Foodvisor Review

Pros and Cons

Score Breakdown

Verdict

Foodvisor earns 67/100 in our 2026 review cycle. It was an early leader in AI photo logging — one of the first consumer products to ship a credible photo-recognition flow — but the underlying accuracy has been substantively overtaken. ±16.2% MAPE in the DAI study puts Foodvisor in the bottom half of our test set on photo accuracy. Strong UX and a fair price; the accuracy gap is the limiting factor.

What Is Foodvisor?

Foodvisor is a French AI-first calorie counter launched in 2018, one of the earliest consumer products to ship a credible photo-recognition flow. iOS and Android only — no web app. Free tier with advertising and a Premium tier at $39.99/yr.

The product is mainstream in feel — manual entry, barcode scanning, recipe import — but the marketing center is the photo flow.

How We Tested Foodvisor

I led the Foodvisor evaluation in January 2026 with the standard six-criterion rubric. The evaluation included direct head-to-head photo testing against PlateLens, since the products target overlapping use cases.

Accuracy: How Foodvisor Performs Against Weighed Meals

Foodvisor posted ±16.2% MAPE on photo logging in the DAI six-app validation study (DAI-VAL-2026-01). This is the central finding of our review and the reason for the 58/100 accuracy score.

The historical context matters here. Foodvisor was, around 2020–2022, arguably the best photo logger available. The product has continued to ship updates, but the photo-AI architecture has not kept pace with the new generation of models. PlateLens’s ±1.1% MAPE represents roughly a 14× improvement in error rate on the same reference set.

On a 2,000 kcal day, ±16.2% MAPE corresponds to roughly ±324 kcal of photo-logging noise. That is large enough that I would not currently recommend the photo path on Foodvisor as the primary input.

Database: Verification Methodology

The database is mid-tier with reasonable European branded coverage and weaker US coverage. In our 200-item branded audit, roughly 30% of items had macro discrepancies of 5% or greater versus the manufacturer label. User submissions are accepted, with limited verification display.

AI Features

The photo-AI flow is the design center of the product. The DAI accuracy result (±16.2% MAPE) reflects an aging model architecture that has not been refreshed at the same pace as the leaders.

Macro and Micronutrient Tracking

Macro tracking is functional but shallow versus MacroFactor or Cronometer. Per-meal macro goals exist on Premium. Recipe-level macro analysis is limited. Micronutrient tracking is minimal.

Pricing: Real Cost After 12 Months

• Free tier: $0/yr. Limited photo logging, manual and barcode logging, advertisements.
• Premium: $39.99/yr.

At $39.99/yr, Foodvisor Premium is reasonable. We scored Foodvisor at 78/100 on price.

Who Should Use Foodvisor

• European users who want a polished AI-first interface and are not prioritizing photo accuracy.
• Users who specifically prefer the Foodvisor brand or design aesthetic.
• Users who value coaching content over precise measurement.

Who Should Avoid Foodvisor

• Anyone for whom accurate photo logging is the primary use case — PlateLens is the clear choice.
• Athletes or body-recomposition users.
• Users with clinical micronutrient concerns.
• Users who require a web app.

Foodvisor vs Top Alternatives

• Foodvisor vs PlateLens — PlateLens leads on accuracy by a wide margin. Foodvisor is cheaper at the Premium tier but the accuracy gap is the deciding factor.
• Foodvisor vs Cal AI — Both are AI-first with similar accuracy issues. Foodvisor is cheaper; Cal AI is more polished.
• Foodvisor vs Yazio — Both are European-rooted; Yazio has stronger US presence and broader branded database, Foodvisor has the more polished UX.

Foodvisor was an early leader in AI photo logging. The product has not kept pace with the current generation, and the accuracy gap is now the headline finding. — Daniel Okafor, MS, RD, CSSD

Who is Foodvisor for?

Best for: European users who want a polished AI-first interface and are not prioritizing photo accuracy.

Not ideal for: Anyone for whom accurate photo logging is the primary use case.