Machine Learning · head to head
Pinecone vs Zilliz

Zilliz
Databases
Managed vector database and vector lakebase for AI applications
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Pinecone reads and writes are billed on separate meters, and reads are far more expensive, at $16 to $18 per million against $4 to $4.50 for writes on Standard; Zilliz pricing structure not publicly disclosed, requires sales contact
- They diverge on capability: Pinecone covers Vector similarity search, Zilliz covers Vector indexing.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which Pinecone and Zilliz actually diverge.
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).
What each one covers
Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.
Only in Pinecone
- Vector similarity search
- Metadata filtering
- Namespace partitioning
- Real-time updates
- Hybrid search
- OpenAI
- Cohere
- LangChain
Only in Zilliz
- Vector indexing
- Distributed architecture
- SQL interface
- Tensor support
- Real-time search
- Cloud-native
- Open-source compatible
What people use each for
The jobs each tool is most often brought in to do.
Pinecone
- Vector database for AI/ML applicationsnot Zilliz
- Semantic search implementationnot Zilliz
- Recommendation systemsnot Zilliz
- RAG (Retrieval-Augmented Generation) architecturesnot Zilliz
Zilliz
- Build retrieval-augmented generation (RAG) systemsnot Pinecone
- Implement semantic search over documentsnot Pinecone
- Create multimodal search with text and imagesnot Pinecone
- Power recommendation engines with vector similaritynot Pinecone
- Enable similarity search on user embeddingsnot Pinecone
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Pinecone
- Reads and writes are billed on separate meters, and reads are far more expensive, at $16 to $18 per million against $4 to $4.50 for writes on Standard
- Unit prices vary by region, so the same workload costs different amounts in different places
- The Standard plan carries a $50 monthly minimum and Enterprise $500, charged whether or not the usage reaches it
- Enterprise pays more per unit as well as more in minimum, at $24 to $27 per million reads against Standard's $16 to $18
- Indexes and namespaces are capped by plan, at 5 indexes on the free tier and 20 on Standard
- RBAC and SSO require the Standard plan
Zilliz
- Pricing structure not publicly disclosed, requires sales contact
- Operational complexity for self-hosted Milvus deployments
- Learning curve for those unfamiliar with vector databases
- Limited built-in analytics compared to some alternatives
Pricing, plan by plan
Pinecone
Free- StarterFree
- 2GB storage
- 2M write units/month
- 1M read units/month
- Builder$20/month
- 10GB storage
- 5M write units
- 2M read units
- Standard$50/month
- Unlimited storage ($0.33/GB/month)
- 20 indexes per project
- 100K namespaces
- Enterprise$500/month
- 99.95% uptime SLA
- BYOC (Bring Your Own Cloud) option
- Private endpoints
Zilliz
FreeNo published plan breakdown. See the Zilliz review.
Which should you pick?
Choose Pinecone if
- You need vector similarity search.
- You want to start without paying.
- You also want metadata filtering.
Choose Zilliz if
- You need vector indexing.
- You want to start without paying.
- You work on Cloud, Self-hosted.
- You also want distributed architecture.
Questions people ask
- Is Pinecone or Zilliz better?
- Neither clearly leads. Pinecone starts at Free and Zilliz at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Pinecone or Zilliz?
- Pinecone starts at Free and Zilliz at Free.
- Does Pinecone or Zilliz run on more platforms?
- Pinecone runs on Web. Zilliz runs on Cloud, Self-hosted.
- Can I use Pinecone for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Pinecone best used for?
- Pinecone is most often used for vector database for ai/ml applications, semantic search implementation, recommendation systems, rag (retrieval-augmented generation) architectures. Of those, vector database for ai/ml applications and semantic search implementation are not what Zilliz is typically brought in for.
- What can Pinecone do that Zilliz cannot?
- Pinecone covers Vector similarity search, Metadata filtering, Namespace partitioning, Real-time updates. Zilliz covers Vector indexing, Distributed architecture, SQL interface, Tensor support.
Answered from the vendors’ own pages
Pinecone: Does Pinecone offer a free plan?
Yes, Pinecone's Starter tier is free and includes 2GB storage, 2M write units/month, 1M read units/month, and supports up to 2 users and 1 project.
SourceZilliz: What is the difference between Milvus and Zilliz Cloud?
Milvus is the open-source vector database that you can self-host. Zilliz Cloud is the fully managed service built on Milvus that removes operational overhead and handles scaling automatically.
SourcePinecone: What are Pinecone's storage costs on the Standard plan?
On the Standard plan, storage costs $0.33/GB per month. Read units cost $16-18 per million units; write units cost $4-4.50 per million units.
SourceZilliz: How many vectors can Zilliz handle?
Milvus and Zilliz Cloud can store and search billions of vectors through their distributed architecture that separates storage and compute layers.
SourcePinecone: What support options does Pinecone provide?
Starter tier includes community Discord support. Builder tier includes free support. Standard tier support costs $29/month for Developer or $250/month for Pro. Enterprise tier includes Pro support.
SourceZilliz: Is Milvus open-source?
Yes, Milvus is open-source under the Apache License 2.0 and is part of the LF AI & Data Foundation.
SourceZilliz: What pricing does Zilliz Cloud offer?
Zilliz Cloud pricing is not publicly listed and requires contacting their team to discuss your specific scale and use case requirements.
SourceRelated pages
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- Zilliz vs Weaviate
- Zilliz vs LlamaIndex
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- Zilliz vs Haystack
- Zilliz vs Ray
- Zilliz vs Keras
- Zilliz vs Kubeflow
- Zilliz vs Apache Spark MLlib
- Zilliz vs Weights & Biases
- Zilliz vs Alteryx
- Zilliz vs Anaconda
- Zilliz vs Domino Data Lab
- Zilliz vs Qdrant
- Zilliz vs Vespa
- Zilliz vs Marqo
- Zilliz vs DataStax
- Zilliz vs Elasticsearch
- Zilliz vs Meilisearch
- Zilliz vs Typesense
- Zilliz vs Materialize
- Zilliz vs OpenSearch
- Zilliz vs Apache Solr
- Zilliz vs Readyset
- Zilliz vs SurrealDB
- Zilliz vs StarRocks
- Zilliz vs Teradata
- Zilliz vs TIBCO Enterprise Message Service
- Zilliz vs Timeplus
- Zilliz vs turbopuffer

