# Materialize > Official Materialize business agent. Materialize provides a live context layer and real-time SQL platform that lets engineering and data teams build… ## Ask it a question Materialize answers for itself at https://api.hailera.com/mcp/materialize. A client that speaks MCP can connect to that address; the tool is ask_materialize and the protocol is 2026-07-28. Where this file and the agent disagree, the agent is current. ## About Materialize What Materialize is Materialize is a live context layer and real-time SQL platform for engineering and data teams. It transforms constantly changing, siloed operational data into continuously updated data products that applications, AI agents, and services can query in real time. Materialize is typically used by teams building: - AI agents that need fresh operational context - Interactive search and recommendation experiences - Event-driven architectures - Data-intensive user interfaces What Materialize does - Integrates data from many sources – Continuously ingests and unifies data from operational databases (such as Postgres, MySQL, SQL Server), ERPs, CRMs, Kafka, and other systems. - Uses SQL to build real-time data products – Engineers write standard SQL to define live, canonical business objects and views. Materialize incrementally maintains those views as source data changes. - Builds a live context graph – Real-time data products can be linked into a continuously updated context graph that agents and services can query directly. - Offloads heavy queries from operational systems – Complex, data-intensive queries are answered from incrementally maintained views in Materialize, rather than from OLTP databases or MCP endpoints. Key benefits - Fresh, trustworthy context – Maintains an up-to-the-second view of operational data, suitable for powering applications and AI agents. - Incremental computation – Uses a breakthrough incremental computation engine that does only the minimal work required to keep views up to date as data changes. - Low latency – Designed for interactive workloads, with end-to-end latency observed in production typically at or below about one second in supported regions. - Scalability and efficiency – Separates storage from compute and scales beyond local memory to support demanding operational workloads economically. - Accessible to existing teams – Any engineer who knows SQL can define new data products or context building blocks without needing specialized stream processing expertise. Representative use cases - Providing live context to AI agents via MCP so they can read, act, and read again against current data. - Powering interactive vector search and RAG pipelines with continuously updated embeddings and attributes. - Enabling real-time feature stores and scoring for machine learning use cases. - Supporting event-driven architectures where services respond to live, incrementally updated views. - Powering rich, data-intensive UIs that require fresh, denormalized views of operational data. Source: https://materialize.com ## Where Materialize works Lists San Carlos and Enterprise as a location. Names San Carlos and Enterprise as a service area. Areas beyond these are not published. ## Prices Materialize offers multiple pricing options depending on how you deploy and scale the platform. The details below summarize what is published on the Materialize pricing page; specific values and availability may change over time, so always refer to the live pricing page or contact Materialize for the most current information. Cloud On-Demand Fully managed SaaS, paid on demand. - Who it is for – Teams that want to get started quickly with pay-as-you-go pricing. - Billing – Usage-based, with monthly billing; pay for compute as you go and cancel at any time. - Compute pricing – List price from around $1.50 per Compute Credit in select regions at the time of writing. - Storage pricing – Example list prices on the public page include per-GB-per-hour storage rates (with lower rates on certain plans). - Networking pricing – Example list prices on the public page include per-GB networking rates. - Support and setup – Self-service setup with support via the Matty AI chatbot and helpdesk tickets. Clusters in Cloud are sized in tiers (for example, M.1-nano through M.1-8xlarge). Each cluster size corresponds to a certain amount of memory capacity and an associated Compute Credits per hour rate. Materialize notes that these values are illustrative and may change; consult the live pricing page for the latest cluster sizing and rates. Cloud Capacity Fully managed SaaS with capacity-based pricing. - Who it is for – Teams needing predictable costs, production scale, and higher-touch support. - Billing – Annual, prepaid plans with volume discounts off list prices. - Support and onboarding – Includes a dedicated account team plus guided onboarding and setup. - Benefits – Access to the same technical platform as Cloud On-Demand, with pricing structured around committed spend rather than purely usage-based billing. Self-managed licenses Community license - Type – Self-Managed Community License. - Pricing – Free forever within documented usage limits (for example, up to 24 GiB memory and 48 GiB disk at the time of writing). - Intended use – Local development, evaluation, and smaller-scale workloads. Enterprise license - Type – Self-Managed Enterprise License. - Who it is for – Large-scale, production deployments that require priority support and unlimited scale. - Pricing model – Annual license; commercial terms are agreed with Materialize based on needs and scale. Important notes - All specific prices, limits, cluster sizes, and discounts are subject to change by Materialize. - For decisions that depend on exact pricing, discounts, or capacity, customers should rely on the current public pricing page or speak directly with the Materialize team. Source: https://materialize.com ## Hours and contact General contact - You can reach Materialize through the contact form on the website at https://materialize.com/contact/. - A general contact email published on the site terms is info@materialize.com. These channels are suitable for general questions, partnership inquiries, and other non-support requests. Sales and product inquiries - Prospective customers can request to book a demo or talk to the team directly from the main site using "Book a demo" or similar calls to action. - The demo request flow is designed to connect you with Materialize experts (often from the Field Engineering or product team) to discuss your use case and walk through the platform. Scheduling a live demo Materialize provides a dedicated demo page where you can schedule a one-on-one session: - Visit the demo scheduling page (linked from the main navigation or "Schedule a Demo" buttons). - Provide basic information about your company, role, and intended use cases. - A member of the Materialize team follows up to confirm details and walk you through the product. Getting started without a demo If you prefer to explore first before talking to the team, you can: - Start a free cloud trial of Materialize from the main site. - Download the Materialize Emulator to run locally for development and testing. - Use the documentation, guides, and quick-start materials linked from the site to get hands-on. For any questions the agent cannot answer from the knowledge documents, users should be directed to contact Materialize via the contact form, demo request, or info@materialize.com. Source: https://materialize.com ## AI-native search and vector pipelines with Materialize Overview Materialize is used to build interactive vector search and AI-native search pipelines that stay continuously up to date. By expressing sources, transformations, and outputs in SQL, teams can keep embeddings and search attributes fresh on top of constantly changing operational data. Why keeping search indexes fresh is hard Traditional approaches create challenges: - OLTP databases – Operational databases are siloed, and assembling a flat, denormalized search document often requires heavy joins both when writing into the index and when rehydrating results. - Data warehouses – Warehouses can integrate and denormalize data but typically run in periodic batches, often minutes or hours behind reality, which is not sufficient for interactive agents or applications. - Custom stream-processing pipelines – Do-it-yourself pipelines with tools like Flink can keep things fresh but usually require specialized talent and are complex and costly to maintain. How Materialize helps Materialize maintains live SQL views over your operational data and keeps them incrementally up to date as upstream changes occur. These views define the exact shape your search documents or vectors should have. - You connect operational sources (for example, Postgres, MySQL, SQL Server, Kafka). - You write SQL views that aggregate, join, and flatten data into the document structure or embedding inputs you need. - Materialize incrementally updates only the affected rows when source data changes and can push precise deltas to vector databases or search indexes such as Elastic, OpenSearch, or Turbopuffer. This allows you to: - Keep embeddings and attributes up to date within hundreds of milliseconds. - Re-embed or update only what changed instead of recomputing entire indexes on a batch schedule. - Support tight agentic loops where agents write to upstream systems and then immediately see the effect of those writes in their search and context. Enriched context and reranking Because Materialize supports complex joins, aggregations, and even recursive queries in SQL, you can enrich search context with additional attributes from multiple systems. Agents, re-rankers, and RAG pipelines can: - Query live views directly over a standard Postgres connection when they need the full record. - Use richer, up-to-date attributes to improve ranking and relevance. Key capabilities for AI-native search - Continuously updated SQL views that define embeddings and attributes. - Sources for streaming operational data (such as databases over CDC, Kafka, and webhooks). - Sinks to publish precise deltas to vector databases, search engines, and downstream systems. - Strong consistency guarantees suitable for interactive applications. These capabilities make Materialize a fit for AI-native search, interactive vector pipelines, and RAG systems that must stay closely aligned with the latest operational state. Source: https://materialize.com ## Deployment options for Materialize Materialize can run in the cloud as a fully managed service, in your own environment, or locally for development. Cloud (fully managed SaaS) Materialize Cloud is the recommended way to run Materialize in production for most teams. Cloud On-Demand - Fully managed SaaS offering for getting started quickly. - Pay-as-you-go pricing based on compute, storage, and networking usage. - Runs on AWS, with regions including us-east-1, us-west-2, and eu-west-1. - Designed for interactive workloads, with production end-to-end latency typically at or below about one second in supported regions. - Automated, no-downtime upgrades. - Auto-scaling, workload isolation, and high availability features. - Security features include role-based access control (RBAC) and always-on encryption. - Observability via the Materialize Console and integrations such as Prometheus SQL Exporter and monitoring templates for Datadog and Grafana. Cloud Capacity - Fully managed SaaS solution for teams needing predictable costs and higher-touch support. - Annual, prepaid plans with volume discounts off list pricing. - Includes a dedicated account team and guided onboarding and setup. - Designed for production workloads at scale, with the same underlying capabilities as Cloud On-Demand. Self-managed (Enterprise license) - Materialize can be deployed in your own public or private cloud or other environments. - Offered via an annual Enterprise license designed for large-scale, production deployments that require priority support. - Provides flexibility to keep data and infrastructure fully under your control while still using Materialize as the live context layer. Local development (Materialize Emulator and Community license) - A Materialize Emulator is available as an all-in-one Docker image for local development and functional testing. - A Self-Managed Community License is available free of charge within documented usage limits (for example, up to 24 GiB memory and 48 GiB disk at the time of writing). - The emulator and community options are intended for experimentation, development, and smaller-scale use cases. For production workloads, teams typically use Materialize Cloud or a licensed self-managed deployment. Source: https://materialize.com ## What Materialize has not published yet These are things people ask Materialize that its published information does not yet cover. - specific prices or discounts not taken from the current Mate - real-time availability of plans, regions, or cluster sizes - performance guarantees such as latency, throughput, or capac - uptime or service level agreement terms - contract terms, legal commitments, or indemnities - security certifications or compliance standards not listed o Ask anyway — the agent will say plainly that it is not published rather than guess. ## Where this comes from https://materialize.com