Databases · head to head
Zilliz vs Pinecone

Zilliz
Databases
Managed vector database and vector lakebase for AI applications
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Zilliz pricing structure not publicly disclosed, requires sales contact; 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
- They diverge on capability: Zilliz covers Vector indexing, Pinecone covers Vector similarity search.
Where they differ
Only the attributes on which Zilliz and Pinecone 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 Zilliz
- Vector indexing
- Distributed architecture
- SQL interface
- Tensor support
- Real-time search
- Cloud-native
- Open-source compatible
Only in Pinecone
- Vector similarity search
- Metadata filtering
- Namespace partitioning
- Real-time updates
- Hybrid search
- OpenAI
- Cohere
- LangChain
What people use each for
The jobs each tool is most often brought in to do.
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
Pinecone
- Vector database for AI/ML applicationsnot Zilliz
- Semantic search implementationnot Zilliz
- Recommendation systemsnot Zilliz
- RAG (Retrieval-Augmented Generation) architecturesnot Zilliz
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
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
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
Pricing, plan by plan
Zilliz
FreeNo published plan breakdown. See the Zilliz review.
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
Which should you pick?
Choose Zilliz if
- You need vector indexing.
- You want to start without paying.
- You work on Cloud, Self-hosted.
- You also want distributed architecture.
Choose Pinecone if
- You need vector similarity search.
- You want to start without paying.
- You also want metadata filtering.
Questions people ask
- Is Zilliz or Pinecone better?
- Neither clearly leads. Zilliz starts at Free and Pinecone at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Zilliz or Pinecone?
- Zilliz starts at Free and Pinecone at Free.
- Does Zilliz or Pinecone run on more platforms?
- Zilliz runs on Cloud, Self-hosted. Pinecone runs on Web.
- Can I use Zilliz for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Zilliz best used for?
- Zilliz is most often used for build retrieval-augmented generation (rag) systems, implement semantic search over documents, create multimodal search with text and images, power recommendation engines with vector similarity. Of those, build retrieval-augmented generation (rag) systems and implement semantic search over documents are not what Pinecone is typically brought in for.
- What can Zilliz do that Pinecone cannot?
- Zilliz covers Vector indexing, Distributed architecture, SQL interface, Tensor support. Pinecone covers Vector similarity search, Metadata filtering, Namespace partitioning, Real-time updates.
Answered from the vendors’ own pages
Zilliz: 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: 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: 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 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: Is Milvus open-source?
Yes, Milvus is open-source under the Apache License 2.0 and is part of the LF AI & Data Foundation.
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: 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
Other head to heads
- Zilliz vs Cockroach Labs
- Zilliz vs PostgreSQL
- Zilliz vs Airtable
- Zilliz vs Amazon Aurora
- Zilliz vs Elasticsearch
- Zilliz vs Apache Kafka
- Zilliz vs PlanetScale
- Zilliz vs Meilisearch
- Zilliz vs Turso
- Zilliz vs Azure SQL
- Zilliz vs ClickHouse
- Zilliz vs Couchbase
- Zilliz vs DuckDB
- Zilliz vs MariaDB
- Zilliz vs Oracle Database
- Zilliz vs DataGrip
- Zilliz vs Firebolt
- Zilliz vs Google Cloud SQL
- Zilliz vs AWS SageMaker
- Zilliz vs Google Vertex AI
- Zilliz vs Azure Machine Learning
- Zilliz vs DataRobot
- Zilliz vs MLflow
- Zilliz vs Snowflake
- Zilliz vs TensorFlow
- Zilliz vs Comet ML
- Zilliz vs Jupyter
- Zilliz vs LangChain
- Zilliz vs Python
- Zilliz vs PyTorch
- Zilliz vs scikit-learn
- Zilliz vs Apache Spark MLlib
- Zilliz vs Weaviate
- Zilliz vs Weights & Biases
- Zilliz vs Alteryx
- Zilliz vs Anaconda
- Pinecone vs Cockroach Labs
- Pinecone vs PostgreSQL
- Pinecone vs Airtable
- Pinecone vs Amazon Aurora
- Pinecone vs Elasticsearch
- Pinecone vs Apache Kafka
- Pinecone vs PlanetScale
- Pinecone vs Meilisearch
- Pinecone vs Turso
- Pinecone vs Azure SQL
- Pinecone vs ClickHouse
- Pinecone vs Couchbase
- Pinecone vs DuckDB
- Pinecone vs MariaDB
- Pinecone vs Oracle Database
- Pinecone vs DataGrip
- Pinecone vs Firebolt
- Pinecone vs Google Cloud SQL
- Pinecone vs AWS SageMaker
- Pinecone vs Google Vertex AI
- Pinecone vs Azure Machine Learning
- Pinecone vs DataRobot
- Pinecone vs MLflow
- Pinecone vs Snowflake
- Pinecone vs TensorFlow
- Pinecone vs Comet ML
- Pinecone vs Jupyter
- Pinecone vs LangChain
- Pinecone vs Python
- Pinecone vs PyTorch
- Pinecone vs scikit-learn
- Pinecone vs Apache Spark MLlib
- Pinecone vs Weaviate
- Pinecone vs Weights & Biases
- Pinecone vs Alteryx
- Pinecone vs Anaconda

