FluentDB vs Keel — An AI assistant whose memory belongs to you.: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of FluentDB and Keel — An AI assistant whose memory belongs to you. — 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.
Keel — An AI assistant whose memory belongs to you.
Keel Labs
Local-first desktop assistant for Mac/Windows that stores plain Markdown on your disk and lets you swap models while keeping your context.
Key features
- Local-First Storage: Stores all assistant memory and workspace data as plain Markdown files on the user's disk to ensure portability, offline access, and easy backup/versioning with Git.
- Model-Agnostic Integration: Supports swapping between model providers (e.g., Claude, GPT, OpenRouter, Ollama) allowing users to change inference backends without losing context or notes.
- Plain Markdown Workspace: Uses human-readable Markdown as the native data format, enabling easy editing, searching, and integration with existing text-based workflows and tools.
- Cross-Platform Desktop App: Provides a native desktop experience for both macOS and Windows users optimized for local performance and file-system based storage.
- Privacy-First Design: Keeps context and memory under user control by defaulting to local storage and enabling use of local or self-hosted model endpoints.
- Bring-Your-Own-Model (BYOM): Allows connecting to local or third-party model runtimes (e.g., Ollama or OpenRouter) so users can run models they trust or prefer.
- Open Source Repository: Project source and releases are available on GitHub, facilitating community contributions, audits, and self-hosted deployments.
- Local-first desktop application for macOS and Windows
- Stores workspace and memory as plain Markdown files on the user's disk
- Bring-your-own-model: support for swapping Claude, GPT, OpenRouter, Ollama
- Context persistence on local filesystem independent of model backend
- Open-source codebase hosted on GitHub (Keel-Labs/keel)
- Plain-markdown workspace for notes, context capture, and memory organization
Best for
- Personal Knowledge Base: Maintain an evolving, searchable personal assistant memory as plain Markdown files that you control and can version with Git.
- Privacy-Sensitive Assistance: Use local or self-hosted model runtimes to run queries and keep sensitive context on-device rather than sending it to hosted services.
- Experimenting with Models: Quickly switch between model providers (Claude, GPT, OpenRouter, Ollama) to compare outputs while keeping the same conversation context and notes.
- Developer Workflows: Keep notes, prompts, and agent memory in Markdown within a repository to integrate with codebases, CI, and version history.
- Offline or Local Inference: Connect to locally running model servers (e.g., via Ollama) to generate responses without relying on external APIs.
- Content Drafting and Iteration: Draft, refine, and store content in Markdown while swapping models to explore different creative or editorial styles.
- Personal knowledge base with locally stored assistant memory
- Using different LLM providers interchangeably while keeping the same local context
- Privacy-focused workflows where sensitive context stays on the user's disk
- Developer experimentation with multiple model backends and routing
- Note-taking and organization using Markdown-backed agent memory