Alternative data providers: rankings and buying guide for 2026
By Jan Suski, six years working in digital. How the scoring works.
Alternative data refers to non-traditional data sources used in finance: transaction records, satellite imagery, web traffic, job postings and app usage, read for what they say about a company before the company says it.
Size of the global alternative data market in 2025, projected to reach $135.8 billion by 2030. Hedge funds allocate over $1.6 million a year each to it, and that figure covers the datasets rather than the people who read them.
Eight providers ranked below on what they collect, who buys it, and how close the data sits to ground truth. The data providers guide covers how alternative data differs from the B2B and company categories.
Quick comparison of alt data providers
Eight providers with the source they draw on, what entry costs, and who buys the output. Five of the eight publish no rate card at all, which is how this market works: price follows the dataset, the refresh rate and the exclusivity you negotiate.
| # | Provider | Data source | Entry price | Primary buyer |
|---|---|---|---|---|
| 1 | AlphaSense | Filings, expert call transcripts, broker research | $17,500 median* | Hedge funds and corporate strategy |
| 2 | FactSet | 60+ aggregated alternative datasets | Quoted, enterprise | Institutional investors |
| 3 | Bright Data | Web scraped data across 120+ domains | From $2.50 per 1,000 records | Quant teams building their own signals |
| 4 | Similarweb | Web traffic and digital engagement | Quoted, custom package | Fundamental investors and corporate clients |
| 5 | Placer.ai | Geolocation and foot traffic | Quoted | Retail and real estate analysts |
| 6 | Revelio Labs | Workforce and hiring data | Quoted | Private equity firms and asset managers |
| 7 | Panjiva | Import and export shipment records | Quoted | Supply chain and trade-exposed coverage |
| 8 | PassBy | Geolocation, 94% correlation to ground truth | Quoted | Institutional data buyers testing accuracy |
Medians marked * are anonymised buyer-reported contract values published by Vendr, not vendor list prices. Everything else is the price the vendor publishes itself.
Types of alternative data sources
Alternative data includes web scraping, app usage and social sentiment, and each category answers a different question about company performance.
Transaction and consumer spending data
Transaction data reflects real-time consumer behavior through purchases. Aggregated credit card data and point of sale data show revenue direction weeks before a quarter closes.
Consumer spending data is the closest alt data gets to reading a company's top line directly, which is why it carries the highest prices and the tightest panel restrictions.
Web data and app usage
Web scraped data includes product listings and pricing from websites, useful for competitive benchmarking and for tracking assortment changes as they happen.
Web traffic shows demand direction for anything sold online. App usage adds the mobile half, which for consumer names is often the larger half.
Geospatial and satellite imagery
Geospatial data uses satellite imagery to monitor physical activities. Investors use satellite images to watch industrial activity, count cars in car parks and track construction against schedule.
Geolocation data tracks consumer movement and behavior patterns, which reads store traffic without waiting for a filing.
Consumer sentiment and survey data
Sentiment analysis from social media gauges public opinion on brands. Survey data provides insights into consumer preferences and intentions, and sentiment data is the cheapest alt data to buy and the hardest to trade on.
Patent data, government contracts and regulatory filings sit alongside these as public sources most teams underuse.
Top alternative data providers, ranked
AlphaSense
Best fit: hedge funds and corporate strategy teams that want alternative data and traditional financial data searchable in one place.
AlphaSense combines financial statements, regulatory filings and expert call transcripts with semantic search across the corpus. Expert call transcripts are the differentiator: primary research at scale without commissioning it.
Pricing
Seat-based, from roughly $24,000 a year per user. Vendr's median contract is $17,500 across 38 purchases.
Why it's ranked #1. It is the only entry here that pairs alternative datasets with fundamental data in one search. It loses on breadth of exotic sources and wins on how fast a analyst gets to an answer.
FactSet
Best fit: institutional investors who want alternative data mapped to securities they already track.
FactSet aggregates over 60 alternative datasets and maps them to its existing entity model, which removes the hardest part of incorporating alternative data: joining a vendor's identifiers to your own.
Pricing
Quoted, enterprise, usually as a module on an existing terminal contract.
Why it's ranked #2. Entity mapping across 60 datasets saves more analyst time than any single dataset provides. It ranks below AlphaSense because the datasets are licensed rather than owned.
Bright Data
Best fit: quant teams with engineering capacity who want raw data and will build the signal themselves.
Bright Data offers access to over 120 domains of structured web data, delivered as feeds rather than dashboards. Unstructured data becomes structured datasets before it reaches you.
Pricing
Datasets start at $2.50 per 1,000 records, so 100,000 records costs $250. Subscribing to refreshes cuts the per-record rate by up to 80%.
Why it's ranked #3. Broadest raw web coverage here and the most flexible. It sits third because it delivers inputs rather than answers, which suits few buyers outside quant.
Similarweb
Best fit: fundamental investors and corporate clients tracking digital demand for named companies.
Similarweb estimates web traffic, channel mix and audience overlap. For any business selling online, it is the fastest read on demand direction between reports.
Pricing
Quoted. Vendr records a $37,800 median annual contract across 157 purchases, ranging from $14,220 to $96,000. Unofficial buyer data rather than vendor pricing.
Why it's ranked #4. Best coverage of digital demand at a price institutional buyers can justify. Estimate accuracy on small domains is what keeps it out of the top three.
Placer.ai
Best fit: analysts covering retail, restaurants and real estate, where footfall is the leading indicator.
Placer.ai measures visits, dwell time and trade-area composition from mobile location data, giving a weekly read on store performance that no filing provides.
Pricing
Quoted by venue count and data depth.
Why it's ranked #5. Strongest geolocation coverage for US physical retail. Panel-derived estimates need validating against known counts, which is the standard caveat for the whole category.
Revelio Labs
Best fit: private equity firms and asset managers reading headcount as a proxy for company performance.
Revelio Labs builds workforce datasets from public profiles and job postings: headcount trends, attrition, skill mix and hiring direction by function.
Pricing
Quoted by dataset and delivery method, with API access available.
Why it's ranked #6. Hiring is one of the earliest observable signals of a strategy change. The data is inferred rather than reported, which caps confidence and the ranking.
Panjiva
Best fit: teams covering trade-exposed sectors where shipment volumes lead reported revenue.
Panjiva compiles import and export records into supplier-buyer relationships, which exposes sourcing shifts before either party discusses them.
Pricing
Quoted, usually within an S&P Global contract.
Why it's ranked #7. Unmatched on trade flows and irrelevant outside them. Narrow fit rather than weak data.
PassBy
Best fit: institutional data buyers who want a stated accuracy figure before committing.
PassBy sells geolocation data and publishes a 94% correlation to ground truth, which is a rarer claim in this market than it should be.
Pricing
Quoted.
Why it's ranked #8. Publishing a correlation figure at all is a mark in its favour. Coverage and track record are shorter than Placer.ai's, which is why it sits below.
What an alternative data platform costs
Hedge funds spend over $1.6 million a year on alternative data on average, and that figure covers the datasets rather than the people who use them.
The headcount is the hidden line. An estimated 1,190 full-time alternative data employees existed across the industry in 2017, and the ratio of staff to datasets has not improved since.
Alternative data vendors price by dataset, by entity coverage and by delivery method. Raw data is cheapest, mapped data costs more, and a platform that runs the analysis costs most.
Data quality and regulatory compliance are the two standing challenges. Sourced data must comply with GDPR and CCPA, and personally identifiable information in a location or transaction feed is a legal problem rather than a technical one.
Alt data for consumer behavior and competitive intelligence
Alt data started as an edge for hedge funds and has become market intelligence for operators. Corporate clients now buy the same feeds their investors read.
Alternative data can reveal business patterns not visible in conventional reports, and those insights often surpass traditional financial reports in predictive power because they arrive first.
For competitive intelligence, the use is direct. Web traffic shows a rival's demand, job postings show what they are building, shipment records show what they are sourcing, and none of it waits for an earnings call.
For consumer behavior, transaction and geolocation feeds answer questions surveys ask badly: what people bought, where they went, how often they came back.
Alternative data enhances due diligence and portfolio monitoring, and it helps investors anticipate trends before formal disclosures. Read alongside fundamental data rather than instead of it.
How investors turn alt data into decisions
Non traditional datasets earn their cost where they change investment decisions, not where they are bought. Most alt data programmes fail in the gap between the two.
The datasets that inform investment decisions are the ones tested against a thesis. Investment strategies built on a feed nobody backtested inherit the vendor's marketing as an assumption.
From data sets to market signals
Raw feeds are inputs. Someone has to analyze data against a hypothesis, and data analysis of a single dataset in isolation produces coincidence rather than market signals.
Pick a company you already understand, pull the dataset for a period you can check, and see if the series would have told you what the filing eventually did. That backtest is what separates investment research from dataset shopping.
Real time data matters more here than in most categories, because the entire edge is arriving before traditional sources do. A feed delivered monthly has already given the advantage away.
Where it fits across strategies
Public financial markets use alt data for earnings prediction and thesis testing. Private markets use it for diligence, where target companies disclose less and a workforce or web traffic series is one of few external reads on business performance.
Corporate teams use the same data sets for brand performance tracking and competitive benchmarking, which is the same analysis with a different question attached.
Weather patterns, government contracts and patent filings sit in the long tail. Each is decisive in one sector and noise in every other, which is why market data breadth matters less than fit.
Compliance and risk management
Compliance and risk management run alongside every purchase. Legal review of the collection method, not just the licence, is what enables teams to use a dataset without inheriting the vendor's exposure.
Data driven decisions built on a feed you cannot explain to a regulator are a liability dressed as actionable insights. Ask how the data was collected, from whom, and under what consent, before the first invoice.
Alternative data FAQ
What is alternative data?
Non-traditional data used to assess company performance and market trends: transactions, web traffic, satellite imagery, geolocation, job postings, app usage and sentiment. It sits alongside traditional data sources such as financial statements and regulatory filings.
Who buys alternative data?
Hedge funds, asset managers, private equity firms and institutional investors, increasingly joined by corporate strategy teams using the same feeds for competitive benchmarking.
How big is the alternative data market?
It reached $18.74 billion in 2025, with projections of $135.8 billion by 2030. That compound annual growth rate is why every data vendor now describes itself as an alternative data platform.
Is alternative data legal?
Yes, where sourcing and processing comply with GDPR, CCPA and the relevant market rules. The risks are personally identifiable information in raw feeds and material non-public information reaching an investment process, and both are diligence questions rather than technical ones.
What is the most useful alternative data source?
It depends on the asset classes and sectors you cover. Transaction data for consumer names, geolocation for physical retail, web traffic for digital businesses, shipment records for industrials. No single source works across all of them.
Bottom line
Match the dataset to what you cover, then check the mapping. Most alternative data disappoints because nobody joined the vendor's entities to the portfolio, not because the data was wrong.
AlphaSense and FactSet for research breadth, Bright Data for raw inputs, Similarweb for digital demand, Placer.ai and PassBy for footfall, Revelio Labs for workforce, Panjiva for trade.
Then run it against a period you already understand. Alt data that cannot explain last year is not going to predict next quarter.