Software advice
Best Vector Databases in 2026
A vector database is a data store that keeps embeddings, the number lists a model produces for text, images or audio, and returns the stored items closest in meaning to a query. Developers use it for retrieval augmented generation, semantic search and recommendations. It is not a language model, and it does not create embeddings unless the vendor adds that as a separate service.
Quick answer
Pinecone ranks first because the managed service has the shortest path from signup to production. Qdrant fits teams that want open source plus a European-run cloud. Chroma fits prototypes and small apps that want a simple client and cheap usage billing.
A team building retrieval for an AI app usually faces three choices at once: run the database yourself or rent it, buy a dedicated product or add vector search to a database you already run, and pay by usage or by reserved capacity. This ranking covers eight options across those choices and judges them on how quickly a developer reaches a working index, what the entry plan costs, what the vendor says about data use, and how hard it is to leave. Plans and policies were read from vendor pages in October 2026. We ran no benchmarks, so speed is described as a design trade-off, using the vendors' own published figures where they exist.
The 8 best vector databases in 2026, ranked
The order is the verdict, there is no score. No vendor paid for position. How we rank.
| # | Tool | Best for | Free plan | Entry price |
|---|---|---|---|---|
| 1 | Pinecone | Teams that want a hosted vector index with no servers to run | Yes | $20 per month (Builder) |
| 2 | Qdrant | Developers who want an open-source engine plus a managed cloud with EU roots | Yes | Free (1 GB RAM cluster), then usage-based |
| 3 | Weaviate | Teams that want hybrid search and built-in embedding services in one cloud | Yes | $45 per month (Flex) |
| 4 | Milvus | Platform teams running very large collections on their own Kubernetes clusters | Yes | Free to self-host |
| 5 | Zilliz Cloud | Teams that want Milvus without operating it, including at very large scale | Yes | Free tier, Dedicated clusters from $197 per month |
| 6 | Chroma | Prototypes and small apps that want a simple client and usage billing | Yes | $0 plus usage (Starter) |
| 7 | LanceDB | Teams storing vectors beside images, video and training data in one table format | Yes | Quoted |
| 8 | MongoDB Atlas Vector Search | Teams already storing app data in MongoDB who want vectors in the same cluster | Yes | Flex clusters, about $8 to $30 per month |
- 1
Pinecone
Pinecone takes first place because the managed index is the quickest route from a notebook to a production RAG app. The free Starter plan is real, Builder adds regions and multiple projects, and Standard brings RBAC, SSO and backups. The trade-off is that Pinecone is hosted only, so the exit path runs through bring-your-own-cloud on Enterprise or a rebuild elsewhere.
Strong on
- Starter is free and includes dense, sparse and full-text indexes
- Security page lists SOC 2 Type II, HIPAA with a BAA, and GDPR readiness
- Standard adds backup and restore, RBAC and SAML single sign-on
Weak on
- No self-hosted edition, so the product cannot leave Pinecone's cloud without a rebuild
- Starter is limited to one AWS region, us-east-1, and up to 5 indexes
Skip it if: Teams that must run the database on their own servers or want an open-source engine they can fork. Builder is a flat fee. Standard has a $50 monthly minimum, then pay as you go.
- 2
Qdrant
Qdrant sits second because it offers the most complete exit strategy in the ranking: the same Rust engine runs on a laptop, on your cloud account through Hybrid Cloud, or on Qdrant's hosted clusters. A free single-node cluster is permanent. The cost is more cluster sizing decisions than a serverless index asks of you, and SSO and private links need the Premium tier.
Strong on
- Free cluster has no expiry and open-source code is available for self-hosting
- Hybrid Cloud runs managed clusters on your own infrastructure for data residency
- SOC 2 Type 2 and HIPAA reports are available through its Trust Center
Weak on
- The free cluster is a single node with no high availability
- SSO, private VPC links and customer-provided keys sit in the Premium tier
Skip it if: Teams that want per-request serverless billing and would rather not pick cluster sizes. Standard tier bills hourly for compute, memory and storage. Premium needs a minimum spend.
- 3
Weaviate
Weaviate ranks third because it packs the most extras around the core database: hybrid search, multi-tenancy, hosted embedding models and a Query Agent that turns questions into database operations. Pricing by stored vector dimensions is easy to model. The free cluster is small, and SSO, 99.9 percent uptime and metrics endpoints start at the contract-based Premium tier.
Strong on
- Hybrid search, vector compression and multi-tenancy are included on the free plan
- Flex bills by vector dimensions, so cost can be estimated from data size
- SOC 2 Type II audit and HIPAA on the Enterprise Cloud tier on AWS
Weak on
- Free cluster allows one collection and up to 3 tenants
- SSO and phone support are limited to the contract-based Premium plans
Skip it if: Teams on a tight budget who need more than 100,000 objects without paying at least $45 a month. Flex is pay as you go with a $45 minimum. The free cluster is capped at 100,000 objects.
- 4
Milvus
Milvus lands fourth because it is the strongest open-source choice for billion-scale collections, with a distributed, Kubernetes-native design and GPU acceleration, yet that scale comes with operational weight. Milvus Lite installs with one pip command for local work, which keeps experiments easy. Small teams usually spend more on running it than they would on a managed index.
Strong on
- Apache 2.0 license under the LF AI & Data Foundation, so no license fee
- Standalone mode and pip-installable Milvus Lite cover laptops and single machines
- Distributed architecture is meant for billions of vectors and streaming updates
Weak on
- Distributed deployments need Kubernetes skills and ongoing operations work
- No vendor SLA unless you buy a managed service built on it
Skip it if: Small teams without infrastructure staff who just need a few million vectors searchable this week. The software costs nothing under Apache 2.0. You pay for servers and the people who run them.
- 5
Zilliz Cloud
Zilliz Cloud is the managed service for Milvus, made by the company that is the open-source project's main contributor, so the two are related rather than rivals. It ranks fifth because the pricing menu is wide, with serverless, dedicated and bring-your-own-cloud options, but the plan structure takes longer to read than a flat fee. Tiered storage cuts cost for huge archives.
Strong on
- Dedicated clusters come as performance, capacity or tiered-storage types for different budgets
- Trust Center lists SOC 2 Type II and ISO/IEC 27001
- Enterprise adds a 99.95 percent SLA, SSO, audit logs and private endpoints
Weak on
- Many cluster types and billing items make forecasting harder than a flat plan
- Standard and Free tiers get community support through Slack only
Skip it if: Buyers who want one simple monthly price and do not want to compare cluster types. Free covers 5 GB. Enterprise Dedicated starts at $197 monthly and Standard has a serverless option.
- 6
Chroma
Chroma ranks sixth because it is the easiest open-source option to start with and its serverless cloud bills only for what you write, store and query. Its terms say customer data will not train AI models without your permission. The Team plan costs $250 a month before usage, which is steep next to rivals at similar scale, and the product has fewer enterprise controls.
Strong on
- Apache 2.0 open source with usage-based cloud pricing that starts at $0
- Terms say Chroma will not use customer data to train models unless permitted
- SOC 2 Type II examination covers Chroma Cloud's security controls
Weak on
- Team plan carries a $250 monthly base fee before usage charges
- Chroma Cloud runs on AWS, and exceeding a usage limit pauses the service
Skip it if: Enterprises that need a published uptime SLA on a mid-sized plan or fine-grained private networking. Starter includes $5 in credits. Team is $250 monthly plus usage with $100 in credits.
- 7
LanceDB
LanceDB ranks seventh because it has narrowed toward a multimodal lakehouse for AI teams, which is excellent if you also curate and train on that data and more than you need if you only want RAG retrieval. The embedded open-source library starts quickly with Python, TypeScript and Rust clients. Enterprise has no public price, so budgeting means a sales call.
Strong on
- Embedded Apache 2.0 library with Python, TypeScript and Rust SDKs
- Vectors, metadata and raw multimodal data sit in a single versioned table
- Enterprise docs list SOC 2 Type II, HIPAA and GDPR support
Weak on
- Enterprise is sold by contact only, with no published plans or prices
- Product focus on training pipelines makes it heavier than a plain RAG store
Skip it if: Teams that only need hosted vector search for a chatbot and want to pay by card today. The open-source library is free. Enterprise pricing is quoted by sales, with no public price list.
- 8
MongoDB Atlas Vector Search
MongoDB Atlas Vector Search closes the list because it is a feature of a general database, not a dedicated engine, which is its appeal and its limit. Vector, hybrid and metadata queries run on the cluster that holds your documents, so no sync job exists. Search Nodes can isolate vector workloads from the core database. Teams without MongoDB gain little over a purpose-built option.
Strong on
- Vector data lives beside operational documents, so no separate sync pipeline is needed
- Hybrid queries combine vector, lexical, geospatial and metadata filters in one pipeline
- Search Nodes isolate vector workloads from the main database
Weak on
- Requires MongoDB Atlas clusters on supported versions such as 6.0.11 or 7.0.2
- Adopting MongoDB just for vectors adds a whole database to learn and pay for
Skip it if: Teams with no MongoDB footprint who only need a small, cheap standalone vector index. Atlas has a free 512 MB tier. Flex is usage-priced, and Dedicated starts at $56.94 monthly.
How these vector databases were judged
How do you judge search quality and speed in a vector database?
Quality depends on the index type, compression and filtering, so ask what trade-offs each product lets you tune. Weaviate's pricing page offers a cost-optimized profile and a performance profile that keeps vectors in memory. Zilliz lists per-cluster figures such as 500 to 1,500 queries per second at about 10 ms for its performance-optimized tier, measured on 768-dimension vectors. Pinecone supports dense, sparse and full-text indexes, and Zilliz, Weaviate and MongoDB advertise hybrid search. Vendor figures are not neutral benchmarks, so load a sample of your own documents and measure recall on questions where you know the right answer.
What does a managed vector database cost in 2026?
Entry prices cluster in the tens of dollars, but billing models differ. Pinecone charges $20 a month on Builder and a $50 monthly minimum on Standard, as of October 2026, then bills storage at $0.33 per GB. Weaviate Flex starts at $45 a month and prices by vector dimensions stored. Chroma Cloud Team costs $250 a month plus usage, while its Starter plan is $0 plus usage. Qdrant and Zilliz each have a free cluster, and MongoDB lists Flex clusters between $8 and $30 a month. Estimate your own vector count before comparing, because per-read fees scale with traffic.
Do vector database vendors train on your data, and where is it stored?
Embeddings can leak the text they came from, so read the data terms. Pinecone's security page says customer data is used only to service API calls. Chroma's terms say it will not use customer data to train AI or machine learning models unless you permit it. Qdrant says data in a cluster stays in the region where it is deployed, though telemetry goes to its U.S. management plane. Weaviate, Zilliz and LanceDB list SOC 2 Type II, and Zilliz adds ISO 27001. For MongoDB and Milvus, the vendor's policy was not checked for training use.
How hard is it to move a vector database later?
The vectors themselves are portable, since they are arrays of numbers you can re-export or regenerate. The sticky parts are index settings, metadata filters and client code. Open-source engines lower the exit cost: Chroma's pricing page says exporting data is easy because the product is open source, and Qdrant provides a migration tool from its open-source version to its cloud. Pinecone is managed only, though its Enterprise tier offers a bring-your-own-cloud option. Keep your source documents and embedding model name stored next to the index so a rebuild on another engine takes hours, not weeks.
Which vector database fits a prototype, a startup or a regulated team?
A prototype or weekend project does well on Chroma, LanceDB or a free Qdrant or Zilliz cluster, because they cost nothing until traffic arrives. A startup that wants no infrastructure work should compare Pinecone Builder with Weaviate Flex. A team already on MongoDB can try its vector search before adding another system. Regulated teams should shortlist vendors that list HIPAA and SOC 2 reports, such as Pinecone, Qdrant, Weaviate, Zilliz and LanceDB, then ask about private networking and customer-managed keys. Very large collections point toward Milvus or Zilliz.
How to choose a vector database in three steps
- Count your vectors and your queries. Write down how many chunks you will embed, the vector dimension, how often documents change and how many searches run each day. A knowledge base of thirty thousand documents is a small workload, and a free tier from Qdrant, Zilliz or Weaviate may carry it. A catalog with tens of millions of items is a different problem. Cost calculators on the Pinecone, Weaviate, Zilliz and Chroma pages accept these numbers, so run all four with the same inputs. Re-run them at ten times the traffic to see whether read fees or storage fees dominate the bill.
- Decide how much operations work you will accept. Running an open-source engine yourself costs nothing in license fees but adds upgrades, backups, monitoring and capacity planning. Renting a managed service moves that work to the vendor and adds a monthly bill. Be honest about your team size. If nobody on the team has run Kubernetes, Milvus self-hosted is a poor first choice, while a single container of Qdrant or Chroma is easy. Check whether the managed vendor offers a way out, such as the open-source edition or a bring-your-own-cloud tier.
- Test with your own documents before committing. Take two hundred real documents and twenty questions with known answers, load them into two or three shortlisted engines, and compare what each retrieves. Turn on metadata filters and hybrid search if your app needs them, because those features differ most between products. Time ingestion as well as queries. Then check the data terms and region options on the vendor's security page for the tier you would actually buy, since compliance features often sit on the Enterprise or Premium plan only.
Frequently asked questions
What is the best vector database in 2026?
Pinecone ranks first in this comparison because it is fully managed, has a free Starter plan and publishes SOC 2 Type II and HIPAA support. Qdrant ranks second for its open-source engine and Berlin-based company, and Weaviate third for hybrid search and a free cluster. The right pick depends on scale, budget and whether you want to self-host.
Is there a free vector database?
Yes, several. Pinecone Starter is free, Qdrant's free cluster has 1 GB of RAM and 4 GB of disk, Zilliz Cloud Free includes 5 GB of storage and up to 5 collections, and Weaviate's free cluster holds 100,000 objects. Chroma, Milvus and LanceDB are open source and cost nothing to run yourself, though you pay for the servers.
Do I need a dedicated vector database, or is pgvector or MongoDB enough?
For modest collections, adding vector search to a database you already run is often enough and avoids syncing two systems. MongoDB Atlas offers vector and hybrid search on the same cluster as your documents. A dedicated engine earns its place when collection size, filtering needs or query volume grow, or when you want to tune index settings.
What is the difference between Milvus and Zilliz Cloud?
Milvus is the open-source database, licensed under Apache 2.0 and hosted by the LF AI and Data Foundation. Zilliz Cloud is the paid managed service built on it by Zilliz, which is Milvus's main contributor. Choose Milvus to run it yourself and Zilliz Cloud if you want someone else to operate it, with serverless, dedicated and bring-your-own-cloud options.
Which vector database is cheapest for a small RAG app?
For tiny apps the free tiers cost nothing, so the decision rests on limits. Pinecone's Starter allows up to 5 indexes and 2 GB of storage, and Chroma Cloud's Starter gives $5 in credits then bills usage. Pinecone lists a 30,000-document knowledge base as roughly 15,000 searches a day on Starter. Check each limit against your own data.
Which vector databases are open source?
Qdrant, Weaviate, Milvus, Chroma and LanceDB all publish their code on GitHub, and Milvus and Chroma are Apache 2.0 licensed. Qdrant is written in Rust and Milvus in Go and C++. Pinecone has no self-hosted edition, and MongoDB's Community Edition is a separate product from Atlas, the hosted service covered here.
Is there a European vector database?
Qdrant's data controller is Qdrant Solutions GmbH in Berlin, and Weaviate B.V. is registered in Amsterdam. Both also run on U.S. cloud providers, so company location does not settle hosting location. Qdrant says cluster data stays in the region you deploy to, while Weaviate lists regions per plan. Ask each vendor about hosting before signing.
How much does it cost to scale a vector database to millions of vectors?
It depends on dimensions, compression and read volume. Zilliz lists dedicated capacity from $63 per million vectors a month for its performance tier down to $5 for tiered storage, measured on 768-dimension vectors. Pinecone bills reads at $16 to $18 per million on Standard, and Weaviate prices by stored dimensions. Use each vendor's calculator with your real numbers.