Spice.ai

Spice.ai is a portable, open-source data and AI platform that fundamentally simplifies the modern data stack. It addresses the complexity, latency, and cost of traditional data pipelines by combining federated SQL query, hybrid search, and embedded LLM inference into a single, high-performance runtime. Designed for enterprise developers and data teams, Spice.ai empowers you to ground real-time applications and AI agents directly in your operational data estate, all while maintaining strict governance and achieving millisecond query performance without the need for complex ETL jobs.

Key Features

Spice.ai provides the foundational engine required to build real-time, data-intensive applications and production-grade AI agents, delivering speed, context, and control in a single environment.

🚀 Zero-ETL SQL Federation & Acceleration

Connect to and query operational databases, data lakes, and analytical warehouses across your enterprise instantly. Spice.ai accelerates your working data sets by materializing and indexing hot tables in-memory or on disk using engines like DuckDB and SQLite. This approach delivers sub-second query performance and eliminates data movement, resulting in up to 100x faster queries compared to traditional federated systems.

🔎 Integrated Hybrid Search Pipelines

Power highly context-aware applications by combining vector similarity, full-text, and relational filters directly within standard SQL. Spice.ai allows you to design and refine sophisticated retrieval pipelines where you can rank structured filters and semantic similarity to optimize relevance. You gain full SQL control to index and search billions of embeddings with low latency, ensuring users always receive relevant and robust results.

🧠 Embedded LLM Inference via SQL

Bring AI directly to where your data lives. Use the native AI() SQL function to call hosted or local LLMs (like OpenAI, Anthropic, or Bedrock) inline from your query layer. This capability allows you to translate text, generate summaries, classify entities, and enrich query results on your enterprise data, integrating AI workflows with standard SQL operations without requiring external APIs or complex glue code.

🛡️ Secure AI Sandboxing & Governance

Enable critical AI workflows like Retrieval-Augmented Generation (RAG) and autonomous agents while preserving enterprise security standards. Spice.ai allows you to provision isolated, least-privilege datasets and governed AI sandboxes. This ensures that LLMs can access only specific, audited data (table, column, or row level) without ever needing direct access to sensitive production databases, maintaining compliance and auditability.

Use Cases

Development teams utilize Spice.ai to simplify their architecture, accelerate performance, and securely integrate AI into mission-critical workloads.

1. Accelerating Real-Time Operational Dashboards and APIs Instead of building custom caches or managing multiple query engines, you can use Spice.ai to consolidate federated data sources (e.g., Postgres, S3, Snowflake) into a single, accelerated SQL layer. This allows you to serve virtualized views and APIs with millisecond latency, providing real-time data freshness for high-volume customer-facing applications or internal monitoring dashboards.

2. Building Data-Grounded AI Agents and RAG Pipelines Deploy AI agents that are grounded in accurate, real-time enterprise data. By leveraging the Hybrid Search capabilities, you can efficiently retrieve relevant context (vectors and structured data) and then use the embedded LLM inference function to generate accurate, hallucination-free responses. This integrated workflow ensures the probabilistic AI system is anchored by verifiable facts from your data estate.

3. Consolidating and Cost-Optimizing the Data Stack Replace fragmented architectures—which often involve separate ETL jobs, caching layers, and search indexes—with one lightweight, portable runtime. By federating and accelerating data in place, you significantly reduce data movement and redundant infrastructure, leading to up to 80% cost savings on data lakehouse and infrastructure spend while increasing data reliability for critical workloads.

Why Choose Spice.ai?

Spice.ai provides verifiable functional and measurable advantages that redefine how enterprises build data-intensive applications and deploy AI.

• Verifiable Performance Gains: Achieve up to 100x faster query performance by accelerating working data sets locally, ensuring applications remain responsive even as your data scales to billions of records.
• True Zero-ETL Architecture: Unlike traditional approaches that require data movement or staging, Spice.ai federates and accelerates data in a single runtime. You query across data lakes and operational databases without complex data pipelines, simplifying your stack and increasing data reliability (up to 2x increase reported).
• SQL-Native AI and Search: Spice.ai integrates advanced search and LLM calls directly into standard SQL workflows. Developers can use familiar tooling to compose hybrid search, refine results, and orchestrate AI inference, avoiding context switching and simplifying the development of complex RAG pipelines.
• Enterprise-Grade Control: Built-in governance, distributed observability, and AI sandboxing ensure that you can deploy real-time, AI-driven apps with the necessary security and compliance, providing full end-to-end tracing across SQL, search, and LLM calls.

Conclusion

Spice.ai delivers the agility and performance required for the AI era, unifying your data access, acceleration, search, and AI inference into one powerful, portable engine. By enabling fast, governed, and zero-ETL access to your enterprise data, Spice.ai allows you to focus on building innovative applications rather than managing infrastructure complexity.