# Spice AI

> Spice.ai is a data and AI platform that combines federated SQL query, hybrid search, and LLM inference in a portable, open-source runtime

Spice AI provides a unified data and AI infrastructure platform. Our open-source runtime enables federated SQL query across multiple data sources, hybrid vector and full-text search, and seamless LLM inference integration.

## Documentation

- [Spice AI Documentation](https://docs.spice.ai): Complete documentation for Spice AI
- [GitHub Repository](https://github.com/spiceai/spiceai): Open-source codebase and examples

## Local Content

- [About Us](https://spice.ai/about-us): Learn about Spice AI's mission, team, and vision for empowering developers to build intelligent apps with unified data and AI infrastructure.
- [2025 Spice AI Year in Review](https://spice.ai/blog/2025-spice-ai-year-in-review): From day one, Spice was designed to simplify building modern, intelligent applications. In 2025 that vision turned into reality.
- [A Developer's Guide to Understanding Spice.ai](https://spice.ai/blog/a-developers-guide-to-understanding-spice-ai): Learn what Spice.ai is, when to use it, and how it solves enterprise data challenges. A developer-focused guide to federation, acceleration, search, and AI.
- [A New Class of Applications That Learn and Adapt](https://spice.ai/blog/a-new-class-of-applications-that-learn-and-adapt): Explore the history of decision engines and how modern machine learning enables applications that learn, adapt, and make better decisions over time with Spice.ai.
- [Adding Spice - The Next Generation of Spice.ai OSS](https://spice.ai/blog/adding-spice-the-next-generation-of-spice-ai-oss): Learn how Spice.ai OSS was rebuilt in Rust to deliver fast, local SQL queries across databases, warehouses, and data lakes.
- [AI needs AI-ready data](https://spice.ai/blog/ai-needs-ai-ready-data): An introduction to AI-ready data and how Spice.ai handles normalization, encoding, and real-time data preparation for ML applications.
- [Spice.ai Now Supports Amazon S3 Vectors For Vector Search at Petabyte Scale!](https://spice.ai/blog/amazon-s3-vectors): Spice AI has partnered with AWS to integrate Amazon S3 Vectors into the Spice.ai Open Source data and AI compute engine.
- [Announcing Spice.ai Open Source 1.0-stable: A Portable Compute Engine for Data-Grounded AI - Now Ready for Production](https://spice.ai/blog/announcing-spice-ai-open-source-1-0-stable): Learn how Spice.ai OSS grounds AI in real data with federated query, fast retrieval, and portable deployment anywhere.
- [Spice Cloud v1.7.0: DataFusion v49, Full-Text Search Updates & More](https://spice.ai/blog/announcing-spice-cloud-v1-7-0): Spice Cloud v1.7.0 includes DataFusion v49, EmbeddingGemma support, and real-time indexing for full-text search
- [Apache Iceberg at Spice AI: How we Query, Accelerate, and Write to Open Table Formats](https://spice.ai/blog/apache-iceberg-at-spice-ai): A technical deep-dive into how Spice AI integrates Apache Iceberg for federated queries, sub-second acceleration, and ACID-compliant writes to open table formats.
- [Basis Set Ventures Deploys Spice.ai to Power Natural Language Queries and Mitigate Hallucinations](https://spice.ai/blog/basis-set-ventures-deploys-spice-ai): Basis Set Ventures uses Spice.ai Enterprise to power natural language searches directly against real-time datasets.
- [Spice AI Announces Contribution of TableProviders for PostgreSQL, MySQL, DuckDB, and SQLite to the Apache DataFusion Project](https://spice.ai/blog/contribution-of-tableproviders-to-datafusion): Spice AI has contributed new TableProviders for PostgreSQL, MySQL, DuckDB, and SQLite to the Apache DataFusion project.
- [Announcing Our Partnership with Databricks!](https://spice.ai/blog/databricks-partnership): Spice partners with Databricks to accelerate operational AI apps with fast SQL queries, Mosaic AI embeddings, and Unity Catalog governance.
- [Getting started with Amazon S3 Vectors and Spice](https://spice.ai/blog/getting-started-with-amazon-s3-vectors-and-spice): Learn how Spice AI integrates Amazon S3 Vectors for scalable, cost-effective vector search - combining semantic, full-text, and SQL queries in one runtime.
- [How we use Apache DataFusion at Spice AI](https://spice.ai/blog/how-we-use-apache-datafusion-at-spice-ai): A technical overview of how Spice extends Apache DataFusion with custom table providers, optimizer rules, and UDFs to power federated SQL, search, and AI inference.
- [Interviewing at Spice AI](https://spice.ai/blog/interviewing-at-spice-ai): A guide to the Spice AI interview process, covering what to expect at each stage, how we evaluate candidates, and tips for preparation.
- [Introducing Spice Cayenne: The Next-Generation Data Accelerator Built on Vortex for Performance and Scale](https://spice.ai/blog/introducing-spice-cayenne-data-accelerator): Spice Cayenne is the next-generation Spice.ai data accelerator built for high-scale and low latency data lake workloads.
- [Making Apps That Learn And Adapt](https://spice.ai/blog/making-apps-that-learn-and-adapt): Building intelligent applications is still too hard for most developers-not because ML is impossible, but because it's treated as something separate from the app.
- [Making Object Storage Operational for Real-Time and AI Workloads](https://spice.ai/blog/making-object-storage-operational): Transform object stores into real-time AI platforms. Spice adds federation, acceleration, hybrid search, and inference capabilities.
- [On Writing](https://spice.ai/blog/on-writing): Writing is fundamental to formalizing thoughts, communicating effectively, and is the ultimate creation tool.
- [Operationalizing Amazon S3 for AI: From Data Lake to AI-Ready Platform in Minutes](https://spice.ai/blog/operationalizing-amazon-s3-for-ai): Transform Amazon S3 from passive storage to an AI-ready platform. Real-world example using Spice and S3 for hybrid search and LLM inference.
- [Real-Time Control Plane Acceleration with DynamoDB Streams ](https://spice.ai/blog/real-time-acceleration-with-dynamodb-streams): How to sync DynamoDB data to thousands of nodes with sub-second latency using a two-tier architecture with DynamoDB Streams and Spice acceleration.
- [Real-Time Hybrid Search Using RRF: A Hands-On Guide with Spice](https://spice.ai/blog/real-time-hybrid-search-using-rrf): Learn how to build hybrid search with Reciprocal Rank Fusion (RRF) directly in SQL using Spice - combining text, vector, and time-based relevance in one query for faster, more accurate results.
- [Spice AI achieves SOC 2 Type II compliance](https://spice.ai/blog/spice-ai-achieves-soc-2-type-ii-compliance): Spice AI completes SOC 2 Type II audit, demonstrating enterprise-grade security and compliance for its data and AI infrastructure platform.
- [The Spice.ai for GitHub Copilot Extension is now available!](https://spice.ai/blog/spice-ai-for-github-copilot-extension-now-available): With the Spice.ai Extension, developers can interact with data, like product requirements documents (PRDs), tickets, and tabular data, from any external data source directly within GitHub Copilot. Save hours copying and pasting across various platforms, relevant data and answers are now surfaced in Copilot Chat, right when you need it.
- [Spice.ai is now generally available!](https://spice.ai/blog/spice-ai-is-now-generally-available): Spice.ai is now available for everyone, including a new community-centric developer hub and Community Edition complimentary for developers.
- [Faster, Simpler Dashboards with Spice and Power BI](https://spice.ai/blog/spice-and-power-bi): Spice AI built a Microsoft Power BI Connector on top of the Flight SQL ADBC driver that makes it easy for Power BI users to query across operational databases, analytical warehouses, and object stores.
- [Spice Cloud v1.10: Caching Acceleration Mode, DynamoDB Streams Support, & More!](https://spice.ai/blog/spice-cloud-v1-1-0): Spice v1.10 includes a new caching acceleration mode, a new DynamoDB Streams data connector in preview, Amazon S3 location-based pruning, S3 Tables write support, and several performance and security improvements.
- [Spice Cloud v1.11: Spice Cayenne Reaches Beta, Apache DataFusion v51, DynamoDB Streams Improvements, & More](https://spice.ai/blog/spice-cloud-v1-11): v1.11 brings Spice Cayenne to Beta, DataFusion v51 and Apache Arrow v57.2, improved DynamoDB Streams, and more.
- [Spice Cloud v1.8.0: Iceberg Write Support, Acceleration Snapshots & More](https://spice.ai/blog/spice-cloud-v1-8-0-iceberg-writes): Announcing Spice Cloud v1.8.0 - now with Iceberg write support, acceleration snapshots, partitioned S3 Vectors indexes & a new AI SQL function
- [Spice Cloud v1.9.0: Introducing the Spice Cayenne Data Accelerator](https://spice.ai/blog/spice-cloud-v1-9-0-cayenne-data-accelerator): Spice Cloud v1.9.0 adds the Cayenne Data Accelerator, Apache DataFusion v50, HTTP data connector support for querying endpoints as tables, and much more.
- [Spice Firecache | Cloud-Scale DuckDB](https://spice.ai/blog/spice-firecache): Cloud-Scale DuckDB
- [Spice OSS, rebuilt in Rust](https://spice.ai/blog/spice-oss-rebuilt-in-rust): Spice.ai OSS has been rebuilt from the ground up in Rust, delivering the performance, safety, and portability needed for production data infrastructure.
- [Getting Started with Spice.ai SQL Query Federation & Acceleration](https://spice.ai/blog/spice-sql-query-federation-acceleration): Learn how to use Spice.ai to federate and accelerate queries across operational and analytical systems with zero ETL.
- [Spice.ai's approach to Time-Series AI](https://spice.ai/blog/spiceais-approach-to-time-series-ai): Explore the challenges of time-series AI and why Spice.ai uses a data-driven reinforcement learning approach to help developers build adaptive, intelligent applications.
- [Spicepods: From Zero to Hero](https://spice.ai/blog/spicepods-from-zero-to-hero): A step-by-step guide to authoring a Spicepod from scratch and using it to build an application that learns and adapts over time.
- [Teaching Apps how to Learn with Spicepods](https://spice.ai/blog/teaching-apps-how-to-learn-with-spicepods): Learn how Spicepods define application goals, rewards, and learning behavior - making it easy for developers to build applications that learn and adapt over time.
- [True Hybrid Search: Vector, Full-Text, and SQL in One Runtime](https://spice.ai/blog/true-hybrid-search): Build hybrid search without managing multiple systems. Query vectors, run full-text search, and execute SQL in one unified runtime.
- [What Data Informs AI-driven Decision Making?](https://spice.ai/blog/what-data-informs-ai-driven-decision-making): Learn the three classes of data required for intelligent decision-making and how Spice.ai simplifies runtime data engineering for AI-powered applications.
- [Write to Apache Iceberg Tables with SQL in Spice](https://spice.ai/blog/write-to-apache-iceberg-tables-with-sql): Spice v1.8 adds native Apache Iceberg write support with standard SQL INSERT INTO statements. Build complete data workflows without ETL - query, accelerate, and write from one runtime.
- [Careers](https://spice.ai/careers): Join Spice AI and help build the future of data and AI infrastructure.
- [Contact](https://spice.ai/contact): Get in touch with the Spice AI team. Whether you're exploring enterprise deployments, pricing, integrations, or technical questions, we're here to help.
- [Spice.ai Cookbook](https://spice.ai/cookbook): A collection of guides and samples to help you build data-grounded AI apps and agents with Spice.ai Open-Source. Find ready-to-use examples for data acceleration, AI agents, LLM memory, and more.
- [AI Model Serving](https://spice.ai/feature/ai-model-serving): Serve, evaluate, and ground AI models directly inside Spice. Call LLMs locally or connect to hosted providers from one secure, high-performance runtime.
- [Distributed Query](https://spice.ai/feature/distributed-query): Scale beyond single-node limits with petabyte-scale, multi-node, distributed queries.
- [Edge to Cloud Deployments](https://spice.ai/feature/edge-to-cloud-deployments): Deploy Spice anywhere, from lightweight sidecars to enterprise clusters. Choose the architecture that fits your performance, scale, and governance needs.
- [MCP Server & Gateway](https://spice.ai/feature/mcp-server-gateway): Deploy MCP servers locally or over SSE, route tools to models, and expose Spice securely as an MCP gateway with full observability.
- [Real-Time Change Data Capture](https://spice.ai/feature/real-time-change-data-capture): Sync accelerated datasets with real-time changes using Change Data Capture (CDC) and maintain low-latency analytics without full-table refreshes.
- [Secure AI Sandboxing](https://spice.ai/feature/secure-ai-sandboxing): Safely connect AI to enterprise data. Spice isolates access for agents and models, enforcing least privilege, observability, and compliance across every query.
- [Get a demo](https://spice.ai/get-a-demo): Get in touch with the Spice AI team. Whether you're exploring enterprise deployments, pricing, integrations, or technical questions, we're here to help.
- [Home](https://spice.ai/home): Ground AI in enterprise data with zero ETL. Spice is an open-source SQL query & hybrid search engine built for data-intensive apps & AI agents.
- [Cybersecurity](https://spice.ai/industry/cybersecurity): Build fast, reliable, and intelligent cybersecurity applications. Spice delivers unified data access, real-time performance, and embedded AI integration across any environment.
- [Financial Services](https://spice.ai/industry/financial-services): Unify, govern, and accelerate sensitive financial data. Spice delivers federation, hybrid search, and integrated AI for regulated workloads.
- [SaaS](https://spice.ai/industry/saas): Power SaaS with live, governed data. Federate across warehouses and DBs, accelerate to millisecond latency, and add AI-all on one portable runtime.
- [Integrations](https://spice.ai/integrations): Spice offers 30+ integrations with leading databases, warehouses, data lakes, streaming systems, and more.
- [What is Apache Ballista?](https://spice.ai/learn/apache-ballista): Apache Ballista is a distributed SQL query engine that extends Apache DataFusion across multiple nodes. Learn how Ballista works, its architecture, and how it compares to Spark and Trino.
- [What is Apache DataFusion?](https://spice.ai/learn/apache-datafusion): Apache DataFusion is an open-source, extensible SQL query engine written in Rust. Learn how DataFusion works, its architecture, how it compares to Trino and DuckDB, and how teams extend it for production use cases.
- [What is BM25 Full-Text Search?](https://spice.ai/learn/bm25-full-text-search): BM25 (Best Match 25) is the standard ranking function for full-text search. Learn how BM25 scores documents using term frequency, inverse document frequency, and document length normalization, and how it compares to TF-IDF.
- [What is Change Data Capture (CDC)?](https://spice.ai/learn/change-data-capture): Change data capture (CDC) tracks row-level changes in databases and streams them in real time. Learn how CDC works, common patterns, and how it enables real-time data pipelines.
- [What is Data Acceleration?](https://spice.ai/learn/data-acceleration): Data acceleration caches frequently accessed data locally for sub-second query performance without moving data permanently. Learn how it works, acceleration strategies, and when to use it.
- [Data Virtualization vs Data Replication: How to Choose](https://spice.ai/learn/data-virtualization-vs-replication): Compare data virtualization and data replication -- two foundational approaches to data integration. Learn the key differences, tradeoffs, and when each approach is appropriate for your workloads.
- [What is Data Virtualization?](https://spice.ai/learn/data-virtualization): Data virtualization provides a unified view of data across multiple sources without physical replication. Learn how it works, how it compares to ETL, and when to use it.
- [What are Embeddings?](https://spice.ai/learn/embeddings): Embeddings are dense vector representations of text, images, or code that capture semantic meaning. Learn how embedding models work, what dimensions represent, and how embeddings enable semantic search, RAG, and classification.
- [Full-Text Search vs Vector Search: How to Choose](https://spice.ai/learn/full-text-search-vs-vector-search): Full-text search matches exact keywords using BM25 scoring, while vector search finds semantically similar content using embeddings. Learn the key differences, when to use each approach, and how hybrid search combines both for optimal results.
- [What is a Hybrid Data Architecture?](https://spice.ai/learn/hybrid-data-architecture): A hybrid data architecture combines application sidecars for sub-millisecond reads with a centralized cluster for data ingestion, acceleration, and distributed compute. Learn how the sidecar-cluster pattern works and when to use it.
- [What is Hybrid Search?](https://spice.ai/learn/hybrid-search): Hybrid search combines vector similarity search with keyword matching to deliver more accurate results than either method alone. Learn how hybrid search works, ranking algorithms like RRF, and when to use it.
- [Learn Data & AI](https://spice.ai/learn/index): Learn about the core technologies behind Spice.ai -- SQL federation, data virtualization, RAG, hybrid search, change data capture, the Model Context Protocol, and more.
- [What is LLM Inference?](https://spice.ai/learn/llm-inference): LLM inference is the process of generating text by running input through a trained large language model. Learn how inference works, key performance metrics, and optimization techniques like KV caching, quantization, and speculative decoding.
- [What is LLM Tool Calling?](https://spice.ai/learn/llm-tool-calling): LLM tool calling is a capability where a model outputs structured function calls instead of plain text, enabling AI agents to query databases, call APIs, and take actions. Learn how tool calling works, security considerations, and how MCP standardizes tool use.
- [What is the Model Context Protocol (MCP)?](https://spice.ai/learn/model-context-protocol): The Model Context Protocol (MCP) is an open standard for connecting AI models to external data and tools. Learn how MCP works, its architecture, and how MCP servers enable agentic AI.
- [RAG vs Fine-Tuning: How to Choose](https://spice.ai/learn/rag-vs-fine-tuning): RAG retrieves external data at inference time while fine-tuning embeds knowledge into model weights. Learn the key differences, tradeoffs, and when to use each approach for production AI applications.
- [What is Retrieval Augmented Generation (RAG)?](https://spice.ai/learn/retrieval-augmented-generation): Retrieval augmented generation (RAG) grounds LLM responses in real data by retrieving relevant context at inference time. Learn how RAG works, its architecture, and production best practices.
- [Sidecar vs Microservice Architecture: How to Choose](https://spice.ai/learn/sidecar-vs-microservice-architecture): Sidecar and microservice are two deployment architectures for data and AI runtimes. Learn the key differences in latency, scaling, resource usage, and when to use each pattern.
- [SQL Federation vs ETL: How to Choose](https://spice.ai/learn/sql-federation-vs-etl): SQL federation and ETL are two approaches to accessing data across distributed systems. Learn the key differences, when to use each, and how modern platforms combine both for real-time performance.
- [What is SQL Federation?](https://spice.ai/learn/sql-federation): SQL federation lets you query multiple databases and data sources with a single SQL statement, without moving or copying data. Learn how federated queries work, key benefits, and common use cases.
- [What is Text-to-SQL?](https://spice.ai/learn/text-to-sql): Text-to-SQL uses large language models to translate natural language questions into SQL queries. Learn how it works, common approaches, key challenges, and production patterns.
- [What is Vector Search?](https://spice.ai/learn/vector-search): Vector search (semantic search) finds the most similar items by comparing vector embeddings using distance metrics like cosine similarity. Learn how ANN algorithms like HNSW work, vector index tradeoffs, and when to use vector search vs. keyword search.
- [What is Vortex?](https://spice.ai/learn/vortex): Vortex is an open-source compressed columnar file format designed for analytical queries. Learn how Vortex compares to Parquet, its adaptive encoding system, and when to use it over Parquet for analytical workloads.
- [Spice AI for AWS](https://spice.ai/partners/aws): Build Fast, Scalable AI Applications with Spice AI and Amazon Web Services
- [Spice AI for Databricks](https://spice.ai/partners/databricks): Build Fast, Accurate AI Applications with Spice AI and Databricks
- [Spice AI for NetApp](https://spice.ai/partners/netapp): Build Accelerated, Data-Grounded AI Applications with Spice AI and NetApp ONTAP
- [Partners](https://spice.ai/partners): Partner with Spice AI to deliver faster data, search, and AI solutions for your customers. Build integrations, co-market solutions, and power data-intensive applications and AI agents together.
- [Hybrid SQL Search](https://spice.ai/platform/hybrid-sql-search): Combine vector similarity, full-text, and keyword search in one SQL query. Fast, scalable, and production-ready.
- [LLM Inference](https://spice.ai/platform/llm-inference): Call LLMs directly from SQL. Generate, summarize, and enrich data inline using the SQL AI function or natural language queries.
- [SQL Federation & Acceleration](https://spice.ai/platform/sql-federation-acceleration): Query any data source with sub-second speed. Spice combines SQL federation and acceleration in a single runtime with zero ETL.
- [Spice Cloud Plans](https://spice.ai/pricing/cloud): Flexible cloud pricing plans for teams of all sizes
- [Pricing](https://spice.ai/pricing): Start free and deploy anywhere: on your laptop, on-prem, at the edge, or in the cloud. Flexible pricing designed for teams building data-intensive applications & AI agents.
- [Privacy Policy](https://spice.ai/privacy-policy): Your privacy matters. Read Spice AI's policy on data collection, usage, protection, and your rights to control your personal information.
- [Security](https://spice.ai/security): Learn how Spice AI protects your data with SOC 2 Type II compliance, strong access controls, encryption, secure coding, and a principled, defense-in-depth approach.
- [Application Search](https://spice.ai/use-case/application-search): Add fast, relevant search to your app with hybrid SQL search. Governed, low-latency, and easy to ship anywhere.
- [Datalake Accelerator](https://spice.ai/use-case/datalake-accelerator): Accelerate query performance in your data lake with Spice. Run SQL locally on federated datasets for up to 100x faster performance.
- [Operational Data Lakehouse](https://spice.ai/use-case/operational-data-lakehouse): Federate, accelerate, and serve data-intensive apps and AI agents directly from object storage with millisecond performance.
- [Retrieval-Augmented Generation](https://spice.ai/use-case/retrieval-augmented-generation): Build more accurate and trustworthy RAG systems. Spice unifies SQL federation, vector search, and model inference for data-grounded AI responses.
- [Secure AI Agents](https://spice.ai/use-case/secure-ai-agents): Build and deploy AI agents that are secure by design. Federate-governed context, enforce policy inline, and route to any model with full auditability.

## Blog

- [2025 Spice AI Year in Review](https://spice.ai/blog/2025-spice-ai-year-in-review)
- [A Developer's Guide to Understanding Spice.ai](https://spice.ai/blog/a-developers-guide-to-understanding-spice-ai)
- [A New Class of Applications That Learn and Adapt](https://spice.ai/blog/a-new-class-of-applications-that-learn-and-adapt)
- [Adding Spice - The Next Generation of Spice.ai OSS](https://spice.ai/blog/adding-spice-the-next-generation-of-spice-ai-oss)
- [AI needs AI-ready data](https://spice.ai/blog/ai-needs-ai-ready-data)
- [Spice.ai Now Supports Amazon S3 Vectors For Vector Search at Petabyte Scale!](https://spice.ai/blog/amazon-s3-vectors)
- [Announcing Spice.ai Open Source 1.0-stable: A Portable Compute Engine for Data-Grounded AI - Now Ready for Production](https://spice.ai/blog/announcing-spice-ai-open-source-1-0-stable)
- [Spice Cloud v1.7.0: DataFusion v49, Full-Text Search Updates & More](https://spice.ai/blog/announcing-spice-cloud-v1-7-0)
- [Apache Iceberg at Spice AI: How we Query, Accelerate, and Write to Open Table Formats](https://spice.ai/blog/apache-iceberg-at-spice-ai)
- [Basis Set Ventures Deploys Spice.ai to Power Natural Language Queries and Mitigate Hallucinations](https://spice.ai/blog/basis-set-ventures-deploys-spice-ai)
- [Spice AI Announces Contribution of TableProviders for PostgreSQL, MySQL, DuckDB, and SQLite to the Apache DataFusion Project](https://spice.ai/blog/contribution-of-tableproviders-to-datafusion)
- [Announcing Our Partnership with Databricks!](https://spice.ai/blog/databricks-partnership)
- [Getting started with Amazon S3 Vectors and Spice](https://spice.ai/blog/getting-started-with-amazon-s3-vectors-and-spice)
- [How we use Apache DataFusion at Spice AI](https://spice.ai/blog/how-we-use-apache-datafusion-at-spice-ai)
- [Interviewing at Spice AI](https://spice.ai/blog/interviewing-at-spice-ai)
- [Introducing Spice Cayenne: The Next-Generation Data Accelerator Built on Vortex for Performance and Scale](https://spice.ai/blog/introducing-spice-cayenne-data-accelerator)
- [Making Apps That Learn And Adapt](https://spice.ai/blog/making-apps-that-learn-and-adapt)
- [Making Object Storage Operational for Real-Time and AI Workloads](https://spice.ai/blog/making-object-storage-operational)
- [On Writing](https://spice.ai/blog/on-writing)
- [Operationalizing Amazon S3 for AI: From Data Lake to AI-Ready Platform in Minutes](https://spice.ai/blog/operationalizing-amazon-s3-for-ai)
- ... and 19 more blog posts

## Optional

- [Full LLMs Content](https://spice.ai/llms-full.txt): Complete content for deep context
- [Sitemap](https://spice.ai/sitemap.xml): Full sitemap for crawling