Alpaca vs Kavout: Which Is Better in 2026?

Figures and features are point-in-time and change; treat the table as a starting map, not a live quote.

A developer-first brokerage with an official MCP server for real-time data, paper trading, and execution by AI agents. Best for people who code.

How it works: Alpaca is a brokerage you talk to through code: a REST and websocket API exposes market data, paper trading, and live commission-free execution, and its official MCP server lets an AI agent pull data, test in a paper account, and place real orders programmatically. You build (or wire up an agent against) that API rather than tapping buttons in an app.

In practice, Alpaca’s AI official mcp server (data + execution). It falls under for builders: connect a broker to an ai agent, which makes it best suited to developers building ai agents. On connecting an account it is “Alpaca accounts”, and on execution it is “Read + trade + paper”. It is priced as free (open source).

One honest limitation: Developer-first; requires code and self-hosting.

AI Kai Score ratings and quant signals with an institutional lean. Best for data-oriented investors.

How it works: Kavout applies machine learning across fundamentals, technicals, and alternative data to generate the Kai Score, a numerical equity rating, alongside other quant factor signals. You use the ratings and signals as research inputs to build or screen a watchlist, then trade at your own broker.

In practice, Kavout’s AI ai kai score ratings. It falls under ai stock research and scoring, which makes it best suited to data-oriented quant signals. On connecting an account it is “No”, and on execution it is “None”. It is priced as subscription.

One honest limitation: Steeper learning curve, less beginner-friendly.

The core difference is category. Alpaca focuses on developers building ai agents (official mcp server (data + execution)), and Kavout on data-oriented quant signals (ai kai score ratings). On broker connection they differ too: Alpaca is “Alpaca accounts” versus Kavout at “No”. That shapes everything downstream: how personal the answers are, where trades settle, and how much control you keep over individual positions.

Every tool gives something up for what it does well. Here is the honest give-and-take on each, so you can weigh the specific strengths against the limitations that come with them rather than judging on the headline category alone.

For AI investing tools, the distinction that matters most is whether the tool works from your actual, connected positions or reasons from something else: a separate account it manages for you, or the tickers and numbers you feed it. It decides how personal the answers can be, and where your money physically lives.

• Alpaca: connects specific supported accounts. Alpaca connects a defined set of accounts (Alpaca accounts), so whether it can see your holdings depends on whether your money is at one of them.
• Kavout: does not see your holdings. Kavout works from market data and the tickers you research, not your live positions. You read its output, then act in whichever broker you keep your money at.

This is where they diverge most. One works from your real, connected account while the other does not, so if you want an assistant grounded in the exact positions you already hold, that gap is the thing to weigh first. This holdings-aware angle is the one Walnut is built around: it connects the brokerage you already use and reasons from your live positions, read-only by default, with any trades left for you to approve.

There is no universal winner here; the right pick depends on the job you are hiring the tool for. Match the category to your intent rather than chasing a single “best.”

• Choose Alpaca if you want developers building ai agents. Its AI official mcp server (data + execution), it is priced as free (open source), and it fits for builders: connect a broker to an ai agent. It is built for developers building their own automated or AI-driven trading systems. Keep in mind that developer-first; requires code and self-hosting.
• Choose Kavout if you want data-oriented quant signals. Its AI ai kai score ratings, it is priced as subscription, and it fits ai stock research and scoring. It is built for data-oriented investors who are comfortable interpreting quant signals themselves. Keep in mind that steeper learning curve, less beginner-friendly.

Because they sit in different categories, this is not strictly either-or: some investors use one for developers building ai agents and the other for data-oriented quant signals, and just watch for overlapping costs.

Cost is easy to misread when two tools charge in different shapes, so compare the model, not just the number. Alpaca is priced as free (open source), while Kavout is priced as subscription. A percentage-of-assets fee scales with your balance, a flat subscription does not, and a “free” tier usually earns elsewhere (on cash, order flow, or premium upgrades), so the cheapest headline is not always the cheapest outcome for your situation.

Pricing and tiers change often. Confirm the current numbers on each provider’s own site before you decide; the framing above is point-in-time.

If neither quite fits, Walnut sits in a third category: chat-driven management of your own brokerage. It connects the brokerage you already use through SnapTrade, lets you analyze and manage it by talking through Claude or ChatGPT, build thematic portfolios around a thesis, and place trades you approve. Read-only by default. See Walnut vs Alpaca and Walnut vs Kavout. Walnut is not an investment adviser.