New Delhi [India], October 2: Managed vector databases have become considerably easier to try without paying for infrastructure upfront. What began as short trials and limited development sandboxes has evolved into a market where several established providers now offer persistent free cloud tiers suitable for prototypes, semantic search projects, RAG systems and small AI applications.
The limits differ substantially. Some providers emphasize storage capacity, others provide generous read and write allowances, while a growing number are adding inference and higher-level AI services around the database itself.
Here are five notable managed cloud vector database options with ongoing free access as of October 2026.
Weaviate Cloud
Weaviate introduced an always-free managed cloud tier in June 2026, extending the company’s existing open-source model into Weaviate Cloud.
The free plan includes one managed cluster per user with up to 100,000 objects, 1 GB of memory, 10 GB of disk, one collection and as many as three tenants. No credit card is required and the cluster does not have a fixed trial expiration.
The database includes the core retrieval capabilities developers would expect from Weaviate, including vector search and hybrid search, which combines semantic vector retrieval with lexical BM25 search. Developers can therefore test search architectures that require more than straightforward nearest-neighbor retrieval.
Where the free offering becomes particularly useful for AI application development is in the surrounding services.
Weaviate includes up to 2,000 hosted embedding requests per day, allowing applications to generate embeddings through Weaviate rather than necessarily integrating a separate inference provider. The free tier also includes 1,000 Query Agent requests per month.
Query Agent lets applications query data using natural language. It can determine which data to search, construct filters and sorts, choose retrieval strategies and perform aggregations. The current free allowance translates to as many as 1,000 Search-mode queries or 250 Ask-mode queries per month because Ask consumes four request units.
Weaviate also makes Engram available through a free tier. Engram is a memory server for AI agents and applications that extracts, transforms and stores useful memories rather than simply preserving raw conversation histories. Its pipeline can reconcile new information with existing context, including merging, consolidating and resolving conflicting memories before committing changes.
That gives developers several pieces of an AI application stack within the same platform: the managed database, embeddings, hybrid retrieval, natural-language database interaction and persistent agent memory.
Weaviate remains open source as well, so developers who eventually want to manage their own infrastructure retain a self-hosting path outside Weaviate Cloud.
The free managed cluster is designed primarily for exploration and prototypes rather than highly available production workloads, but it provides enough capacity and surrounding services to build substantial working applications before moving to a paid deployment.
Pinecone
Pinecone’s Starter plan gives developers permanent access to its fully managed vector infrastructure without requiring a paid subscription.
The current free allowance includes up to 2 GB of database storage, five indexes, 100 namespaces per index, 2 million write units per month, 1 million read units per month and 1 GB of monthly egress.
Pinecone estimates that 2 GB can hold roughly 300,000 records when using 1,536-dimensional vectors and approximately 500 bytes of metadata per record, although actual capacity depends on the data being stored.
The platform supports dense, sparse and full-text indexes, giving developers multiple approaches to retrieval without leaving the managed service. That makes the free plan applicable to conventional semantic search as well as workloads that combine lexical and semantic techniques.
Pinecone also extends beyond the database through Pinecone Inference. Its Starter plan provides access to embedding models and limited reranking, allowing developers to handle part of the retrieval pipeline within Pinecone rather than building every component around external inference providers.
Another part of the free offering is Pinecone Assistant. The Starter tier currently includes 1 GB of Assistant storage together with allowances for input, output, context-processing and ingestion usage. Assistant is designed for applications that need to answer questions from stored documents without manually building every element of a RAG pipeline.
Pinecone’s free tier therefore covers considerably more than vector storage. Developers can experiment with multiple retrieval types, managed inference, reranking and higher-level document-question-answering functionality while staying within one cloud service.
Unlike Weaviate, Milvus and Qdrant, Pinecone does not provide an open-source version of its core managed database for self-hosting. Its model remains centered on operating vector infrastructure as a cloud service.
For developers who prefer that fully managed approach, the Starter plan provides enough database and AI-service capacity for meaningful experimentation and smaller applications.
Milvus through Zilliz Cloud
Milvus is an open-source vector database, while Zilliz Cloud provides its closely associated managed cloud experience.
Zilliz offers one free cluster per organization with 5 GB of storage, up to five collections and 2.5 million virtual compute units, or vCUs, each month. No payment information is required to create the free cluster.
Zilliz estimates that the 5 GB capacity is sufficient for approximately one million 768-dimensional vectors. The precise number depends on vector dimensions, metadata and index configuration, but the published estimate illustrates the amount of data developers can work with on the free plan.
Rather than imposing a simple query-count allowance, Zilliz measures read and write consumption using vCUs. Operations including search, query, insert, upsert and delete consume this monthly allocation.
That model gives developers room to test different usage patterns while working with the same Milvus technology used for substantially larger vector deployments.
Milvus itself has developed around high-scale similarity search and supports features including filtering, multiple index strategies and hybrid retrieval patterns. Using Zilliz Cloud removes the operational work of provisioning and maintaining a Milvus deployment while retaining access to its broader ecosystem.
The free tier concentrates primarily on the database and its retrieval capabilities rather than bundling a large collection of agent-oriented services around it.
That can be useful for developers who specifically want to evaluate vector database architecture, indexing and retrieval at meaningful data volumes without introducing additional layers into the stack.
Because Milvus remains open source, projects can also move between managed Zilliz infrastructure and independently operated Milvus deployments depending on their future infrastructure requirements.
Qdrant Cloud
Qdrant combines its open-source vector database with a managed cloud service that includes a permanent free cluster.
The free cluster consists of a single node with 0.5 vCPU, 1 GB of RAM and 4 GB of disk. Qdrant describes the tier as free forever and positions it for testing and prototypes.
The resource-based model differs from services that specify a fixed number of vectors. The number of records that fit into the cluster depends on vector dimensions, metadata payloads, indexing configuration and techniques such as quantization.
This gives developers direct control over how they use the available memory and storage rather than tying the plan to one standardized vector count.
Qdrant has particularly extensive support for payload filtering, allowing structured metadata conditions to participate directly in vector retrieval. Applications can combine semantic similarity with attributes such as category, timestamp, location or user-specific properties without separating those operations into independent systems.
Qdrant Cloud also includes free inference with selected models on the free tier.
This allows developers to experiment with embedding and vector-search workflows without necessarily operating their own inference infrastructure for every use case.
Like Weaviate and Milvus, Qdrant also maintains an open-source version of its database. Projects can therefore begin on a managed free cluster while preserving the option to deploy Qdrant independently later.
The free cluster does not include high availability, backup and disaster-recovery capabilities or the service-level commitments available on paid plans. Those limitations are consistent with its positioning as a development and prototype environment rather than production infrastructure.
For developers who want a managed environment while retaining relatively direct control over vector database behavior, the free Qdrant Cloud cluster provides a substantial testing ground.
Upstash Vector
Upstash Vector approaches the category from a serverless perspective rather than exposing a traditional continuously provisioned vector database cluster.
Its free plan includes one vector database, a maximum capacity of 200 million vector dimensions, up to 100 namespaces and as much as 1 GB of associated data and metadata.
The service allows up to 1,536 dimensions per vector on the free plan.
Because Upstash expresses storage capacity as total vector dimensions, the practical number of records depends directly on embedding size. At 768 dimensions, 200 million dimensions theoretically correspond to roughly 260,000 vectors. At 1,536 dimensions, the equivalent is approximately 130,000 vectors before considering the other limits of the database.
The free tier also provides 10,000 queries and 10,000 updates per day.
Those recurring daily allowances make the service useful for applications that need consistent activity rather than an environment used only occasionally for development.
Upstash Vector supports namespaces, metadata filtering and live index updates, allowing applications to segment data and combine semantic retrieval with structured conditions. Developers can access the service through REST as well as Python, TypeScript and Go SDKs.
Its serverless architecture means developers do not need to size or maintain a vector database cluster. The application interacts with the service while Upstash handles the underlying infrastructure.
The free plan focuses closely on vector retrieval rather than providing the broader agent and memory layers offered by some other platforms, but its substantial daily request limits make it practical for semantic search, recommendations and lightweight RAG applications.
Managed vector databases now offer much more at no cost
The important shift in 2026 is not simply that developers can store vectors for free. The free offerings now reflect several different approaches to building AI infrastructure.
Weaviate provides a managed vector database alongside hosted embeddings, Query Agent and Engram memory. Pinecone combines vector infrastructure with inference, reranking and Assistant. Zilliz gives developers substantial managed Milvus capacity measured through storage and compute usage. Qdrant combines an open-source database with a resource-based managed cluster and selected free inference. Upstash provides a serverless model with recurring daily query and update allowances.
The differences become useful because vector applications themselves are becoming more varied.
A semantic-search application may value storage and high query allowances. A RAG system may benefit from integrated embedding or reranking services. An agentic application may need retrieval plus persistent memory. A development team expecting eventually to operate its own infrastructure may also care whether the underlying database remains available as open-source software.
Free managed vector databases have consequently moved well beyond disposable demonstrations. Their current allowances are large enough to build functioning applications, evaluate architectures with real data and determine which parts of an AI stack developers want the database platform itself to handle.
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