FactSet Alternatives: 2026 Options

Every round-up of FactSet alternatives has the same problem: it treats FactSet as one product and then recommends replacements that are not competitors with each other. Koyfin, PitchBook and a reference-data feed vendor solve unrelated problems. If a list puts them side by side without saying which layer of the subscription each one replaces, the list is not answering the question, it is answering four questions at once and hoping one of them is yours.

This article splits a FactSet subscription into the four things institutions actually buy it for, then judges alternatives inside each layer. It also states plainly which figures are verifiable and which are not, and publishes AmericanETP's own pricing instead of hiding it behind a demo form.

What are the main alternatives to FactSet in 2026?

There is no single alternative to FactSet, because FactSet is a bundle rather than a product. It combines an interactive workstation, an aggregated research and document library, licensed reference and constituent data, and portfolio analytics with attribution, sold under one contract and one login. The credible alternatives sit in five different categories, and most firms replacing FactSet end up buying from two or three of them.

The categories, with representative names:

• Terminal-style workstations: Bloomberg, S&P Capital IQ. Closest to a like-for-like replacement, and priced accordingly.
• Lightweight screening and charting: Koyfin, YCharts, TradingView. Cheaper per analyst, thinner underneath.
• Research and document search: AlphaSense, Factiva. Transcripts, filings, broker research, news archives.
• Advisor and asset-owner analytics: Morningstar Direct, Kwanti, and portfolio risk monitors sold to the same buyer.
• File-delivered reference data: AmericanETP, Exchange Data International, and the long tail of constituent and holdings feed vendors that appear in data marketplace listings.

This is why the pages currently ranking for "factset alternatives" contradict each other. One is answering "what do I give my junior analysts instead of a seat", another is answering "where do I get index membership history for a backtest". Both are legitimate. Neither answer works for the other question.

Why 'FactSet alternative' is the wrong unit of comparison

The unit of comparison that works is the layer, not the vendor. A FactSet subscription bundles four functions that different vendors sell separately, and a replacement plan has to account for all four or it fails on the one nobody mapped. In practice, the layer teams discover missing three months in is the licensed reference data layer, because a GUI-only tool lets you look at constituents without giving you the right to move them into your own systems.

Swapping the whole bundle for one cheaper product usually fails in a specific and predictable way. The screening layer is the most visible, so it gets replaced first and cheaply, and the team declares victory. Then a quant researcher asks for point-in-time membership with effective dates, or compliance asks whether the new tool's terms permit storing holdings in the firm's own database, and the project reopens.

Seat-based bundling is what makes this expensive to get wrong, because you are often paying for four layers per person even when only one person needs the fourth. That dynamic is covered in more detail in FactSet Pricing: What It Costs in 2026.

How much does FactSet cost, and what are you actually paying for?

FactSet does not publish a public price list for institutional subscriptions, and as of 5 September 2026 we could not verify a per-seat figure from FactSet's own pages. What circulates in round-ups as a firm number is almost always a buyer-reported figure from a specific negotiated contract, which is useful as an order of magnitude and worthless as a benchmark.

Say that plainly rather than guessing, because the guess is the dangerous part. A quoted figure from someone else's contract tells you nothing about the term length, seat count, module mix, or data licensing riders that produced it, and those are the variables that move the total.

What is structurally true of enterprise financial data contracts generally, and worth walking into a negotiation expecting, is that price scales with named users and with modules, and that redistribution or internal-storage rights are usually priced as a separate line rather than included by default. That is why the honest version of "how much does FactSet cost" is a different question: how many people in your firm need which layer, and does anything need to leave the GUI?

Once you frame it that way, the arithmetic often changes shape. Five analysts who need screening, one researcher who needs constituent history in a database, and a client-reporting team who need attribution is three purchases, and the cheapest bundle is frequently not a bundle. The mechanics of how seat-based licensing drives that total are broken down in FactSet Pricing: What It Costs in 2026.

Alternatives for screening, charting and monitoring

This is the layer with the most credible cheap alternatives, and the one most firms replace first. Koyfin, YCharts and TradingView all sit in the "look at it, chart it, screen it, share it" part of the workflow, and S&P Capital IQ sits at the heavier end of the same workflow. What this layer generally buys you is cost per analyst; what it generally does not buy you is the right to redistribute the underlying data or a documented point-in-time history.

Koyfin positions itself publicly against terminal-style incumbents including FactSet, and maintains comparison material framed that way. We are not reproducing figures or feature claims from that page here, because vendor comparison pages are written by one side and change without notice. Read it as positioning, verify any specific claim against the current pricing and terms pages, and note the date you checked.

Koyfin. Sits in the screening-and-charting layer rather than in the enterprise-bundle layer, which is the distinction that matters when you are mapping a replacement. We did not verify its pricing model, onboarding process or feature set from Koyfin's own pages as of 5 September 2026, so treat any figure you see quoted elsewhere as unsourced until you check the current pricing and terms pages yourself. What this layer does not carry, as a rule rather than as a criticism of one vendor, is research aggregation, attribution, and data you are contractually free to move into your own stack. Not for: anyone whose actual requirement is machine-readable constituent history under a redistribution licence.

YCharts. Also sits in this layer and is commonly evaluated alongside Koyfin. We did not verify its positioning, pricing or feature set from YCharts' own pages as of 5 September 2026, so the practical test is your own: does the licence permit storing the data in your systems, and are the histories it displays point-in-time or current-snapshot? Not for: systematic teams that need raw files and reproducible histories, unless those two questions come back the way you need them to.

TradingView. Evaluate it on price behaviour, charting and alerting rather than as a fundamentals research suite. We did not verify its current feature set, scripting capabilities or licence terms from TradingView's own pages as of 5 September 2026, and we are not reproducing undated claims about them here. Not for: research or reporting workflows that need auditable fundamentals, on the evidence of what this category is generally bought to do.

S&P Capital IQ. Belongs in the full workstation category alongside Bloomberg rather than in the lightweight screening category, which is the relevant point when you are planning a replacement: it is a change of vendor within a category, not a step down to a cheaper one. We did not verify its module list, coverage or pricing from S&P Global's own pages as of 5 September 2026. Not for: teams whose complaint about FactSet was the price of the bundle rather than the fit of the bundle, since replacing a bundle with a bundle rarely produces a cost saving.

One honest note about this layer generally: it handles extended and overnight sessions unevenly, and if your workflow now touches near-continuous trading you should test that specifically. The data-side consequences are covered in 24×5 Trading: What It Breaks in Data.

Alternatives for research, filings and document search

This layer covers transcripts, filings, broker research, news archives and expert-call libraries, and the named alternatives in the current search results are AlphaSense and Factiva. It is the layer least substitutable by open sources, because the value is in licensed content plus retrieval quality, and it is also the layer most firms over-buy.

Over-buying happens because research access is easy to justify per seat and hard to measure per seat. Log usage before renewal rather than after, and be specific about which content sets are actually opened: a large share of the spend in this layer is often concentrated in a handful of publications and one transcript archive.

AmericanETP does not compete in this layer at all, and there is no version of our product that replaces it. If you arrived at this search looking for a cheaper way to search transcripts and broker research, evaluate AlphaSense and Factiva against your own usage logs and stop reading here, because nothing below is relevant to that decision.

Alternatives for index constituents, ETF holdings and reference data

This is the layer the ranking pages barely cover, and the one that most often breaks a migration. A constituent and holdings feed has to deliver daily membership with effective dates, identifiers that join cleanly to the rest of your stack, usable sector classification, history deep enough to backtest without survivorship bias, and licence terms that permit whatever you intend to do with the file. A GUI that displays holdings satisfies none of those requirements.

Judge any vendor in this layer on five things:

• Membership with dates. Additions and deletions with effective dates, not just a current snapshot.
• Identifiers. Something stable enough to join to your security master without a fuzzy name match.
• Classification. Sector and asset-class fields that stay consistent across rebalances.
• History depth. Enough archive that a backtest sees the index as it was, not as it is.
• Rights. Whether you may store, transform and redistribute the data, and at what scope.

What AmericanETP provides in this layer

AmericanETP delivers ETF and index constituent lists as CSV files by subscription, updated twice daily, with a 6pm EST primary run and a secondary run around noon EST. Coverage spans 3,878 US and global indexes and US-traded ETFs. Holdings files include Bloomberg FIGIs, and a separate fundamentals file carries the ETF description, leverage and constituent counts.

Archived constituent datafiles go back to July 2009, which is the part that matters for anyone reconstructing an index as it stood rather than as it stands. Delivery is by file download or FTP, so the data lands in your environment rather than in a viewer. Sample files are on the Downloads page, membership changes are published in the Constituent Change Report, and the identifier extensions are documented under Bloomberg Extensions.

Pricing is published rather than quoted: $1,500 per year or $150 per month for an individual, and $2,500 per year or $250 per month firm-wide. AmericanETP was founded in the 1990s as MasterDATA and renamed to avoid confusion with IBM's use of that term, which makes it older than most of the ETF data vendors it now sits alongside.

Who it is not for. Anyone who wants a screening interface, a research library, or attribution output. AmericanETP is a file feed, and if nobody on the team wants files, it is the wrong purchase. For how the sector fields behave across providers, see ETF List by Sector: 2026 Classification Guide.

Exchange Data International and the feed vendors listed in data marketplaces occupy the same layer with different coverage footprints and different licence structures. Compare them on the five criteria above and on delivery mechanics, and get the redistribution terms in writing before the technical evaluation, not after.

Alternatives for portfolio analytics and attribution

This layer answers why a portfolio returned what it returned, and the names that show up for advisor and asset-owner buyers are Morningstar Direct, Kwanti, and dedicated risk monitors sold into the same seat. The distinction that matters when you evaluate them is between a reporting tool, which presents figures it was given, and an attribution engine, which decomposes return into effects you can defend to an investment committee.

Both are useful and they are not interchangeable. A reporting tool with pretty output and weak decomposition will pass an internal review and fail a client question about why the sector effect and the selection effect do not reconcile.

The upstream dependency is the point most evaluations miss. Attribution output inherits every flaw in the holdings and benchmark data feeding it, so a benchmark whose constituents are a current snapshot rather than point-in-time membership will produce attribution that quietly misattributes rebalance effects to selection. This layer consumes the reference data layer, it does not replace it.

For a fuller comparison of this layer, including where Morningstar's own products fit and where they do not, see Morningstar Alternatives: 2026 Options.

Who this layer is not for. Systematic funds that compute their own attribution in-house. They need the holdings and benchmark files and nothing above them.

Are there free alternatives to FactSet?

Partly, and only for the screening layer. Exchange and issuer disclosures, index provider public factsheets, regulatory filing archives and the free tiers of charting tools will get an individual analyst a long way on current-state questions. They will not get a firm a reproducible history, and they will not get anyone redistribution rights.

Free breaks in four specific places, and it always breaks in the same order:

1. Point-in-time history. Issuer holdings pages publish today. Reconstructing 2014 from public pages is an archaeology project.
2. Corporate action handling. Splits, spin-offs and ticker changes need to be applied consistently or your history quietly diverges from reality.
3. Identifier joins. Free sources give you tickers and names. Joining on those across sources is where most of the engineering time goes.
4. Redistribution rights. Terms of use on public pages generally prohibit exactly what a data vendor or a client-reporting workflow needs to do.

A worked illustration. Ask two free sources for the price-to-earnings ratio of the S&P 500 and you will usually get two different numbers, because they differ on trailing versus forward earnings, on whether earnings are GAAP or operating, on how negative-earnings constituents are treated, and on the weighting method used to aggregate. None of the sources is lying. They are computing different things and labelling them identically.

That is a reference-data problem wearing a fundamentals costume: the answer depends on the constituent set and the weights as of a specific date. The mechanics are worked through in PE of S&P 500: Why the Numbers Disagree, and the reproducible calculation is set out step by step in Build the P/E Ratio S&P 500.

How to choose: a selection framework by firm type

Start from your firm type, not from a feature grid, because firm type determines which layers you must buy and which you can drop entirely. Then apply licensing as a first-order filter, before the demo, because a tool that fits perfectly and forbids your intended use is not a candidate.

• Quant fund. Must buy: licensed reference and constituent data with deep point-in-time history and clean identifiers. Can usually drop: research aggregation, GUI-first workstation, vendor attribution.
• RIA. Must buy: screening and client-facing analytics, plus attribution if you report performance to clients. Can usually drop: broker research libraries and raw feed licences.
• Bank desk. Must buy: workstation and research, with entitlement controls and audit trails that satisfy compliance. Can usually drop: little, which is why bundles keep winning here.
• Exchange. Must buy: reference data with explicit redistribution scope, since the output is a product. Can usually drop: advisor-facing analytics.
• Data vendor or fintech. Must buy: file or API delivery with redistribution rights and a documented change history. Can usually drop: every GUI layer.

Delivery mechanics follow from that. GUI-only tools require no engineering and permit no automation. APIs suit request-driven applications and rate-limit batch work. File drop and FTP suit nightly batch, backtests and warehouse loads, and are the least fashionable and most reliable option for anyone whose real consumer is a database rather than a person.

Questions to ask before you book a demo

• What exactly may we do with the data: view, store, transform, redistribute, and at what scope?
• Is history point-in-time, and how far back does the archive actually go?
• Which identifiers are included, and are they licensed for our use?
• What is the delivery mechanism and the daily update schedule, in a specific timezone?
• What happens on a rebalance day, and is the change published separately?
• What is the price for our seat count, in writing, before evaluation?

If a vendor cannot answer the licensing question in one sentence, that is your answer.

Where each option genuinely wins

FactSet remains the better buy for a specific and legitimate case: a firm that wants one contract, one support line, and consistency across all four layers, and would rather pay for the bundle than manage four vendors and reconcile their differences. That is not a compromise, it is a real preference with real operational value, and firms that unbundle for price sometimes rebundle later for exactly that reason.

AmericanETP does not compete with FactSet as a whole and makes no claim to. It competes in one layer, the constituent and holdings feed, where the deliverable is a CSV file with dated membership, FIGIs and archives back to July 2009, at published pricing. If your unmet need is a screening interface, a research library or an attribution engine, buy those from the vendors above.

FAQ

Who are FactSet's main competitors?

By layer rather than by brand: Bloomberg and S&P Capital IQ compete as full workstations; Koyfin, YCharts and TradingView compete on screening and charting; AlphaSense and Factiva compete on research and document search; Morningstar Direct and Kwanti compete on analytics and attribution; AmericanETP and Exchange Data International compete on file-delivered constituent and reference data. Any list that presents these as substitutes for one another is comparing different products.

Which is better, Bloomberg or FactSet?

They are peers, and the honest answer depends on the desk. Bloomberg is generally chosen where messaging, fixed income and market-facing workflow dominate; FactSet is generally chosen where research, screening and portfolio analytics dominate. Neither publishes a rate card we could verify as of 5 September 2026, so the comparison you can actually run is a trial with your own workflows rather than a spec sheet.

Does JP Morgan use FactSet?

We cannot verify the current vendor arrangements of any specific bank, and neither can the pages that assert it. Large banks typically run several of these platforms at once, allocated by desk and role, because the layers serve different functions. Treat any confident claim about a named institution's vendor stack as unsourced unless it links to that institution's own disclosure.

Can I use FactSet for free?

There is no general free tier we could verify. Access is usually available to students and faculty through university library subscriptions, and vendors run time-limited trials as part of a sales process. For ongoing free work, you are looking at exchange and issuer disclosures, index provider factsheets and free charting tiers, which cover current-state screening and not much else.

What is the cheapest FactSet alternative for index constituent data?

AmericanETP publishes its pricing rather than quoting it: $1,500 per year or $150 per month for an individual, and $2,500 per year or $250 per month firm-wide, for twice-daily CSV constituent files covering 3,878 US and global indexes and US-traded ETFs, with archives back to July 2009. Other feed vendors in this layer generally quote rather than publish, so a like-for-like comparison requires you to request terms. Compare on history depth, identifiers and redistribution scope, not on headline price alone.

Do FactSet alternatives include redistribution rights?

Rarely by default, and this is the single most expensive assumption in a migration. GUI-first tools typically license viewing by a named user and prohibit systematic extraction; feed vendors license storage and transformation, with redistribution priced separately by scope. Get the intended use written into the agreement before evaluation, because discovering the restriction after integration means rebuilding.

Sample constituent files, the fundamentals file layout and the FIGI-bearing holdings format are all available to inspect before you talk to anyone: see americanetp.com or use the contact page if you need a specific index checked against your coverage list.