FluentDB vs Freu: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of FluentDB and Freu — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
FluentDB
FluentDB
Native macOS database client with an AI co-pilot for PostgreSQL, MySQL, SQLite, and SQL Server — bring your own model.
Key features
- AI Co-pilot with Guardrails: Ask questions in plain English and get trusted SQL, with safety checks that prevent destructive operations and data leakage.
- Bring Your Own Model: Point FluentDB at Anthropic (Claude Code), OpenAI (Codex), or a local Ollama model — prompts go direct to your provider, never through FluentDB.
- Schema-Aware SQL Editor: Full 2026-era editor with autocomplete, formatting, and instant results, and a one-click switch into AI mode.
- Fluid 100K+ Row Grid: A fast data table that scrolls thousands of rows smoothly without stutter, built for large datasets.
- Instant Chart Visualization: Turn any query result into a chart without leaving the app.
- MCP Integration: Connect any MCP-compatible AI agent to manage FluentDB connections on your behalf.
- Multi-Database Support: Connect to PostgreSQL, MySQL, SQLite, and SQL Server today, with MongoDB, Redis, ClickHouse, Snowflake, BigQuery, and DuckDB in the pipeline.
- Command Palette Browsing: Hit ⌘P to search and open any table or view in a snap.
Best for
- Ad-hoc Analytics on Production Databases: Ask FluentDB in plain English to summarize a table, then review and run the generated SQL against Postgres or MySQL.
- Safe Data Exploration: Junior engineers explore live databases without fear thanks to AI guardrails that block destructive statements.
- Local-Only Querying: Analysts working with sensitive data run queries against SQLite/SQL Server using a local Ollama model so nothing leaves the machine.
- Team License Management: A small team buys reassignable seats and shares one activation pool across multiple Macs.
- Agent-Driven Database Ops: Route an MCP-compatible coding agent through FluentDB to open connections and run queries autonomously.
Freu
Freu AI (freu-ai)
Ahead-of-time web automation that records browser sessions and compiles them into reusable deterministic skill commands to reduce agent token use.
Key features
- Ahead-of-Time Compilation: Records a browser session once (via Chrome extension and CDP) and compiles it into a reusable JSON-based DSL skill that agents can execute deterministically.
- Token Usage Reduction: Offloads repeated visual and reasoning steps to compiled programs, reducing LLM/agent token consumption (repo claims up to ~90% savings) and lowering recurring inference costs.
- Chrome Extension + CDP Runner: Captures user interaction and driving Chrome DevTools Protocol commands for precise, reproducible playback and capture of complex UI flows.
- Skill DSL & Artifacts: Emits human- and agent-readable artifacts (SKILL.md and <Cmd>.json steps) that document the workflow, provide structured muscle memory, and enable auditing and reuse.
- Local HTTP Bridge: Runs a Python HTTP service (default 127.0.0.1:8787) to serve skills to agents and orchestrate learn/run cycles programmatically.
- Deterministic Execution: Converts volatile DOM parsing and visual reasoning into stable, deterministic commands so agents can skip expensive visual interpretation.
- Extensibility Toward Desktop: Roadmap includes an OS-level Computer Use Agent (CUA) and vision-based desktop automation to extend AOT pipeline beyond browsers.
- Record browser sessions via a Chrome extension and CDP command runner
- Compile recorded sessions into reusable, deterministic JSON skill files (DSL)
- Local Python HTTP bridge (freu-cli) that communicates with the Chrome extension (default 127.0.0.1:8787)
- Outputs SKILL.md and <Cmd>.json structured steps suitable for agent consumption
- Reduces LLM token usage by delegating repeated deterministic actions to compiled skills
- Logging and intermediate artifact generation during learn/run workflows
- CLI-based workflow: learn (capture + LLM) and run (execute compiled skill)
- Planned extension to OS-level vision-based desktop automation (Computer Use Agent)
Best for
- Automating repetitive web workflows (form submission, navigation, multi-step interactions) by recording once and replaying as a compiled skill.
- Reducing LLM/agent operating costs by converting expensive, repeated web reasoning into deterministic skills loaded into the agent context.
- Building stable enterprise automation where auditability and reproducibility are required—SKILL.md and JSON steps provide documented, inspectable workflows.
- Integrating precompiled skills into agent pipelines via the local HTTP bridge to coordinate when and how skills are executed programmatically.
- Creating reusable automation libraries across teams: capture a complex sales/CRM flow once and share the compiled skill for consistent execution.
- Translating human-performed browser sessions into structured automation artifacts for testing, monitoring, and regression checks.
- Stabilize and accelerate complex enterprise web workflows by precompiling repetitive tasks
- Reduce costs for LLM-enabled agents by offloading deterministic UI interactions to compiled skills
- Build reusable skill libraries for agent-driven automation and RPA-like tasks
- Automated end-to-end test recording and deterministic playback for web applications
- Integrate precompiled web skills into agent context windows to avoid DOM re-parsing