# Vertex AI Matching Engine Alternative for Vector Search

> 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.

## 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.

[Create Account](https://app.pinecone.io/)

[Contact Us](https://www.pinecone.io/contact)

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### Cost by index size

| Feature | Google Vertex AI Matching Engine | Pinecone |
| --- | --- | --- |
| Overall | Partial | Full |
| 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

| Feature | Google Vertex AI Matching Engine | Pinecone |
| --- | --- | --- |
| Overall | Partial | Full |
| Vector search (ANN) | Yes | Yes |
| Real-time Index Updates | No | Yes |
| Full metadata filtering | No | Yes |
| Namespacing | No | Yes |
| Hybrid search (sparse + dense vectors) | No | Yes |
| Run in GCP | Yes | Yes |
| Run in AWS | No | Yes |

### Support

| Feature | Google Vertex AI Matching Engine | Pinecone |
| --- | --- | --- |
| Overall | Partial | Full |
| Premium support | Costs $29–$12.5k/month + 3–4% of monthly charges | Included |

[Get Started](https://app.pinecone.io/)

### Vertex AI

[Vertex AI Matching Engine](https://cloud.google.com/vertex-ai/docs/matching-engine/overview) 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](https://cloud.google.com/vertex-ai/docs/matching-engine/ann-service-overview) is based on ScaNN, Google’s ANN algorithm.

### Pinecone

[Pinecone](https://www.pinecone.io/) 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.

![alt](https://cdn.sanity.io/images/vr8gru94/production/764bd5d27b72348882b9919fdb77317491445b34-1236x834.png)

### 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

- ![Microsoft](https://cdn.sanity.io/images/vr8gru94/production/9f1a1e8edfb77639ee53acf49a1f8ec3673813d7-301x62.png)
- ![Hubspot](https://cdn.sanity.io/images/vr8gru94/production/ab7219a1516e1c83a82fada65af94ee34209917e-1000x284.png)

- ![Shopify](https://cdn.sanity.io/images/vr8gru94/production/b05bb43481cf5a6489cc781e3d757eba90c4b1a5-500x142.png)
- ![GoDaddy](https://cdn.sanity.io/images/vr8gru94/production/716e14b46c46f8d90d434da1cbaf0899cd580ab3-341x70.png)
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## Talk to our experts about Pinecone vs. Vertex AI Matching Engine from Google

Send us your questions about Pinecone and why developers choose us. We’ll schedule a time to learn and share more with you.

[Complete the form in a browser](https://www.pinecone.io/lp/vertex/#contact)

[View this page on Pinecone](https://www.pinecone.io/lp/vertex/)