Should you build or buy Financial Market Data & Research Platforms?

Financial market data and research platforms deliver licensed real-time and historical pricing, exchange entitlements, broker research, and a professional messaging network to traders, analysts, and portfolio managers. Firms buy access as terminal seats or data feeds, because the underlying content is licensed from exchanges and data vendors, not owned by the firm.

Buy the platform and the data rights; you cannot build your way out of exchange fees, entitlements or the broker research library. Where a build pays is the research layer on top, the AskResearchGPT pattern, and that is a separate project from replacing the terminal.

Build it, buy it, or bridge?

⚒ Build it
✓ Buy it
➔ Bridge
Cost shape
Data licensing and exchange entitlement fees apply regardless of who builds the interface on top.
Bloomberg's list price is unpublished, and the vendor bundles data, news, analytics and messaging into one licensed seat regardless of who builds the interface on top.
License the data and workstation; put engineering effort into a proprietary research layer above it.
Time to value
A research assistant built on licensed feeds can ship in months once data rights are in place.
A terminal seat is active the same day a firm signs the licensing and entitlement agreements.
Start on a licensed terminal, then layer a custom research assistant on top of the same feeds.
Differentiation captured
The proprietary research and analysis layer on top of licensed data is where a firm can differentiate.
The underlying market data and messaging network is the same licensed content at every firm that buys it.
The vendor owns the data rights and terminal; the firm owns the analysis built on top of it.
AI feasibility today
AI research assistants like Man Group's agentic research workflow run on top of licensed feeds, not in place of them.
Vendors are building AI assistants into terminals rather than letting AI replace the licensed data underneath.
Add an AI research layer on licensed feeds; the data rights and exchange fees do not go away.
Who it fits
Firms with the engineering capacity to build a research assistant on top of licensed data they already buy.
Any firm needing licensed real-time pricing, exchange entitlements, and broker research for trading or analysis.
Firms wanting a proprietary research layer while still buying the underlying licensed data and terminal.

When building makes sense

There is a real build case in this category, but it sits entirely above the data, not in place of it. Morgan Stanley's AskResearchGPT searches Morgan Stanley Research, which publishes more than 70,000 proprietary reports a year, for its investment banking, sales and trading, and research staff, and similar tools exist inside investment banking and research divisions at other firms, but none of these replace a Bloomberg, FactSet, Capital IQ, or LSEG seat. The raw market data, exchange entitlements, and broker research those staff rely on are still licensed separately. That is the shape of a credible build: a research or analysis layer, an internal chat assistant, or a custom dashboard, built on content and data a firm already owns or pays to license. What a firm cannot build around is the licensed data itself. Exchange entitlement fees, display-versus-business-use licensing terms, and broker research relationships are commercial and regulatory arrangements, not engineering problems, and no firm has documented replacing a terminal seat by building a data pipeline from public sources instead.

When buying makes sense

Buying is the clear call because the actual product in this category is licensed content and exchange access, not software a firm could replicate by hiring engineers. Bloomberg publishes no public list price; third-party cost trackers put a seat at $28,000 to $32,000 a year, and FactSet, Capital IQ Pro, and LSEG Workspace charge comparable amounts for their own bundles of pricing data, research, and messaging network access. That fee buys entitlements and broker relationships built over decades, not a UI a competent team could rebuild over a quarter. The work of consuming and displaying that data is generic across every firm that licenses it, which means the platform itself confers no competitive edge regardless of who owns it. License the data and workstation sized to your trading and research needs, and put in-house engineering effort into the proprietary research and analysis layer that sits on top of the licensed feed, where the actual differentiation lives.

The desk read

Build-versus-buy analysis for Financial Market Data & Research Platforms is being written. In the meantime, the framework that drives every B4 call is on the B4 Index page.

Representative vendors Bloomberg TerminalFactSet Workstation + 5 more, listed in the full index

Frequently asked

Why can't a firm just build its own Bloomberg Terminal alternative?

Because the product is licensed data and exchange entitlements, not a piece of software. Exchange fees, display-versus-business-use licensing terms, and broker research relationships are commercial arrangements a firm has to negotiate and pay for no matter who builds the display layer on top of them.

Are banks actually replacing terminal seats with AI tools?

No documented case shows that. Deutsche Bank's DB Lumina and Bridgewater's Pocket Analyst are built on top of licensed and proprietary content their firms already hold, and traders and analysts at those firms still rely on Bloomberg, FactSet, or similar terminals for live market data alongside them.

What does a Bloomberg Terminal seat actually cost?

Bloomberg publishes no public list price. Third-party cost trackers put a seat at $28,000 to $32,000 a year, before any additional data subscriptions a firm adds on top. FactSet, Capital IQ Pro, and LSEG Workspace price their bundles in a comparable range for similar breadth of data and research access.

What are the main financial market data and research platforms?

Representative vendors include Bloomberg Terminal, FactSet Workstation, S&P Capital IQ Pro, and LSEG Workspace. Firms typically choose based on which broker research library and messaging network their counterparties already use. B4 Pro scores the full set.

The B4 Index scores every software category on two axes, strategic differentiation and AI feasibility, to classify it Build, Buy, Bridge, or Beware. See the full methodology.