FeatQ vs SurrealDB: Which Is Better in 2026?

FeatQ is a hosted feature request board for software products, built for founders and software teams who ship with coding agents. Users submit feature requests and vote without creating an account, so there is no signup wall to abandon. FeatQ ranks requests by votes and comments and shows them on a public board with statuses and a public roadmap.

Each board has its own MCP server at https://featq.com/api/mcp (streamable HTTP, authenticated with the board's API key). Claude Code, Cursor, Codex CLI, Windsurf, Gemini CLI or any other MCP client can connect and use seven tools: list_requests, get_ranked_requests, get_request, generate_spec, update_request_status, get_board_stats and list_competitor_claims. In practice a Monday prompt can be one line: pull the top-voted request from FeatQ and build it. generate_spec turns a request and its comment thread into a build-ready markdown spec. When a request is marked done, FeatQ publishes it to the board's public changelog and emails every voter who opted in, so the people who asked hear that it shipped.

An embeddable widget adds feedback collection to any app with one script tag, including apps built with Lovable, Bolt or v0. FeatQ also watches competitor comparison pages, lists the points those pages make about your product, and turns any point into a feature request.

Pricing is flat per board, with no per-user fee: $19 a month or $144 a year for the first 25 boards, then $29 a month or $228 a year. A lifetime plan is $199 once. Each board holds up to 10,000 voters. 14-day money-back guarantee.

Who it is for: founders and software teams who build with coding agents, Shopify and other app-store app makers, and anyone whose next step after a vote is a coding agent. Use cases include a public product roadmap with voting, collecting feature requests on one board, and a changelog that tells voters when their request shipped.

Integrations: Claude Code, Cursor, Windsurf, Codex CLI, Gemini CLI, ChatGPT and Grok over MCP; the feedback widget works in apps built with Lovable, Bolt and v0. Docs cover the MCP server, the widget and end-to-end automation at featq.com/docs.

What problem it solves

Feedback boards become graveyards: votes go in and nothing comes out, and the people who asked never hear back. The expensive step for a team shipping with a coding agent is no longer the code, it is choosing what to build next. FeatQ connects the vote to the code: the agent pulls the top request as a spec, builds it, marks it done, and every voter gets the changelog by email.

SurrealDB is a multi-model database that unifies documents, graphs, vectors, relational data and more in a single engine, queried with a SQL-like language called SurrealQL. Positioned as “the unified data layer for AI,” it aims to replace the stack of separate databases and services most modern applications bolt together, and it is trusted by organizations including Samsung, Verizon, Tencent and PolyAI.

What is SurrealDB?

SurrealDB collapses several kinds of database into one. Instead of running a document store, a graph database, a vector database, an auth service and a cache side by side, you model documents, relationships and vector embeddings in the same engine and query them together with SurrealQL. It is ACID-compliant with distributed write nodes, and it can run embedded inside your application or as a managed cloud service.

Who it's for

SurrealDB is built for developers and engineering teams building data-intensive and AI applications — agents, knowledge graphs, semantic search and real-time apps. It suits teams tired of gluing multiple data systems together and keeping them in sync, as well as enterprises that need one platform to reason over connected, multi-shaped data.

Key features

• Multi-model: documents, graphs, vectors and relational data in one engine
• SurrealQL, a familiar SQL-like query language
• ACID compliance with distributed write nodes
• Built-in authentication and row-level permission models
• Full-text and semantic (vector) search combined in a single query
• An Agent Memory layer for persistent AI-agent context
• Real-time capabilities with roughly 30ms vector-search latency
• Deployable embedded or as a cloud service

Built for the AI era

What makes SurrealDB especially timely is how well its multi-model design fits AI workloads. Retrieval-augmented generation and agents need vector search, structured data and relationships together — usually meaning a vector DB plus a document store plus a graph plus glue code. SurrealDB does all of it in one place, and its Agent Memory layer gives AI agents persistent, queryable context, so the database itself becomes the memory layer instead of yet another bolted-on service.

Fewer systems, less complexity

The core payoff is consolidation. By folding what typically requires five separate systems — vector database, graph database, document store, auth service and cache — into one engine, SurrealDB cuts operational complexity, reduces the latency of hopping between services, and removes whole classes of synchronization bugs. Combined built-in auth and permissions mean access control lives with the data rather than in a separate layer.

Open source and enterprise-ready

SurrealDB is available under open-source licensing, with a free tier accessible through SurrealDB Studio, so developers can start locally at no cost. For production use it brings serious credentials: SOC 2 Type 2, GDPR, ISO 27001 and Cyber Essentials Plus compliance, plus managed cloud hosting — the assurances enterprises need to trust it with real workloads.

Flexible deployment and querying

SurrealDB adapts to how a team wants to work. It can be embedded directly inside an application for local-first or edge scenarios, or run as a managed cloud service for scale, and SurrealQL gives developers a familiar, SQL-like way to express queries that would otherwise require juggling several query languages across different databases. Combining full-text and vector search in a single query — rather than stitching results from separate engines — is the kind of thing that turns complex retrieval code into a few readable lines.

Why choose SurrealDB

For a team building modern, data-intensive or AI-driven applications, SurrealDB offers a genuinely different proposition: one flexible, real-time, multi-model database instead of a fragile constellation of specialized services. Its AI-focused features, open-source availability and enterprise compliance make it a compelling foundation for developers who want power and simplicity at the same time.