directly interchangeable.
A vector database can be constrained by RAM, disk, index size, query volume, write volume, network traffic or compute. Two services that both advertise a few gigabytes of free capacity may behave very differently once an HNSW index, metadata and query workload are added.
The free tier is therefore best understood as a workload envelope, not a single storage number.
A small semantic-search application with tens of thousands of documents could comfortably fit into several of these offerings. A high-frequency application with the same number of vectors could run into query or compute allowances much sooner. Likewise, an application with a few hundred thousand vectors and extensive metadata may hit memory or disk limits before it reaches the theoretical vector count suggested by a provider.
Free tiers are increasingly becoming architecture tests
The most useful way to think about the 2026 free tiers is as a chance to test the architecture you intend to use.
Pinecone lets developers experience a highly managed, vector-focused service with usage-based allowances. Weaviate provides a comparatively feature-rich managed environment. Qdrant offers a small but permanent dedicated vector cluster with simple resource limits. Zilliz gives developers a free entry point into the Milvus ecosystem.
Redis, MongoDB and Supabase make a different argument: perhaps you do not need a separate vector database at all.
That distinction can matter more than whether one service gives you four or five gigabytes of free storage.
If embeddings are only one part of an application whose primary data already lives in PostgreSQL, Supabase and pgvector can eliminate an entire synchronization layer. If MongoDB is already the application’s system of record, Atlas Vector Search can provide a similar benefit. If the application fundamentally revolves around vector retrieval and needs the capabilities of a vector-native engine, Qdrant, Weaviate, Pinecone or Zilliz can provide a more specialized environment.
The free tier is where that decision can be tested with relatively little financial risk.
What developers should watch before choosing
The most important trap is assuming that “free forever” means “production ready for free.”
None of these offerings should be treated that way automatically.
Free environments commonly have restrictions around availability, resources, regions, backups, support, authentication, networking or scaling. Qdrant’s free cluster, for example, is single-node and has no high availability. Weaviate’s free tier is best effort. MongoDB’s Free tier has substantially fewer operational capabilities than its dedicated tiers. Supabase’s free plan lacks automatic database backups and can pause inactive projects.
There is also a less obvious consideration: migration cost.
A free tier is most valuable when it lets you validate the same APIs, indexing strategy and application architecture that you will use after paying. Moving from a prototype to production should ideally involve increasing resources rather than rebuilding the retrieval layer.
That is one reason managed free tiers have become so important. They allow developers to discover not only whether vector search works, but whether a particular provider’s operational model fits the application.
The bigger trend in 2026
The vector database market is moving away from the idea that every AI application needs a standalone vector store.
Dedicated services remain important, particularly for applications where vector retrieval is central. But PostgreSQL, MongoDB and Redis increasingly make vector search a native part of broader application databases. At the same time, dedicated vendors are making their managed services easier to try for free.
That leaves developers with more choices than simply “Pinecone versus open source.”
For a small experiment, almost any of these free options can be sufficient. For a RAG prototype that needs meaningful capacity, the differences become more significant. And for a production system, the free tier should be treated primarily as a proving ground: a way to test ingestion, indexing, filtering, retrieval quality and application behavior before deciding what level of infrastructure the workload actually requires.
The most useful question in 2026 is therefore not Which vector database has the biggest free tier?
It is Which free tier lets me test the architecture I am most likely to keep when the application starts costing money?
That question produces a much more meaningful comparison—and it is one that the rapidly expanding free offerings from Pinecone, Weaviate, Qdrant and Zilliz are increasingly designed to answer.
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