Pinecone vs Weaviate: Managed or Open Source Vector DB? (2026)

Quick verdict

Short answer

Choose Pinecone if you want no database to operate at all, are fine with a proprietary service, and like a billing model based on read units, write units and storage. Choose Weaviate if you want an open source engine you can self-host or move to and from a managed cloud, need a choice of vector index per collection, or want native shard-per-tenant multi-tenancy with tenant offloading. Both support hybrid keyword and vector search and both can embed text for you. Price both against your own data size and query volume, because the two clouds meter very different things.

How we know: This comparison is research-based: deployment models, licences, index options, hybrid search, multi-tenancy, embedding integrations and pricing were checked on 7 November 2026 against Pinecone and Weaviate pricing pages, documentation, release notes and the Weaviate GitHub repository. We have not deployed either product, so nothing here is a measured result.

Pinecone is a proprietary managed vector database from Pinecone Systems. There is no downloadable server: indexes run on Pinecone's serverless platform on AWS, Google Cloud or Azure, and Enterprise customers can run it in their own cloud account through Bring Your Own Cloud (BYOC), generally available since 25 August 2026. You do not pick an ANN algorithm; you pick a record model. Document indexes, generally available since 2 September 2026, hold dense vectors, sparse vectors and BM25 full-text fields in one schema. Pod-based indexes are legacy and new Standard and Enterprise organisations can no longer create them.

Weaviate is an open source vector database server written in Go, mostly under the BSD 3-Clause licence (files in the repository's wl/ folder are under a commercial licence). You run it with Docker or on Kubernetes, or use Weaviate Cloud, which has Free, Flex and Premium plans. Collections hold objects with properties and one or more named vectors, and each collection chooses its vector index: HNSW by default, flat, dynamic or the disk-oriented HFresh index.

Different shapes of product. Pinecone is only a service; Weaviate is software first, with a service on top. That single fact drives most of the rows below, from licence to pricing to who tunes the index.

Side by side

Aspect	Pinecone	Weaviate
Product model	Proprietary managed service (serverless)	Open source database server plus Weaviate Cloud
Self-hosting	No; Enterprise BYOC runs Pinecone in your cloud account	Yes: Docker or Kubernetes, no licence fee
Licence	Closed source (SDKs are public)	BSD-3-Clause for most of the repository; wl/ folder under a commercial licence
Vector index choice	Not user-selectable; dense, sparse and document indexes	Per collection: HNSW (default), flat, dynamic, HFresh
Hybrid search	Dense plus sparse vectors, or BM25 full-text fields in document indexes, with optional reranking	BM25 and vector search run in parallel and fused (relativeScoreFusion default, rankedFusion), weighted by alpha
Multitenancy	Namespaces per index: 100 on Starter, 1,000 on Builder, 100,000 on Standard, 1,000,000 on Enterprise	Native, one shard per tenant; tenants ACTIVE, INACTIVE or OFFLOADED to cloud storage
Embedding integration	Pinecone-hosted embedding and reranking models	Model provider modules (OpenAI, Cohere, AWS, Google, Ollama and others) and Weaviate Embeddings
Managed clouds	AWS, Google Cloud, Azure (Starter on AWS us-east-1 only)	Weaviate Cloud on AWS and Google Cloud; Azure listed for Premium Dedicated
Billing model	Read units, write units, storage and egress, with plan minimums	Per 1M stored vector dimensions, storage and backup per GiB
Free option	Starter: 2 GB storage, 2M write and 1M read units a month	Self-hosting; Weaviate Cloud Free with 100,000 objects and 1 collection
Main trade-off	Least to operate, but no portability outside BYOC and query-driven costs	Portable and configurable, but self-hosting means RAM planning and operations work

Key differences

Managed only versus open source with a cloud

Pinecone cannot be downloaded. Most buyers use its multi-tenant serverless platform; Enterprise customers who need data to stay in their own account can use BYOC, which Pinecone prices through sales. Everything else, from index structure to scaling, is handled by Pinecone. That removes almost all operations work, but it also means you cannot run the same database on a laptop, in a private data centre or in an air-gapped environment.

Weaviate is software you can run anywhere Docker or Kubernetes runs, with the same API on Weaviate Cloud. Teams often start on one and move to the other. The open source licence is permissive BSD-3-Clause for most of the repository, but the repository notes that files in its wl/ folder are under a commercial licence, so check which components you deploy. Self-hosting is free in licence terms; you pay in infrastructure and in the work of sizing memory for HNSW, upgrading and backing up.

Index control and hybrid search

Weaviate lets each collection choose its vector index. HNSW is the default and is built in memory; flat suits small collections and many small tenants; dynamic starts flat and switches to HNSW past a threshold; HFresh, added in v1.36, is a cluster-based index that keeps most data on disk. Quantization, including rotational quantization (RQ), reduces memory. Hybrid search runs a BM25 query and a vector query in parallel and fuses the scores; alpha sets the balance, where 0 is keyword only and 1 is vector only.

Pinecone hides the ANN algorithm. You choose the record model instead: a document index can combine dense vector, sparse vector and full-text string fields, while older vector indexes hold either dense or sparse vectors. Hybrid retrieval combines these signals, optionally with a Pinecone-hosted reranker. In our view this suits teams that do not want to tune index parameters, while Weaviate suits teams that want to trade memory, recall and cost themselves.

# Weaviate (Python client v4): hybrid query weighted towards vectors
response = articles.query.hybrid(
    query="refund policy for annual plans",
    alpha=0.75,
    limit=5,
)

Multitenancy models

Pinecone partitions an index into namespaces, and queries target one namespace. The pricing page lists the namespace ceiling per index by plan: 100 on Starter, 1,000 on Builder, 100,000 on Standard and 1,000,000 on Enterprise. It is a simple model that works well when each tenant's data has the same shape.

Weaviate enables multi-tenancy per collection and stores each tenant in its own shard. Tenants can be ACTIVE, INACTIVE (kept on disk, not in memory) or OFFLOADED to cloud storage through the S3 offload module, which helps when many tenants are idle most of the time. The cloud Free plan allows up to 3 tenants, so multi-tenant designs need Flex or Premium on Weaviate Cloud, or a self-hosted cluster.

Embeddings and model integrations

Both products can turn text into vectors for you. Pinecone hosts embedding and reranking models that are called at upsert and query time, so a single service handles storage, embedding and reranking. Weaviate has model provider modules for many API-based providers (OpenAI, Cohere, AWS, Google, Voyage AI and others) and local options such as Ollama and Hugging Face Transformers, plus its own hosted Weaviate Embeddings service billed per million tokens. Weaviate's approach gives more choice of model and lets self-hosted deployments keep inference local; Pinecone's gives fewer moving parts.

What you pay for

Pinecone meters activity: read units for queries, write units for upserts, storage per GB and egress. Costs therefore rise with query volume and result size, which can be harder to forecast than fixed capacity. Weaviate Cloud meters what you store: a rate per million vector dimensions (which varies with index type, compression and region), storage per GiB and backup per GiB. A large but rarely queried collection therefore tends to favour activity-based billing, and a small but heavily queried one tends to favour storage-based billing, but the crossover depends on your data, so use both vendors' calculators. Exact list prices are below.

Pricing and licensing

Pinecone. Listed on pinecone.io/pricing on 7 November 2026 (prices shown with a $ sign, currency code not stated): Starter is free with up to 2 GB storage, 2M write units and 1M read units a month, 5 indexes and AWS us-east-1 only. Builder is a flat $20 a month with up to 10 GB storage, 5M write units and 2M read units. Standard has a $50 monthly minimum, then read units at $16 to $18 per million, write units at $4 to $4.50 per million, storage at $0.33 per GB per month and egress at $0.10 per GB after 100 GB, with a 3-week trial and $300 of credits. Enterprise has a $500 monthly minimum, read units at $24 to $27 per million, write units at $6 to $6.75 per million and a 99.95% uptime SLA. BYOC is priced by sales.

Weaviate. Self-hosting the open source database has no licence fee. Listed on weaviate.io/pricing on 7 November 2026 ($ sign, currency code not stated): Free is $0 with no stated time limit, 100,000 objects and 1 collection. Flex is pay-as-you-go from $45 a month, with vector dimensions from $0.00465 per 1M, storage from $0.12 per GiB and a 99.5% SLA. Premium is a prepaid contract from $400 a month through sales, shared (99.9% SLA) or dedicated (99.95% SLA), with dimension rates from $0.003875 (shared) and $0.002718 (dedicated) per 1M. Rates vary by index type, compression and region, and the price book changed for usage from 1 September 2026.

Pricing checked on the vendors' official pages on 7 November 2026. Prices change; confirm before buying.

Where each one leads

Pinecone strengths

Nothing to deploy, size or upgrade: serverless indexes with no index parameters to tune
Document indexes combine dense, sparse and BM25 full-text fields in one schema
Hosted embedding and reranking models in the same service
Very high namespace ceilings per index on Standard and Enterprise for multi-tenant products
Published unit prices, a free Starter plan and a flat $20 Builder plan

Weaviate strengths

Open source (BSD-3-Clause for most of the code) and self-hostable at no licence cost
Choice of vector index per collection, including the disk-oriented HFresh index
Native shard-per-tenant multi-tenancy with inactive and offloaded tenant states
Wide range of embedding, generative and reranker provider integrations, including local models
Same API on self-hosted deployments and Weaviate Cloud, so you can move between them

Limitations

Pinecone limitations

Closed source with no self-hosted edition outside Enterprise BYOC
Read-unit billing ties cost to query volume and result size
Starter is limited to AWS us-east-1, 5 indexes and 2 GB of storage
Pod-based indexes are legacy, so older deployments need a migration to serverless

Weaviate limitations

Self-hosted HNSW indexes are built in memory, so large deployments need RAM planning or compression
Weaviate Cloud Free allows only 100,000 objects, 1 collection and 3 tenants
Cloud cost per vector dimension varies with index type, compression and region
Part of the repository (the wl/ folder) is under a commercial licence

When to choose each

Choose Pinecone if

You want a vector store with no infrastructure and no index tuning
Your team is comfortable with a proprietary managed service and its data residency options
You want embedding, reranking and storage from one vendor
You need many lightweight tenants separated by namespaces
Your stored data is large relative to your query volume

Choose Weaviate if

You need the option to self-host, on premises or in your own Kubernetes cluster
You want to choose and tune the vector index and quantization per collection
Many tenants are idle much of the time and could be kept inactive or offloaded
You want to pick embedding models from many providers or run them locally
You prefer an open source licence to reduce lock-in

When neither is right

Your vectors belong next to relational data you already keep in PostgreSQL: see pgvector vs Pinecone and the pgvector profile.
You need a self-hosted engine with many index types, including disk and GPU indexes: compare Milvus vs Qdrant.
You only need an in-process library for a prototype or a single application: see Chroma vs LanceDB.
Vector search must sit inside a full-text search engine you already run: see Elasticsearch vs OpenSearch vector search.

Final recommendation

Bottom line

If you want the vendor to run everything and accept a proprietary service, Pinecone is the more direct fit, with serverless indexes, hosted models and namespaces that scale to large tenant counts. If you want open source code, the freedom to self-host, control over index types or tenant offloading, Weaviate is the more direct fit, provided you plan memory for self-hosted HNSW or budget for Weaviate Cloud. Load a representative sample into both free tiers, estimate a month of queries and storage with each calculator, and decide on cost model and portability as much as on features.

Frequently asked questions

Can I self-host Pinecone like Weaviate?

No. Pinecone has no downloadable server. The closest option is BYOC, generally available for Enterprise customers since 25 August 2026, which runs Pinecone inside your own cloud account and is priced by sales. Weaviate can be self-hosted with Docker or Kubernetes.

Do both support hybrid search?

Yes. Pinecone combines dense vectors with sparse vectors or, in document indexes, BM25 full-text fields. Weaviate runs BM25 and vector search in parallel and fuses the scores, with alpha controlling the balance (the server default is 0.75).

How do the two handle multi-tenant applications?

Pinecone uses namespaces inside an index, with up to 1,000,000 per index on Enterprise. Weaviate stores each tenant in its own shard and lets tenants be active, inactive or offloaded to cloud storage.

Which has the better free tier?

They measure different things. Pinecone Starter gives 2 GB storage and monthly read and write unit allowances on AWS us-east-1. Weaviate Cloud Free gives 100,000 objects in 1 collection with no stated time limit, and the open source server is free to self-host.

Sources

Checked November 2026.

How we research comparisons: our editorial method.