How Data-Vendor TCO Calculator works

Scope

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.

• Databento pricing
• Polygon.io pricing
• Alpaca Markets data plans
• Tiingo pricing
• FMP pricing
• Alpha Vantage premium

Tier-selection algorithm

For each vendor the algorithm runs this sequence:

1. Filter tiers to those whose resolutions[] array includes the requested bar resolution.
2. Filter further: if the scenario requires real-time, the tier must have includesLive=true.
3. Same for options (includesOptions) and futures (includesFutures).
4. If one or more tiers remain, pick the tier with lowest annualTotal = monthly·12 + oneTime.
5. 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:

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

1. List pricing only. Enterprise and volume-negotiated rates are not modeled.
2. US equities bias. European, APAC, and crypto-native feeds are partially or not covered.
3. Metered estimates are coarse. Databento actual spend can vary ±50% from the estimate depending on schema, delivery mode, and query patterns.
4. 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.
5. No regional pricing. All prices in USD; non-US customers may face FX or tax implications not modeled.
6. 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.