A calibrated reference for price, not a rule
Every buyer asks the same question about a quote: is this a fair price? The LevaData Benchmark answers it with data, for one specific part at one specific volume, as a range rather than a single "perfect" number.
A fair-market price band
For a specific part and quantity, built from anonymized customer transactions and distributor prices across a very large electronics universe.
A price thermometer
It shows if your price is in a good range, just okay, or clearly higher than where the market usually is, so you know where to spend your time.
A replacement for your judgment
It does not override your contracts or your engineers. It is a guide, not a mandate: a reference that supports your decisions.
Think of Kelley Blue Book or Zillow. They look at thousands of real car or home sales to estimate a fair value range. LevaData does the same for electronic parts, using real customer and distributor prices instead of a single list price or someone's opinion.
The Benchmark screen in LDZ
This is how the benchmark shows up for a buyer. Each row is one part you buy. The bar is the market band: it starts at the benchmark (the line) and runs up to the distributors' lowest price. The dot is your price. Click a colored tile to filter, and click any row to see what to ask the supplier for.
| MPN | Manufacturer | Supplier | Benchmark qty | Your price | Benchmark | Market band | Gap |
|---|
Real prices from customers and distributors, cleaned before they count
The benchmark is not a guess or a consultant's opinion. It combines two kinds of real price data into one anonymized pool, and AI cleans that pool before any benchmark is calculated.
What goes in
- Distributor price files and live distributor prices
- Customer transactions (what buyers actually paid)
What gets fixed
- Part numbers and manufacturer names cleaned up, so "TI" and "Texas Instruments" are one company
- All prices in US dollars, with standardized commercial terms
- Unit-of-measure anomalies excluded, such as a price per reel recorded as a price per piece
- Prices above distributor pricing excluded from the benchmark calculation
What comes out
- Prices from all sources in one place
- No customer can see another customer's prices
- Ready for the benchmark engine
Why this is hard to copy. Most companies only ever see one of these views: their own prices, or distributor list prices. LevaData combines multi-customer prices, live market feeds and a map of equivalent parts, and learns from real outcomes. That combination is the data moat.
Four steps from raw prices to a live benchmark
The method uses AI to group truly equivalent parts, learns the typical discount buyers get versus distributor prices, and applies that discount to today's distributor price. Because it starts from today's price, the benchmark moves when the market moves.
Benchmark engine
Takes in distributor price files and customer transactions, and stores them in one anonymized pool.
Result: one clean pool of pricesAI master clusters
Groups parts that are truly the same, using the part number's logical core pattern (LCP), form–fit–function rules and the alias / alternate graph. Like parts are compared only with like parts.
Result: clusters of equivalent partsLearned discount ratios
For each cluster, models learn the typical discount buyers get versus the market price, from many years of real deals, volumes and outcomes.
Result: a community discount per clusterLive benchmarks
Applies that learned discount to today's lowest distributor price for the part. As distributor prices change, the benchmark updates on its own.
Result: an achievable price to negotiate withTry the formula
Benchmark ≈ Distributor price × (1 − Community avg discount)
LCP: reading the DNA inside a part number
Manufacturers hide a lot of meaning inside a part number. The core describes the actual electrical and mechanical design. The suffixes usually describe packaging, temperature grade, RoHS status or tape versus tray. The Logical Core Pattern (LCP) algorithm learns that core from millions of part numbers, so price data for the same real part is pooled together.
Treating every full part number as unique splits one real part into thin, noisy buckets.
Separates the core that defines function from suffix noise. STM32F407VG stays; T6, T7, TR are variants of it.
Checks voltage, tolerance, package and reliability, so parts that only look alike in text are never treated as interchangeable.
Links known equivalents and approved alternates, so the system knows when parts A, B and C are genuine alternatives.
Like decoding a car's VIN. Two dealer ads can look similar but be different trims. Decoding the VIN tells you which offers are really the same model, engine and trim. LCP does that for parts, then learns what people actually paid versus the sticker price.
Two ends of the market, each with the volume behind it
Price depends on how many you buy, so every price LevaData shows comes with its order quantity. For each part there are two reference points: the distributors' lowest price and the volume you need to get it, and the median customer price (the LevaData Benchmark) with the volume customers buy at. Pick a part to compare the two ends.
Data from every region
Customer transactions and distributor prices from thousands of suppliers across regions.
US dollars, standard terms
All prices are converted to US dollars, and they already include standardized commercial terms, so they compare fairly.
A global market anchor
Like a global oil price: one reference, while your local price still varies with taxes and logistics. Compare it against your own regional situation.
Why you can trust it, and where to use your own judgment
The benchmark is built to stay honest over time. It also has edges. Knowing both is what makes it safe to use in a negotiation.
Built-in checks
Every band comes from large-scale real customer transactions and distributor prices, not rules of thumb.
If a cluster shows inconsistent prices, that is a signal to re-examine how its parts were grouped.
As new parts, alternates and deals appear, the alias graph and clusters are updated.
Unit-of-measure anomalies and prices above distributor pricing are excluded from the benchmark calculation, so they can't distort it.
Electronics manufacturers and brands use LevaData Benchmarks to reduce procurement cost and improve margins.
Edges of the map
Even with LCP and form–fit–function rules, small differences in grade, reliability, qualification or lifecycle can matter for your program.
The benchmark blends industries, regions, EMS / OEM mixes and volumes. Your portfolio may be more regulated, more niche or structurally different.
Extended terms, rebates, stocking, spread NRE, services and risk coverage don't show up in the unit price, so they sit outside the band.
For new, niche or low-volume parts, the band may rest on few observations or on extrapolation, so treat it as less firm.
Like a GPS arrival time. GPS uses millions of data points to estimate when you'll arrive. It doesn't replace your judgment about the road ahead, but it is far better than guessing. The benchmark plays the same role for price.
The band shows the market. Your situation shows where to land.
The band runs from the benchmark up to the distributors' lowest price. Add what you know about your own position to decide whether to aim for the low, middle or high end of it. Tick what applies to see how the target moves.
What do you bring to this negotiation?
Where to aim for STM32F407VGT6
With nothing ticked, aim for the middle of the band.
Illustrative only. The benchmark gives the price spine; you shape the full package.