Nexla + Vespa.ai: The Power Duo for AI-Ready Data Pipelines

Nexla and Vespa.ai just announced a partnership that eliminates the traditional headaches of moving data into AI-powered search and vector database systems.

Who is Vespa AI?

Vespa.ai is a production-grade AI search platform that combines a distributed text search engine with a vector database. It’s the infrastructure behind demanding applications like Perplexity AI and powers search, recommendations, personalization, and RAG at enterprise scale.

Core capabilities:

• Hybrid search: Combines vector search (ANN), lexical search (BM25), and structured data queries in a single request
• Real-time inference: Executes machine-learned models and LLM calls directly where data is stored, eliminating data movement bottlenecks
• Massive scalability: Handles billions of documents with sub-100ms query latency and up to 100k writes per second per node
• Native RAG support: Built-in retrieval-augmented generation with phased ranking and LLM integration
• Multi-vector and multi-modal: Supports ColPali for visual documents, token-level embeddings, and complex tensor operations

GigaOm recognized Vespa as a leader in vector databases for two consecutive years, noting its performance advantages over alternatives like Elasticsearch, up to 12.9X higher throughput per CPU core for vector searches.

The Partnership: Nexla’s Vespa Connector and Plugin

Nexla recently launched a Vespa connector that makes data integration with Vespa.ai seamless. The integration includes:

1. Vespa Connector in Nexla: Handles all data piping from sources like Amazon S3, PostgreSQL, Pinecone, Snowflake, and others directly into Vespa
   
2. Vespa Nexla Plugin CLI: Automatically generates draft Vespa application packages (including schema files) directly from a Nexset, eliminating manual configuration (View in GitHub)
   

This means you can move data from S3 to Vespa, migrate from Pinecone to Vespa, or sync PostgreSQL to Vespa, all without writing a single line of code.

When Nexla Customers Should Use Vespa

You’re a Nexla customer. Use Vespa when you need:

• Advanced AI search and RAG applications: If you’re building intelligent search, recommendation systems, or RAG applications that require hybrid search (combining semantic vector search with keyword matching and metadata filtering), Vespa is purpose-built for this. Nexla gets your data into Vespa; Vespa delivers production-grade AI search with machine-learned ranking.
• Real-time, high-scale query performance: When you need to serve thousands of queries per second across billions of documents with sub-100ms latency, Vespa’s distributed architecture scales horizontally without compromising quality. Nexla ensures your data flows continuously into Vespa with incremental updates and CDC support.
• Complex ranking and inference: If your use case requires multi-phase ranking, custom ML models, or LLM integration at query time, Vespa executes these operations locally where data lives, avoiding costly data movement. Nexla prepares and transforms your data into the exact schema Vespa needs.
• Cost efficiency at scale: Vespa delivers 5X infrastructure cost savings compared to alternatives like Elasticsearch while handling vector, lexical, and hybrid queries. Nexla minimizes integration costs by automating pipeline creation and schema management.

When Vespa Customers Should Use Nexla

You’re a Vespa customer. Use Nexla when you need:

• Multi-source data consolidation: Vespa is your search and inference engine, but data lives everywhere: S3 buckets, PostgreSQL databases, Snowflake warehouses, Salesforce CRMs, APIs, and files. Nexla connects to 500+ sources with bidirectional connectors and consolidates data into Vespa without custom ETL scripts.
• Automated schema generation and management: Instead of manually writing Vespa schema files and managing schema evolution, Nexla’s Plugin CLI auto-generates schemas from your Nexsets. As source schemas change, Nexla’s metadata intelligence detects changes and propagates them downstream automatically.
• Data transformation and enrichment: Before data hits Vespa, it often needs cleaning, filtering, enrichment, or format conversion. Nexla provides a no-code transformation library and supports custom SQL, Python, or JavaScript, all without maintaining separate ETL infrastructure.
• Vector database migration: Moving from Pinecone, Weaviate, or another vector database to Vespa? Nexla handles the migration with zero code, extracting records, transforming data to match Vespa’s schema, and syncing documents continuously.
• Data quality and monitoring: Nexla continuously monitors data flows with built-in validation rules, error handling, and automated alerts. When data quality issues arise, Nexla quarantines bad records and provides audit trails, ensuring Vespa always receives clean, trustworthy data.
• Real-time and streaming pipelines: Vespa supports real-time updates, but getting real-time data from streaming sources (Kafka, APIs, databases with CDC) requires integration logic. Nexla handles streaming, batch, and hybrid integration styles, optimizing throughput and latency for each source type.

Conclusion

Nexla handles the “getting data ready” problem. Vespa handles the “doing powerful things with data” problem.

If you’re a Nexla customer building AI applications, Vespa gives you production-grade vector search, hybrid retrieval, and RAG capabilities at any scale. If you’re a Vespa customer struggling with data integration complexity, Nexla eliminates months of pipeline development and makes multi-source data flows conversational.

Together, they solve the full stack: Nexla transforms messy, scattered data into clean, schema-validated data products, and Vespa turns those data products into blazing-fast AI-powered search, recommendations, and generative experiences.

Ready to Build Production-Grade AI Search Without the Integration Complexity?

• For Nexla Customer: Start building AI-powered search and RAG applications with Vespa’s production-grade infrastructure. Explore the Vespa Connector
• For Vespa Customers:  Eliminate months of pipeline development with Nexla’s automated data integration. Try Express for conversational pipeline building

Or schedule a demo to see the Nexla+Vespa integration in action.

FAQs

What is Vespa.ai, and what makes it different from other vector databases?

Vespa.ai is a production-grade AI search platform combining distributed text search with vector databases. It delivers hybrid search (vector + lexical + structured queries in one request), real-time inference, sub-100ms query latency at billions of documents scale, and 12.9X higher throughput than Elasticsearch for vector searches.

How does Nexla’s Vespa connector simplify data integration?

Nexla’s Vespa connector automates pipelines from 500+ sources (S3, PostgreSQL, Pinecone, Snowflake) to Vespa with zero code. The Vespa Plugin CLI auto-generates schema files from Nexsets, eliminating manual configuration. It handles transformations, schema evolution, and continuous sync automatically.

When should Nexla customers use Vespa for their AI applications?

Use Vespa when building advanced AI search and RAG applications requiring hybrid search, real-time query performance (sub-100ms latency at scale), complex ranking with ML models or LLMs at query time, or cost-efficient infrastructure (5X savings vs. Elasticsearch). Nexla delivers clean, validated data; Vespa powers intelligent search.

When should Vespa customers use Nexla for data pipeline management?

Use Nexla for multi-source consolidation (500+ connectors), automated schema generation and evolution tracking, data transformation and enrichment before indexing, vector database migration from Pinecone or Weaviate, data quality monitoring with validation, and real-time streaming from Kafka or CDC sources.

How does the Nexla-Vespa integration support vector database migrations?

Nexla handles migrations from Pinecone, Weaviate, or other vector databases to Vespa with zero code. It extracts records, transforms data to match Vespa schemas, syncs documents continuously, and monitors data quality throughout the migration, eliminating custom ETL scripts and reducing migration time from months to days.

What is hybrid search, and why does it matter for RAG applications?

Hybrid search combines vector search (semantic similarity), lexical search (keyword matching), and structured data filtering in a single query. This improves RAG accuracy by retrieving documents based on meaning AND specific terms, reducing hallucinations from purely semantic retrieval that might miss critical keywords.

How does Nexla ensure data quality for vector database indexing?

Nexla provides continuous monitoring with built-in validation rules, error handling, and automated alerts. Bad records are quarantined before reaching Vespa, audit trails track data lineage, and schema drift detection prevents indexing errors, ensuring Vespa always receives clean, trustworthy data for accurate search results.