Yazio's Database Is Full of Wrong Entries — Here's Why and What to Use Instead

Yazio's community-submitted entries are the source of most calorie mismatches. Here's how to spot them — and what to use instead.

If you have used Yazio for more than a few weeks, you have almost certainly logged a food where the numbers looked wrong. A grilled chicken breast with 420 calories. A plain apple with 22g of protein. A slice of bread that somehow contains more fat than butter. You were not imagining it. The entry was wrong, and the reason is structural: a huge portion of Yazio's food database is user-submitted, and user submissions are not reviewed by nutritionists before they appear in search results.

This post is blunt because the problem is not a small one. If your calorie tracker is giving you bad numbers, you are not tracking — you are guessing with extra steps. Below is a practical breakdown of why Yazio entries go wrong, what the common patterns look like, how to catch bad entries before they pollute your log, and which verified-database apps avoid the problem at the source.

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Why Does Yazio Have So Many Wrong Entries?

Yazio, like MyFitnessPal and FatSecret, leans heavily on community submissions to fill out its food database. The appeal for the company is obvious. Community submissions scale cheaply. Users upload foods from their own pantries, their local supermarkets, and their favorite restaurants, which expands the database far faster than any in-house nutritionist team could. For regional coverage in dozens of countries, it is a practical way to reach scale.

The problem is what happens after a user submits an entry. In a community-submitted database, the entry typically becomes searchable with minimal review — sometimes with none at all. There is no nutritionist validating the macro ratios. There is no dietitian cross-checking the numbers against established food composition databases. There is no automated sanity check flagging a pasta sauce with 80g of protein per 100g. The entry sits in the database, surfaced alongside legitimate ones, and every user who finds it before anyone reports it gets the wrong numbers logged into their day.

This approach also scales the wrong way. As the database grows, the ratio of verified to unverified entries gets worse, not better. Popular foods eventually accumulate enough submissions that one of them is usually right, but less common items — regional brands, home recipes, restaurant dishes, store-brand items — often have only one or two entries, and there is no signal showing you which one, if any, was entered correctly.

Where do the wrong numbers actually come from?

There are a few repeating sources of error:

• Unit confusion. A user enters a food as "per 100g" but types the "per serving" numbers, or vice versa. A 30g protein bar gets logged as 30g of protein per 100g.
• Cooked versus raw confusion. A user weighs a cooked chicken breast and enters it as raw. Another user finds the entry and logs their raw chicken against cooked numbers, ending up with roughly 25% less food than they think.
• Brand and flavor mix-ups. The correct brand name with the wrong flavor's macros. A protein yogurt's numbers assigned to a full-fat yogurt label.
• Typos that nobody catches. 42g of protein instead of 4.2g. 520 calories instead of 52. A decimal in the wrong place.
• Old packaging. A user submits numbers from a package reformulated two years ago. The manufacturer has since changed the recipe, but the old entry stays searchable forever.
• Restaurant guesses. "Chicken sandwich — Local Cafe" with numbers a user estimated, not numbers the restaurant published. These are pure guesses dressed up as data.

None of these require malice. They are honest mistakes. But in a community database with no nutritionist gatekeeping, honest mistakes are published and served to millions of other users as if they were facts.

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Real Examples of Wrong Entry Patterns

If you search Yazio long enough, the same families of error show up again and again. Recognizing the patterns is the first defense.

The "everything is protein" entry

Look for entries where the protein value is wildly inconsistent with the food. A croissant with 18g of protein. A rice cake with 12g. A banana with 9g. These are usually the result of a user logging a protein-forward product (protein bread, protein rice, protein cereal) but saving it under the plain food's name. The next user who searches for the plain food gets the fortified numbers.

The "cooked weight, raw label" entry

Chicken, beef, salmon, rice, pasta, and oats all shrink or expand with cooking. A common error is an entry labeled "raw" that was measured cooked, or labeled "cooked" with raw-weight numbers. If you weigh your food raw and the log is using cooked numbers, you are under-counting by roughly 20–30% on proteins and over-counting on grains. This compounds meal after meal.

The "decimal in the wrong place" entry

Look at any entry with suspiciously round, large protein or fat values — 40, 50, 60 grams where the food does not plausibly contain that. Many of these are shifted decimals. The correct value is 4, 5, or 6 grams.

The "generic restaurant meal" entry

A restaurant name with a dish, submitted by a user who almost certainly did not have lab analysis performed. These entries are not data. They are a stranger's guess about what a stranger's kitchen served them. For popular chains, the first-party nutrition data from the restaurant itself is usually more trustworthy — but finding that entry among the dozens of user-submitted copies requires active sorting.

The "brand-new product" entry

Newly launched products often have one entry in the database, submitted within a week of the product's release, and nobody has corrected it yet. For the first few months after launch, that single entry is the only source, right or wrong.

The "wrong serving size" entry

An entry that gives correct nutrition per 100g but sets the default serving to 30g when the real serving is 55g, or vice versa. Users log "one serving" without checking and end up with roughly half or double the actual intake.

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How to Tell If a Yazio Entry Is Wrong

You cannot audit every entry you log, but you can catch the worst offenders with a few quick sanity checks.

Compare macros against the calorie total

Every 1g of carbohydrate and protein is about 4 calories. Every 1g of fat is about 9 calories. Add protein grams times 4, carb grams times 4, and fat grams times 9. If the sum does not roughly match the listed calorie count (within 10% or so), one of the numbers is wrong. A grilled chicken breast showing 200 calories but only 2g of protein is flagged instantly by this check.

Compare against a known reference

If you are logging a generic food — plain chicken breast, plain rice, plain broccoli — the correct numbers barely change between databases. Cross-reference against USDA's FoodData Central, a manufacturer's own label, or a verified-database app like Cronometer or Nutrola. If the Yazio entry disagrees with both, it is the outlier.

Prefer entries with a verified source

Some Yazio entries come from official brand data or partnered sources. When multiple entries exist for the same food, prefer the one with a clear brand match, a clean serving size, and numbers that look plausible. Skip the entries submitted by unfamiliar users with unusual numbers.

Watch for "too easy" macros

Macros that land on round numbers — exactly 20g protein, exactly 10g fat, exactly 30g carbs — are often estimates a user typed in, not measured values. Real food data almost always has decimals.

Reconcile against the package

For anything with a label, trust the label. Open the product, read the back, and if the Yazio entry does not match, create your own custom food. The 30 seconds it takes to enter a custom food is cheaper than a week of bad data.

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How Verified-Database Apps Avoid This

The alternative to a community-submitted database is a verified one — where every entry is reviewed by nutrition professionals, sourced from regulatory food composition databases, or pulled directly from manufacturer-provided data before it becomes searchable.

Two apps take this approach seriously in 2026: Cronometer and Nutrola.

Cronometer

Cronometer built its reputation on nutritional accuracy. Its core database draws from USDA's FoodData Central and the Canadian Nutrient File (NCCDB), with manufacturer data added for branded items. The free tier can feel restrictive — daily log limits and no barcode scanner — but the data itself is trustworthy. For users who need precise numbers for medical or athletic reasons, Cronometer has been a default recommendation for years. Its interface is dense and spreadsheet-like, which some users love and others find overwhelming, but the numbers are defensible.

Nutrola

Nutrola's database is built around a verified-first approach. Every entry is reviewed by nutrition professionals before it becomes searchable, and sources include regulatory food composition databases, manufacturer-provided label data, and vetted international food references. The result is a 1.8 million+ entry database where the numbers are designed to be trustworthy at the time you log them — not after community correction cycles catch the mistakes weeks or months later.

Nutrola combines the verified-database approach with modern logging tools that community-submitted apps typically own (AI photo recognition, voice logging, barcode scanning, recipe import), plus full 100+ nutrient tracking, 14 language support, and pricing that starts at €2.50 per month with a free tier available. Zero ads on any tier.

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How Nutrola's Database Is Different

• Nutritionist-verified entries. Every food reviewed by nutrition professionals before it becomes searchable. No raw community dumps.
• Regulatory sources prioritized. Entries pulled from established food composition databases (USDA FDC, EFSA, and international equivalents) and manufacturer-provided label data.
• 1.8 million+ verified foods. Breadth that is usually only found in community-submitted databases, without the data-quality tradeoff.
• 100+ nutrients per entry. Calories, protein, carbs, fat, fiber, sodium, potassium, iron, calcium, magnesium, vitamins A through K, and more. Not just the basic four.
• AI photo logging in under 3 seconds. Point the camera, get a verified match from the reviewed database — not a random community estimate.
• Voice logging. Natural-language entry that routes to verified entries, not unverified ones.
• Barcode scanning against verified entries. When you scan a product, the match is from reviewed data — not a stranger's guess on the same UPC.
• Recipe URL import with verified breakdowns. Paste a recipe link, get ingredient-by-ingredient verified nutrition.
• Consistent macros that reconcile. Every entry's protein, carbs, and fat multiply out to match its listed calories — because verified data is internally consistent.
• 14 languages. Full localization for international users without fragmenting into separate regional databases of uneven quality.
• Zero ads on every tier. The free tier is not a funnel for advertisers — it is a usable product.
• €2.50/month paid tier with a free tier available. Verified data should not cost what premium ad-heavy community apps charge.

Yazio vs. Nutrola at a glance

Feature	Yazio	Nutrola
Database source	Community-submitted + partial brand data	Nutritionist-reviewed + regulatory sources
Entry review before publishing	Minimal	Reviewed by nutrition professionals
Database size	Large	1.8 million+ verified
Nutrients tracked	Basic macros, some micros	100+ nutrients
AI photo logging	Limited	Under 3 seconds, verified match
Voice logging	No	Yes
Barcode scanning	Yes, mixed-quality hits	Yes, verified hits
Recipe URL import	Limited	Yes, verified breakdown
Languages	Multiple	14
Ads	Present on free tier	None on any tier
Pricing	Free tier + paid plans	Free tier + €2.50/month

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Should You Keep Using Yazio?

To be fair: Yazio does some things well. The onboarding flow is friendly. The interface is pleasant. The recipe feature is one of the better-looking ones on the market. Intermittent fasting tracking is well integrated. For users whose priority is a calm, simple habit-building experience, Yazio is not a bad choice.

The database problem is also not unique to Yazio. MyFitnessPal has the same structural issue at an even larger scale. FatSecret relies on community data too. Any calorie tracker that grew big on user-submitted entries carries the same risk. If you are ready to accept that risk in exchange for the other things Yazio offers, you can keep using it responsibly by:

• Always cross-checking unfamiliar entries against a verified source.
• Creating your own custom foods for anything you eat regularly, using the package label or the manufacturer's own website.
• Ignoring restaurant entries submitted by users. Use the restaurant's own published nutrition whenever possible.
• Sanity-checking your daily macro totals against your expected totals. If the numbers drift, a bad entry is usually the reason.

But if you are tracking for reasons that punish inaccuracy — medical nutrition, athletic performance, a specific macro target, a weight goal that stalls for unclear reasons — a verified-database app is not a nice-to-have. It is the difference between tracking and guessing. Cronometer is a solid choice. Nutrola is a solid choice. Either one removes the community-submission problem at the source.

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Frequently Asked Questions

Why does Yazio have so many wrong entries?

Yazio relies heavily on community submissions to populate its food database. User-submitted entries are not reviewed by nutritionists before they appear in search results, so typos, unit mix-ups, cooked-versus-raw confusion, brand mismatches, and outright guesses end up searchable alongside correct entries. As the database grows, the proportion of unverified entries grows with it.

How can I tell if a Yazio entry is wrong?

Check whether the macros reconcile against the calories (protein grams times 4, plus carbs times 4, plus fat times 9 should roughly equal the calorie count). Cross-reference against USDA FoodData Central, the manufacturer's own label, or a verified-database app like Cronometer or Nutrola. Be suspicious of entries with unusually round numbers, unusual protein values, or generic restaurant names.

Is Yazio's database worse than MyFitnessPal's?

Both rely heavily on community-submitted data, so both have the same structural quality problem. MyFitnessPal's database is larger, which means more wrong entries in absolute terms, but also more chances that a correct entry exists somewhere in the results. Yazio's is smaller and often cleaner for common foods, but less reliable for niche or regional items.

What calorie tracker has the most accurate database?

Cronometer is widely considered the most accurate free option, drawing from USDA FoodData Central and NCCDB. Nutrola takes a nutritionist-verified approach across 1.8 million+ entries with 100+ nutrients tracked per food, plus AI photo logging, voice logging, barcode scanning, and recipe URL import — features community-submitted apps typically own but apply to unreviewed data.

Can I just fix the wrong Yazio entries myself?

You can report incorrect entries and create custom foods for your own use. You cannot directly correct another user's submission in most cases, and there is no guarantee Yazio will update a flagged entry quickly. For foods you eat regularly, creating a custom food from the package label is the most reliable workaround within Yazio itself.

Does Nutrola have a free tier?

Yes. Nutrola offers a free tier in addition to the €2.50/month paid plan. Both tiers include nutritionist-verified data and zero ads. The paid plan unlocks the full feature set including AI photo logging, voice logging, advanced nutrient tracking, and recipe URL import.

How does Nutrola keep its database verified at 1.8 million+ entries?

Nutrola combines regulatory food composition databases (USDA FDC, EFSA, and international equivalents), manufacturer-provided label data, and a nutrition-professional review process for new entries. The pipeline is designed so that entries are reviewed before becoming searchable, rather than published first and corrected later.

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Final Verdict

Yazio's database problems are not a user failure. They are the predictable result of a community-submission model without nutritionist gatekeeping. If your tracking has felt off — macros that do not match the food, calorie totals that do not line up with your intake, progress that stalls for no obvious reason — the odds are good that bad entries are part of the story. You can keep using Yazio and work around the issue with manual checks and custom foods, or you can switch to a verified-database app and remove the problem at the source. Cronometer is a strong choice for accuracy-focused users who can live with its interface. Nutrola pairs a 1.8 million+ nutritionist-verified database with AI photo logging in under three seconds, voice logging, 100+ nutrients tracked, 14 languages, zero ads, and pricing from €2.50/month with a free tier. Either way, the first step is the same: stop logging against numbers you cannot trust.