Pinecone as an alternative to Google Vertex AI Matching Engine
Pinecone makes it easy to build high-performance vector search applications. It’s a managed, cloud-native vector database with a simple API that you can start using and scaling within minutes. Developers love the simple API, full metadata filtering, extensive documentation, and low cost.
| Google Vertex AI Matching Engine | Pinecone | |
| Cost by index size | ||
| Data processing | $3 per GiB | Free |
| 100k vectors | $1.064 | $0.096 |
| 500k vectors | $1.064 | $0.096 |
| 1M vectors | $1.064 | $0.096 |
| 5M vectors | $1.064 | $0.096 |
| 10M vectors | $1.064 | $0.192 |
| Features | ||
| Support |
Vertex AI
Vertex AI Matching Engine is a subset of Vertex AI services for semantic search. It includes (1) a set of model architectures for training embeddings, and (2) an approximate nearest neighbor (ANN) service. The two parts can be used together or separately. For instance, it is possible to use the Matching Engine’s model training capabilities to generate embeddings, and then use these embeddings elsewhere (eg, in Pinecone). The Vertex AI Matching Engine ANN service is based on ScaNN, Google’s ANN algorithm.
Pinecone
Pinecone makes it easy to build high-performance vector search applications. It’s a managed, cloud-native vector database with a simple API and no infrastructure hassles.
Pinecone does not host or run embeddings models. You need to run your embedding models and perform your transformations outside of Pinecone, and only then you can upload your embeddings into Pinecone.
Pinecone is cost-effective and provides great performance at scale. However, customers say developer experience is the biggest reason they chose Pinecone. The simple API, web console, and documentation make it easy and quick to deploy and manage the vector database.
What can you do with vector search?
Once you have vector embeddings, manage and search through them in Pinecone to power semantic search, recommenders, and other applications that rely on relevant information retrieval.
Search
Semantic search, Product search, Multi-modal search, Question-Answering
Generation
Chatbots, Text generation, Image generation
Security
Anomaly Detection, Fraud Detection, Bot/threat detection, Identity verification
Personalization
Recommendations, Feed ranking, Ad targeting, Candidate selection
Analytics & ML
Data labeling, Model training, Molecular search, Generative AI
Data Management
Pattern matching, Deduplication, Grouping, Tagging
Fast, fresh, and filtered vector search.
Fast
Ultra-low query latency, even with billions of items. Give users a great experience.
Fresh
Live index updates when you add, edit, or delete data. Your data is ready right away.
Filtered
Combine vector search with metadata filters for more relevant and faster results.
Trusted by thousands of engineering teams building AI applications
We wanted sub-second vector search across millions of alerts, an API interface that abstracts away the complexity, and we didn’t want to have to worry about database architecture or maintenance.
Peter Silberman
Chief Technology Officer, Expel
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