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Power Your AI Applications with Expert pgvector Support

Get expert support to run pgvector so you can build reliable, high-performance AI applications.

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pgvector powers AI. Percona powers pgvector.

pgvector brings native vector search to PostgreSQL, enabling AI use cases like semantic search, recommendations, and anomaly detection. But with large data volumes, approximate search algorithms, and pgvector performance tuning requirements, scaling pgvector for production can be complex.

Percona helps you run pgvector with confidence. Whether you’re building a proof of concept or deploying AI applications at scale, our experts ensure seamless integration, optimization, and ongoing support.

Why Percona for pgvector support?

Optimize pgvector for speed and scale

We eliminate bottlenecks and
fine-tune performance so developers and IT teams can move faster and power applications that require:

  • Vector storage
  • Similarity search
  • SQL integration
  • Enterprise scale
 

Build secure, compliant AI applications

Safeguard your PostgreSQL deployment with tested security protocols and best practices, ensuring your AI applications remain secure and compliant.

Cut costs
– even when scaling

As AI workloads grow, keep your pgvector deployment efficient with open source software and flexible deployment options — so you avoid rising infrastructure costs.

Own your data. Control your deployment.

Train and run AI applications on your terms — whether on-prem for maximum data protection, in hybrid environments to reduce cloud costs, or in the cloud for scalable performance.

FAQ’s

Have Questions?

pgvector is an extension for PostgreSQL that enables the storage and searching of vector embeddings. It enhances your existing PostgreSQL database with powerful similarity search capabilities, which are fundamental for many AI applications.

pgvector allows you to perform vector similarity searches directly within your PostgreSQL database. This capability is crucial for AI-driven features like semantic search, product recommendations, image recognition, and anomaly detection, where finding “closest match” data is essential.

Vector search is a method used to find items that are semantically similar, not just keyword matches. In AI, data like text, images, and audio is converted into numerical representations called vectors. Vector search finds the closest vectors in a database, enabling more intuitive and human-like search and recommendation experiences.

While pgvector is powerful, using it in a production environment presents challenges. These can include handling large volumes of data, choosing and tuning the right approximate search algorithms for performance, and optimizing indexing to maintain speed and accuracy as your application scales.

Dedicated support from experts ensures that your pgvector implementation is optimized for performance, reliability, and scale. Specialists can help you navigate the complexities of integration, fine-tune performance to eliminate bottlenecks, and ensure your AI application runs smoothly, allowing your development teams to focus on building features.

Percona helps by fine-tuning your pgvector and PostgreSQL setup to eliminate performance bottlenecks. Our experts focus on optimizing everything from indexing strategies and search algorithms to database configuration, ensuring your AI applications are fast and can scale efficiently as data volumes grow.

Percona assists in safeguarding your entire PostgreSQL environment using tested security protocols and industry best practices. This ensures your data remains protected and your AI applications meet necessary security and compliance standards.

Yes. By optimizing your database performance and leveraging open source software, Percona helps you maintain an efficient pgvector deployment. This prevents rising infrastructure costs, even as your AI workloads and data increase.

Percona offers you complete control and flexibility over your deployment. You can run your pgvector-powered applications on your own terms—whether on-premises for maximum data control, in the cloud for scalability, or in a hybrid environment to balance costs and performance.
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