Weaviate is live · free plan, paid from $45/mo · last verified 4 Nov 2026 · viability healthy
Weaviate
Weaviate is an open-source AI database that combines vector search, hybrid search, and RAG with agent memory in one stack.
Weaviate earns its place when retrieval, tenant isolation, and agent memory must ship in one system, and Engram's Claude Code persistence plus the free Query Agent tier are real working features rather than roadmap slides. Know the price shape before you commit: Flex starts at $45/mo pay-as-you-go, Premium is a prepaid contract from $400/mo, and SSO/SAML appears only at Premium. Small teams needing SAML should budget for that contract or look elsewhere.
Verdict generatedVerified 9d ago · liveness 78/100 · cite: rightaichoice.com/tools/weaviate
- Multi-tenant AI SaaS that needs tens of thousands of segmented indexes in one cluster
- Production RAG teams wanting hybrid search, embeddings, and memory from one platform
- Agentic products that need persistent managed memory through Engram
- Enterprise search where RBAC, SSO/SAML, and HIPAA compliance are requirements
- Simple similarity search on a small dataset where Chroma or FAISS is lighter
- Teams wanting zero-config setup with no index, shard, or quantization decisions
- Applications that only need keyword search, where Elasticsearch fits better
We scan live Reddit threads, YouTube comments, X posts, G2 reviews and other communities — and hand you an honest verdict in under a minute.
- Honest verdict, not marketing
- Real pros & cons from real users
- Attributed quotes with receipts
3 free scans · no card needed
Skip Weaviate if you need SSO/SAML on a self-serve monthly plan — SAML starts on the prepaid Premium tier with a minimum from $400/mo — or if you only need nearest-neighbor lookups where Chroma or FAISS is lighter.
Backups are charged on top of the minimum, based on data volume and retention — vector dimensions cost from $0.00465/1M on Flex and storage from $0.12/GiB, so a large corpus pushes the bill above the $45/mo minimum.
Flex at $45/mo pay-as-you-go fits seed-to-Series-A teams shipping a first production RAG or multi-tenant feature. Premium (prepaid from Contact Sales, minimum from $400/mo) fits companies that need SSO/SAML, 4-hour Sev 1, and multi-cloud coverage in 7 regions. Enterprise fits regulated buyers needing 99.95% uptime across ~40 regions. Against Pinecone and Qdrant, Weaviate bundles embeddings, query translation, and Engram memory in one bill; against Chroma or FAISS, you pay more for machinery a
In short
Weaviate — Weaviate is an open-source AI database that combines vector search, hybrid search, and RAG with agent memory in one stack. Best for Multi-tenant AI SaaS that needs tens of thousands of segmented indexes in one cluster, Production RAG teams wanting hybrid search, embeddings, and memory from one platform, Agentic products that need persistent managed memory through Engram. Free to start; paid plans from $45/mo.
No head-to-head comparison for Weaviate yet — the alternatives below are the closest matches we list.
What is Weaviate?
Weaviate — Weaviate is an open-source AI database that combines vector search, hybrid search, and RAG with agent memory in one stack. Best for Multi-tenant AI SaaS that needs tens of thousands of segmented indexes in one cluster, Production RAG teams wanting hybrid search, embeddings, and memory from one platform, Agentic products that need persistent managed memory through Engram. Free to start; paid plans from $45/mo.
Weaviate is an open-source AI database for teams building RAG pipelines and agentic products. It stores your objects and their vector embeddings together, then serves semantic vector search, BM25 keyword search, and hybrid search with alpha tuning from one deployment. The vendor cites over 20M open-source downloads, with customers running 9 billion vectors and 50K+ tenants in a single cluster. Four services ride on that foundation. The core Vector Database handles storage, indexing, and retrieval, now including HFresh, a disk-based index tuned for low heap usage and incremental background maintenance. Weaviate Embeddings generates vectors from text and images in-platform, so you skip a separate embedding pipeline; Snowflake Arctic-Embed-M-V1.5 runs $0.025 per 1M tokens, V2.0 $0.040, and ModernVBERT $0.065. The Query Agent turns natural-language questions into database operations, and Engram adds managed persistent memory so agents adapt per user, with a Claude Code plugin that carries context across sessions. Production concerns are covered: multi-tenancy across tens of thousands of segmented indexes, RBAC, async replication, SOC 2 Type II, and HIPAA on Enterprise Cloud (AWS). Release 1.40 added PathSeer, a third HNSW filtering strategy drawn from a SIGMOD 2026 paper; 1.39 promoted the Boost API and MMR diversity retrieval to GA, previewed 4-bit Rotational Quantization, and shipped an experimental Search REST API. Deployment spans Weaviate Cloud, Docker, Kubernetes, or embedded via Python and JavaScript, with Python, Go, TypeScript, and JavaScript SDKs plus GraphQL and REST. It's more machinery than a small similarity lookup needs and less than running a vector store, an embedding service, and a memory layer as three separate purchases.
Key features
- Vector search over high-dimensional embeddings with HNSW or disk-based HFresh index
- Hybrid search combining vector similarity and BM25 keywords with alpha tuning
- PathSeer adaptive filtered vector search, a third HNSW filter strategy in Weaviate 1.40
- Built-in Weaviate Embeddings generating vectors from text and images in-platform
- Embedding models: Snowflake Arctic-Embed-M-V1.5, Arctic-Embed-M-V2.0, ModernVBERT
- Query Agent translates natural-language questions into Weaviate database operations
- Engram managed persistent memory for LLM agents and applications
- Engram plugin gives Claude Code persistent memory across sessions
- MCP Server so LLMs and IDE assistants interact with your Weaviate instance
- 4-bit Rotational Quantization with SIMD performance work (preview in 1.39)
- Boost API for result boosting, generally available in 1.39
- MMR diversity retrieval, generally available in 1.39
- Multi-tenancy scaled to 50K+ tenants in a single cluster
- Async replication and HA clusters with up to 99.95% uptime SLA
- Python, Go, TypeScript, JavaScript SDKs plus GraphQL and REST; Docker, Kubernetes, embedded deployment
Behind the verdict
Pick Weaviate when your retrieval layer and your isolation model are the same problem. The customers the vendor showcases are the tell: 50K+ tenants in one cluster, 9 billion vectors in production, banks with HIPAA and RBAC requirements. If you're building multi-tenant AI SaaS where each customer needs a segmented index, the built-in tenant system is the reason to choose it over a general-purpose vector store bolted onto a separate embedding service. The AI services angle is more than bundling. Weaviate Embeddings removes one pipeline and one vendor bill; the Query Agent lets a product answer natural-language questions without you hand-writing every query path. Engram is the piece worth trialing if you're building agents that need to remember users across sessions. In practice the Claude Code plugin is a fast way to see whether managed memory helps your workflow before wiring it into a product. Pass on it if your whole job is fast nearest-neighbor lookup over a few hundred thousand vectors. Chroma or FAISS will get you there with less config, less to operate, and no index or quantization decisions. And if you only need keyword search, Elasticsearch remains the sharper tool for that one job. The honest friction points: SSO/SAML is gated behind Premium, so a five-person team with an enterprise security questionnaire in hand faces a prepaid contract rather than a $45/mo plan. HIPAA is Enterprise Cloud only, so don't plan a pay-as-you-go healthcare workload. The Free tier is a sandbox (100,000 objects, 1 collection, 3 tenants, best-effort SLA) not a staging ground for real traffic. Versus Pinecone, Weaviate gives you open source, embedded deployment, and the self-hosted path that managed-only competitors don't offer; versus Qdrant, the difference is the AI services
Models under the hood
as of 2026-11-08
Limitations
- Weaviate Cloud's Free tier is a genuine sandbox, not a production slot: 1 cluster per user, 100,000 objects, 1 GB memory, 10 GB disk, 1 collection, up to 3 tenants, 2,000 embedding requests/day, and 1,000 Query Agent requests/month.
- Flex starts at $45/month pay-as-you-go with a shared cloud cluster and 99.5% uptime; Premium is a prepaid contract from Contact Sales with a minimum from $400/mo, and up to 99.95% uptime; Enterprise adds HIPAA on Enterprise Cloud (AWS) at 99.95% uptime.
- The product surface has grown quickly — Vector Database, Embeddings, Query Agent, Engram, HFresh, Boost API, 4-bit Rotational Quantization — so that breadth carries a steeper learning curve than a single-purpose vector store.
- If you run Weaviate 1.39.x below 1.39.3, upgrade to patch the high-severity Google modules credential disclosure issue.
as of 2026-10-10
Researching Weaviate? Get your full AI stack in 60 seconds.
Free, no signup — tell us your goal and get tools matched to your budget & existing stack.
Is Weaviate still active?
Weaviate is live · free plan, paid from $45/mo · last verified 4 Nov 2026 · viability healthy.
Viability score
How well maintained and how widely used is Weaviate? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: November 2026
How we score →What's new in Weaviate
Checked 8 days agoAcross the latest 8 updates: 3 feature updates, 1 launch and 4 news mentions.
Weaviate 1.40 Release
Weaviate 1.40 makes dropping a named vector and 4-bit Rotational Quantization GA, adds multi-vector embeddings on HFresh and PathSeer filtering, and changes vector configuration defaults.
Deprecating the GraphQL API: timeline and migration paths
Weaviate's GraphQL API is deprecated as of October 26, 2026. Post covers the timeline for Weaviate Cloud and self-hosted, who is affected, and migration paths.
How Weaviate responded to the eMudhra certificate compromise
Weaviate says it patched its customer estate and infrastructure platform in under 17 hours to mitigate risk from eight leaked certificates from the eMudhra certificate authority.
Weaviate security release - Medium severity fix for cross-collection vector disclosure in nearObject
Weaviate v1.39.3 fixes a medium-severity authorization issue in the nearObject GraphQL operator. CVE pending assignment.
PathSeer: Adaptive Filtered Vector Search
Weaviate 1.40 adds PathSeer, a third HNSW filter strategy from a SIGMOD 2026 paper, contributed by the paper's first author. Post compares it to sweeping and ACORN.
Persistent memory for Claude Code with Weaviate Engram
The Engram plugin gives Claude Code persistent, long-term memory, so it remembers what you've done and brings it back when it matters.
Weaviate security release - High severity fix for credential disclosure in the Google modules
Weaviate v1.39.3 fixes a high-severity credential disclosure issue in the Google modules.
Agent Memory with Engram: A Practical Guide
Guide to using Engram: writing topic descriptions that control extraction, choosing bounded topics and scopes, selecting a retrieval mode, and placing memories in the prompt without hurting prompt-cache efficiency.
What do users say about Weaviate?
How we verified this
Every listed fact about Weaviate — pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 4 Nov 2026. Analysis is drafted with AI models and checked against our editorial standards before publishing; see how we verify.
Verification history
We have re-verified Weaviate 22 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed pricing page
- — changed pricing page
- — changed summary, description, integrations, who it suits, our verdict, pricing tiers, who should skip it, features
- — changed pricing page
- — changed pricing tiers, our verdict, summary, who should skip it, who it suits, description, features, platforms, pricing page
- — changed features, description, our verdict, hidden costs, limitations, switching in, switching out, models, who should skip it, pricing tiers, pricing fit, setup time, summary
Showing the 6 most recent of 22 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does Weaviate cost in 2026?
Weaviate: Free to start; paid plans from $45/mo. Pricing last verified 4 Nov 2026.
Plans compared
Every published Weaviate tier, its price as the vendor publishes it, who it fits, and what it adds over the tier below. The 12-month cost projection follows.
| Plan | Price | Billing | Who it's for |
|---|---|---|---|
| Free | Free | — | Developers and small teams evaluating Weaviate or prototyping a first vector or hybrid search app on a managed cluster.Adds: Free entry point: 1 cluster, 100,000 objects, 1 collection, up to 3 tenants, 2,000 embedding requests/day, and 1,000 Query Agent requests/month. |
| Flex | From $45/moVendor: “Starts at $45/mo” | monthly | Seed-to-Series-A product teams shipping a first production RAG or multi-tenant feature without committing to a contract.Adds: Adds pay-as-you-go monthly billing, unlimited objects, up to 1,000 collections, async replication and HA at 99.5%, 7-day backups, and 30,000 Query Agent requests/month. |
| Premium | Custom pricingVendor: “Contact sales (prepaid from $400/mo minimum)” | quote | Scaling production teams that need SSO/SAML, predictable prepaid spend, and faster incident response.Adds: Adds prepaid contract terms, shared or dedicated deployment, up to 99.9% uptime, 4-hour Sev 1, 30-day backups, SSO/SAML, phone and Slack support, and 7 cloud regions. |
| Enterprise Cloud | Contact sales | quote | — |
| Query Agent add-on | $30/organization/moVendor: “$30/organization per month” | monthly, per seat | Teams already on a paid Weaviate plan that want natural-language querying beyond their included request quota.Adds: $30/org per month for 4,000 requests, with unlimited usage-based requests beyond that; free to try at 1,000 requests/month. |
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Where the pricing makes sense
The company stage and team size where Weaviate's pricing actually pencils out — and where peers do it cheaper.
Flex at $45/mo pay-as-you-go fits seed-to-Series-A teams shipping a first production RAG or multi-tenant feature. Premium (prepaid from Contact Sales, minimum from $400/mo) fits companies that need SSO/SAML, 4-hour Sev 1, and multi-cloud coverage in 7 regions. Enterprise fits regulated buyers needing 99.95% uptime across ~40 regions. Against Pinecone and Qdrant, Weaviate bundles embeddings, query translation, and Engram memory in one bill; against Chroma or FAISS, you pay more for machinery a
Has Weaviate's pricing changed?
No plan price for Weaviate has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick Weaviate?
Pick Weaviate if you are Multi-tenant AI SaaS that needs tens of thousands of segmented indexes in one cluster, Production RAG teams wanting hybrid search, embeddings, and memory from one platform, Agentic products that need persistent managed memory through Engram, Enterprise search where RBAC, SSO/SAML, and HIPAA compliance are requirements.
Look elsewhere if you are Simple similarity search on a small dataset where Chroma or FAISS is lighter, Teams wanting zero-config setup with no index, shard, or quantization decisions, Applications that only need keyword search, where Elasticsearch fits better, Healthcare workloads expecting HIPAA on a pay-as-you-go plan rather than Enterprise Cloud.
Skip it if Skip Weaviate if you need SSO/SAML on a self-serve monthly plan — SAML starts on the prepaid Premium tier with a minimum from $400/mo — or if you only need nearest-neighbor lookups where Chroma or FAISS is lighter.
GeneratedReal-world workflow fit
Concrete scenarios for the personas Weaviate actually fits — and what changes day-one when you adopt it.
Spin up a Free cloud cluster, load product data, and run hybrid search with alpha tuning to prototype a support chatbot before committing spend.
Outcome: A working RAG prototype on 100,000 objects with 2,000 embedding requests/day before paying anything.
Move to Flex at $45/mo pay-as-you-go, enable multi-tenancy for tens of thousands of segmented indexes, and wire Engram so each user's agent remembers prior sessions.
Outcome: Production hybrid search with per-tenant isolation and persistent agent memory on one stack.
Take a Premium or Enterprise prepaid contract for SSO/SAML, dedicated deployment, and HIPAA on Enterprise Cloud (AWS), then profile slow queries per stage and per shard.
Outcome: Compliance-ready retrieval with 99.9%–99.95% uptime and data-driven tuning of the retrieval pipeline.
Use cases
- Build semantic and hybrid search over a product catalog using alpha-tuned vector plus BM25 retrieval.
- Implement RAG for a support chatbot grounded in your own documentation.
- Give AI agents persistent, per-user memory through Engram.
- Deploy multi-tenant vector search for SaaS, with tens of thousands of isolated tenants in one cluster.
- Power recommendation systems with vector similarity search.
- Improve code assistants with hybrid search over codebases via the Weaviate MCP Server.
- Let analysts ask natural-language questions over large document corpora using the Query Agent.
- Diagnose slow retrieval pipelines using per-stage, per-shard query profiling.
Setup time & first value
How long it actually takes to get something useful out of Weaviate — broken out by persona, not the marketing-page minute.
Free Weaviate Cloud cluster: minutes to a first hybrid query — the Quickstart is an end-to-end demo taking 15–30 minutes. Flex: same day, then tune index type and compression as load grows. Embedded via Python or JavaScript: fastest for quick evaluation. Docker or Kubernetes self-host: an afternoon for a basic deployment, longer once replication, RBAC, and multi-tenancy are configured.
Switching to or from Weaviate
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Pinecone: map namespaces to Weaviate tenants, re-embed with Weaviate Embeddings or your own model, and reindex into HFresh or HNSW.
- →From Qdrant: export vectors and payloads, recreate collections in Weaviate, and rebuild hybrid search using BM25 plus vectors.
- →From Chroma: load collections through the Python SDK, then add hybrid search and multi-tenancy as you outgrow the prototype.
- →From FAISS: wrap the flat index in a Weaviate deployment to gain persistence, replication, RBAC, and Query Agent on top of the same vectors.
- →From Elasticsearch: keep keyword retrieval while adding vector and hybrid search with alpha tuning in one deployment.
- ↗To Pinecone: export objects and vectors, then rely on Pinecone namespaces for the tenant isolation Weaviate handled natively.
- ↗To Qdrant: move collections and payloads if you want a leaner vector-first store and will rebuild embeddings and query translation separately.
- ↗To Chroma or FAISS: downshift if you only need small-scale similarity search and can trade away multi-tenancy, replication, and Engram.
- ↗To pgvector: consolidate into Postgres if your retrieval stays modest and you prefer fewer systems over hybrid search depth.
Integrations
Resources & guides
- Documentationweaviate.io
Weaviate Database
Complete documentation for Weaviate, the open-source vector database for AI applications.
- Learnweaviate.io
Weaviate Learning Center
Training courses, resources, and support options for builders of all levels. We’re with you on your AI journey.
- Resourceweaviate.io
Blog
Blog
- Resourceweaviate.io
Weaviate Database
Complete documentation for Weaviate, the open-source vector database for AI applications.
Tutorials & Learning
YouTube returned 6 videos for “Weaviate”, and we withheld 6: 6 could not be judged, because “Weaviate” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Weaviate.
Official links
What are the best Weaviate alternatives?
The closest alternatives to Weaviate we list are Qdrant, Chroma and Pinecone. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| Qdrant | Freemium | Pricing not published | — |
| Chroma | Freemium | Pricing not published | — |
| Pinecone | Freemium | Pricing not published | — |
Alternatives to Weaviate
View all
Tools that pair well with Weaviate
Common stack mates teams adopt alongside Weaviate, with the specific reason each pairing earns its keep.
Qdrant
Open-source vector search engine for filtered hybrid retrieval at production scale
Chroma
Chroma: open-source vector, full-text, regex and metadata search built on object storage.
Pinecone
Pinecone is a fully managed vector database for production RAG, semantic search, and agent knowledge retrieval.
Frequently asked questions
Categories
For makers of Weaviate
- Get a Verified Listing for Weaviate ($39): a written decision within 1 working day.
- Free listing for another tool: Free reviews are decided in order, within 3 weeks of the review starting.
Used Weaviate? Help shape our editorial sentiment research.