Running the shipped data-vendor-tco engine on the input below produces exactly
this output. Continuous integration recomputes it against the engine bundle on
every build, so these numbers cannot drift from the code.
What does the Data-Vendor TCO Calculator methodology page document?
How AI Fin Hub's Data-Vendor TCO Calculator scores market-data vendors. Pricing sources, tier-selection algorithm, metered-pricing model, limitations. It states the formulas, assumptions, data sources, limitations, and reproducibility steps behind the Data-Vendor TCO Calculator, in the Finance category.
When was the Data-Vendor TCO Calculator methodology last reviewed?
This methodology was last reviewed on 2026-04-20. The matching tool is at https://aifinhub.io/data-vendor-tco/.
Are the Data-Vendor TCO Calculator numbers reproducible?
Yes. This page embeds a worked example whose output is the verbatim result of running the shipped data-vendor-tco engine on a fixed input; the embedded JSON is recomputed and diffed against the engine in CI, so the numbers cannot drift from the code.
The tool estimates annualized US-dollar total cost of ownership for
retail + small-team use of the six most-referenced market data APIs
in retail algo communities (2026). It is optimized for the question:
"given my universe, resolution, and asset-class needs, which
vendor is cheapest that actually fits?"
It does not:
model enterprise / negotiated rates;
include broker commissions, execution fees, or data redistribution licenses;
account for region-specific data feeds (Europe, APAC) beyond US equities;
predict future vendor pricing (tiers drift; see limitations).
Pricing sources
All pricing reflects published list rates on vendor sites as of 2026-04-20.
Filter tiers to those whose resolutions[] array includes the requested bar resolution.
Filter further: if the scenario requires real-time, the tier must have includesLive=true.
Same for options (includesOptions) and futures (includesFutures).
If one or more tiers remain, pick the tier with lowest annualTotal = monthly·12 + oneTime.
If no tier remains, the vendor is displayed dimmed with "Fits: no."
Metered pricing model (Databento)
Databento uses per-byte + per-symbol-day metered pricing rather than
flat subscriptions. Exact quotes require live meter estimation with
known symbol lists and schema selections. For the TCO calculator we
use a deliberately coarse modeled estimate:
monthly_estimate = base · universe_mult · resolution_mult · 0.1
With these multipliers:
Universe
Multiplier
Small (≤50 symbols)
1
Medium (~500 symbols)
5
Large (~2,000 symbols)
15
All US equities (~10,000)
40
Resolution
Multiplier
Daily bars
1
1-min bars
2
1-sec bars
4
Tick data
8
Level-2 order book
16
The multipliers are anchored to publicly-reported retail monthly spend
in r/algotrading and r/quant threads ($100–$500/month range for medium-universe
minute-bar research). They are calibration estimates, not quotes. For
production decisions, always verify via Databento's quoting tool.
Ranking + presentation
Qualifying vendors (Fits: yes) sort cheapest-first by annual total.
Non-qualifying vendors appear below, dimmed, so the reader sees both
what's available and what's not. There are no sponsored placements
or affiliate-tagged vendor rows; see
sponsor-disclosure.
Refresh
Pricing is refreshed when a vendor announces a material pricing
change. The most-recent refresh date is shown at the top of each
vendor row. Corrections are logged at
/corrections/.
Limitations
List pricing only. Enterprise and volume-negotiated rates are not modeled.
US equities bias. European, APAC, and crypto-native feeds are partially or not covered.
Metered estimates are coarse. Databento actual spend can vary ±50% from the estimate depending on schema, delivery mode, and query patterns.
Asset-class toggles are binary. "Options" means "some options coverage"; the tool does not distinguish between all-exchange options feeds vs a subset. For rigorous options research, consult the vendor's options-coverage docs directly.
No regional pricing. All prices in USD; non-US customers may face FX or tax implications not modeled.
Historical downloads are captured in oneTime where the vendor sells them as one-off; not all vendors distinguish historical from live in their pricing, which can distort comparisons.
Editorial independence
As of today there are no affiliate or sponsor relationships between
AI Fin Hub and any vendor referenced here. The tiered selection
algorithm is deterministic and applied uniformly. If that changes,
any commercial relationship will be disclosed at the row level and
at /sponsor-disclosure/.
Changelog
2026-04-20 — Initial release with 6 vendors (Databento, Polygon, Alpaca, Tiingo, FMP, Alpha Vantage). Metered pricing model calibrated against r/algotrading retail spend reports.