Kavout vs Ziggma: 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.

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.

A portfolio analytics platform with proprietary stock scores, portfolio simulation and dividend tracking for self-directed investors.

How it works: You connect or enter your holdings and Ziggma analyses the portfolio for exposure, risk and dividend income, scoring individual stocks on fundamental factors. A simulator lets you test how adding or removing a position would change the whole portfolio before you do it, which is the feature most portfolio trackers lack.

In practice, Ziggma’s AI scores stocks and analyses portfolio construction. It falls under ai stock research and scoring, which makes it best suited to portfolio analytics and stock scoring for self-directed investors. On connecting an account it is “Yes, for tracking”, and on execution it is “No”. It is priced as free tier plus a paid tier (verify current).

One honest limitation: Analysis and simulation only; it cannot place an order, so acting on anything means going back to your broker.

The core difference is category. Kavout focuses on data-oriented quant signals (ai kai score ratings), and Ziggma on portfolio analytics and stock scoring for self-directed investors (scores stocks and analyses portfolio construction). On broker connection they differ too: Kavout is “No” versus Ziggma at “Yes, for tracking”. 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.

• 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.
• Ziggma: reads your real connected holdings. Ziggma connects your real brokerage (Yes, for tracking) and works from your actual positions, so its answers reflect what you genuinely own rather than a generic model.

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 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.
• Choose Ziggma if you want portfolio analytics and stock scoring for self-directed investors. Its AI scores stocks and analyses portfolio construction, it is priced as free tier plus a paid tier (verify current), and it fits ai stock research and scoring. It is built for a self-directed long-term investor who wants to understand portfolio-level effects before adding a position. Keep in mind that analysis and simulation only; it cannot place an order, so acting on anything means going back to your broker.

Because both sit in the same category, the choice comes down to the finer details above rather than a fundamental difference in approach.

Cost is easy to misread when two tools charge in different shapes, so compare the model, not just the number. Kavout is priced as subscription, while Ziggma is priced as free tier plus a paid tier (verify current). 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 Kavout and Walnut vs Ziggma. Walnut is not an investment adviser.