AlphaSense Alternatives for Buy-Side Firms

For buy-side firms, the best AlphaSense alternative is Kepler: enterprise intelligence built around your own firm, where our engineers build your firm’s ontology, every document is read in full before you ask, and every number is computed in code and cited to the exact page, line or cell. A research library tells you what has been said; Kepler gives you the numbers your firm decides on, on your own data and definitions, in a form you can defend at an investment committee.

Teams that look past AlphaSense usually have one of three reasons:

• Cost and contract terms. AlphaSense doesn’t publish prices, contracts commonly auto-renew, and expert calls and premium content add to the bill. The reported figures are below.
• AI that knows the firm. The context a firm cares about most sits in its own fund documents, CRM, portfolio data, models and notes. A library built around licensed content treats that material as one more source to search, and the firm’s definitions live in prompts and Skills rather than in a model of the firm (how to get AI to use your firm’s definitions). What users say: “It’s difficult to bring inside organizations with a lot of security concerns with internal data,” a G2 reviewer wrote in October 2026 (AWS Marketplace, G2 reviews).
• Numbers they can prove. When a figure goes into a model, an IC memo or an LP report, the analyst has to show where it came from and how it was calculated.

We build Kepler, so it’s listed first and labeled. Facts about other vendors come from their websites, press releases and public reporting as of November 2026, and pricing appears only where it has been reported.

How much does AlphaSense cost?

AlphaSense doesn’t publish prices, so the best guide is purchase data collected by software-buying platforms:

• SpendHound reports average spend of $10,131 a year for smaller customers and $139,455 a year for enterprise customers, from the contracts of 160 AlphaSense customers it tracks.
• Vendr reports that contracts commonly auto-renew unless canceled 60 to 90 days ahead and often carry 3% to 5% annual increases on renewal, and that expert calls and premium content are often sold separately.

Seats, expert-call usage and premium content drive the total, and the notice window matters as much as the price if you plan to switch. Reported prices for Bloomberg and Hebbia sit under each tool below.

AlphaSense alternatives at a glance

Tool	Best for	Core content	Your firm’s data	Where numbers come from
Kepler	AI built around your firm, every number provable	26M+ SEC filings with XBRL, 40M+ IR documents and transcripts, market data, news	Your firm’s ontology, every document read in full	Computed by code, cited to page, line or cell
Bloomberg ASKB	Teams that work in the Terminal	Bloomberg data, news, research from 800+ providers	Chats, Worksheets, Bloomberg Drive	Bloomberg data with linked sources
Hebbia	Questions across large document sets	Global filings, transcripts; licensed feeds you connect	SharePoint, Box, Egnyte, Datasite, CRM	Model answers per cell, with source links
Third Bridge, AlphaSights	Expert-call transcripts	Expert interview libraries	Not the focus	The transcript itself
FactSet, S&P Capital IQ Pro	Firms already on either platform	Fundamentals, estimates, transcripts, filings	MCP access for your own AI tools	Structured datasets
Bigdata.com	Licensed content inside your AI assistant	News, transcripts, broker research, filings	Uploads	Excerpts with source, date and URL
Claude or ChatGPT for Financial Services	A general assistant on data you license	The providers you connect	Connectors to file stores and warehouses	Model answers linked to sources

The best AlphaSense alternatives

1. Kepler (our pick)

We build Kepler.

Best for: buy-side teams that want AI centered on their own firm, and numbers they can defend in front of an investment committee.

A research library answers “what has been said about this company?” Most of what a buy-side team decides on is a different question: what the firm owns, what it’s exposed to, how a number moved on the firm’s own definition, and which documents in a pile of thousands say something that matters. Those answers depend on your data and your definitions, and the numbers have to hold up.

That’s the problem we built Kepler for. Our engineers built this layer before at Palantir, and on site with your team they start by building your firm’s ontology: a living model in which each fund, vehicle, holding, manager, deal and document resolves to one record, and terms like adjusted EBITDA or net IRR carry your firm’s definition. Public data is loaded on day one: 26M+ SEC filings with full text and XBRL, 40M+ IR documents and earnings call transcripts, market data and news for 14,000+ companies in 120+ countries. In the first week we connect your SharePoint, OneDrive, Box, Egnyte, email, CRM, data rooms and research notes, read-only, and we read every page of every document with small extraction models we train, so a question returns the complete set. We route each task to the model best suited to it: our small models do the reading, frontier models handle the question and the write-up, and every better model that ships makes Kepler better.

Every figure is pulled from its source and computed in code with the formula visible; the model never writes a number. Click one and the exact page, line or cell opens beside the answer. Before an answer reaches you, each citation has been matched to its source, and where two sources conflict you see both. Agents watch your coverage on schedules and on new filings, then deliver alerts with the exact quote, updated models and drafted memos to the Kepler app, Excel, PowerPoint, Claude, ChatGPT or Slack. Your engineers can build their own agents and apps on the same data and engine through our API and MCP server.

What you get that a library doesn’t

• Your firm’s ontology, built from your own documents and kept current as new ones arrive (how it works).
• Complete answers. Every document is read in full before you ask, footnotes and scanned exhibits included.
• The same number every time, computed by code over cited inputs, as filed or as restated.
• Your playbooks, built in: the earnings update the way your PM wants it and the IC memo in house style, saved once and applied every time.
• Agents you can leave alone, acting as the person who set them up and never steered by what they read.
• Kepler everywhere: the app, Office, Claude, ChatGPT, Slack, email and the Kepler API.
• Your rules built in: a single permission check on every read by a person or an agent, every query logged with its lineage, SOC 2 Type II, our cloud or yours (security).

Questions former AlphaSense users bring to Kepler

• “Every company in our coverage that mentioned pricing pressure on its last two calls, with gross margin for the same quarters from the 10-Qs.”
• “Tell me the morning any holding changes guidance, with the quote and the prior number.”
• “Trading comps for US cybersecurity on each filer’s own adjusted EBITDA, every multiple traced to a line item.”

“I can’t put a number in front of a client if I can’t show where it came from.” (Managing Director, multi-billion dollar fund)

Not the right fit if: you’re an individual investor looking for a research subscription for your own portfolio. We build and deploy Kepler for investment firms.

Try it on your coverage at app.kepler.ai or book a demo. For the full comparison, read Kepler vs AlphaSense.

2. Bloomberg Terminal with ASKB

Best for: teams that already run their day in the Terminal.

ASKB opened in beta on February 23, 2026 and reached general availability on October 15. Where AlphaSense licenses broker research into its own library, ASKB’s agents read research from more than 800 sell-side and independent providers alongside Bloomberg data, news and Bloomberg Intelligence, and they can write BQL for Excel and BQuant. Third Bridge expert transcripts have been in ASKB since April 2026. Clients can search their own Instant Bloomberg chats, Worksheets and Bloomberg Drive files, and Drive answers cite the file and passage.

Trade-off vs AlphaSense: Bloomberg leads on real-time market data and analytics. A single Terminal was reported in 2024 at $31,980 a year for two-year contracts starting in 2025, so it is seldom the cheaper choice. For the two side by side, see AlphaSense vs Bloomberg ASKB.

What users say: “Customer support is not as efficient as it used to be,” wrote a VP of product reviewing the Terminal on Capterra in November 2025.

3. Hebbia

Best for: asking the same questions across hundreds of documents at once.

Where AlphaSense brings its own library, Hebbia brings a workspace. Its Matrix lays documents out as rows and questions as columns, and Matrix 2.0 (August 26, 2026) turns a finished grid into a memo, deck, model or email, stopping for a person to review each step. Since October 1, Headless Hebbia has offered the same retrieval through an API and an MCP server that Claude and ChatGPT can call. Hebbia covers filings from the US, Europe, the UK, Canada, Australia and New Zealand plus transcripts; FactSet, S&P Capital IQ, PitchBook, Preqin, Daloopa, Third Bridge and Guidepoint connect through your licenses, and a Datasite integration followed in October 2026.

Trade-off vs AlphaSense: no content library of its own; market data, expert calls and research come through licenses you hold. Sacra puts the Professional tier at $10,000 per seat a year.

See AlphaSense vs Hebbia and Hebbia alternatives.

4. Third Bridge and AlphaSights

Best for: teams whose main use of AlphaSense is expert-call transcripts.

With Tegus inside AlphaSense, these are the two large independent sources of expert research. Third Bridge says its library holds 100,000+ expert transcripts, and its MCP server (March 2026) makes them reachable from ChatGPT, Claude and firms’ own models. Its transcripts also appear in Bloomberg ASKB, Hebbia and Claude for Financial Services. AlphaSights adds hundreds of company one-pagers and investor-led call transcripts to its AlphaNow library each week, and every transcript comes with an AI call summary.

Trade-off vs AlphaSense: expert research only. You still need sources for filings, broker research and financial data.

5. FactSet and S&P Capital IQ Pro

Best for: firms that already use either one as their system of record for fundamentals.

FactSet launched an MCP server in December 2025 covering fundamentals, estimates, ownership, M&A and pricing, added AI Document Search in 2026, and offers integrations with ChatGPT and Claude. S&P Capital IQ Pro’s ChatIQ answers questions across documents with citations, ChatIQ Spreadsheets (October 2026) builds Excel models with refreshable Capital IQ formulas, and Capital IQ data reaches Claude through Kensho’s MCP server. On October 27, 2026, S&P Global announced a strategic review of Capital IQ and associated data businesses, worth weighing before a multi-year renewal.

Trade-off vs AlphaSense: both are strongest on structured data. Check which qualitative content your license includes, since expert calls usually need a separate source.

6. Bigdata.com

Best for: firms that want licensed research content inside Claude, ChatGPT or their own agents.

Bigdata.com, RavenPack’s content platform, delivers the kind of material AlphaSense users read into tools they already run: transcripts, 200+ premium news sources, research from 100+ broker providers, filings for 90,000+ companies and Economist Intelligence Unit content, by API or as a connector in Claude, ChatGPT and Microsoft Copilot. Each answer is a ranked excerpt carrying its source, date and URL, and uploaded files sit in the same search.

Trade-off vs AlphaSense: a content layer, so analysis happens in whichever assistant you connect. Its expert-interview feed (via Knowledge Ridge) adds 200+ new interviews a year.

7. Claude for Financial Services and ChatGPT for Financial Services

Best for: firms that want a general AI assistant working over data they already license.

Anthropic’s Claude for Financial Services connects to FactSet, S&P Capital IQ, PitchBook, Morningstar, LSEG, Moody’s, Daloopa, Third Bridge and Guidepoint, with Excel, PowerPoint and Word add-ins. OpenAI’s ChatGPT for Financial Services, launched September 10, 2026 for eligible institutions, includes SEC filings, Quartr transcripts, PitchBook Essentials, Daloopa and LSEG News, plus more than 50 connectors. Both say their tools link figures to source documents and don’t train on business data by default.

Trade-off vs AlphaSense: premium data needs your own provider licenses, and OpenAI describes its included datasets as “selected coverage.” Each is a frontier model on its own, learning your firm’s definitions from what you connect. Kepler runs inside both. See ChatGPT vs Claude for financial analysis.

What AlphaSense does well

AlphaSense is strongest on reading: the Tegus expert-call library (acquired in 2024), which it now puts at 300,000 interviews, sell-side research through Wall Street Insights, and one search across what it says are more than 500 million documents. Expert-call transcripts and broker research are licensed content outside Kepler, so teams that read them daily keep a library for that; our Tegus alternatives guide covers the expert-call options.

Kepler vs AlphaSense

	Kepler	AlphaSense
Built around	Your firm: its ontology, documents and definitions	A licensed library of 500M+ documents
Where each number comes from	Pulled from source and computed by code, cited to the line	Standardized financials and Canalyst models; AI answers and agents over the library and your documents
What a citation opens	The exact page, line or cell, with the formula for calculations	The text snippet behind the answer
Your firm’s definitions	Built into your ontology and applied every time	Encoded per workflow in Skills and templates
Finding everything	Every document read in full before you ask	Search ranking across the library
When sources disagree	Shows both, with sources, and flags the conflict	Reconciled inside agent runs
Same question twice	The same number, with the same sources	No published reproducibility guarantee
As filed vs as restated	Either, on any date	Not a stated feature
Work that runs on its own	Agents on schedules and events, acting as their owner	SuperAnalyst monitoring and refreshed outputs (early access June 2026, launched on October 6, 2026)
Where it works	App, Excel, PowerPoint, Word, Claude, ChatGPT, Slack, API	AlphaSense app, Excel and PowerPoint assistants, Agent API
Deployment	Our engineers on site; our cloud or yours	Enterprise SaaS or private cloud

Choose AlphaSense when the work is mostly reading: expert calls, broker research and searching what management and competitors have said.

Choose Kepler when the work is the firm’s own: numbers that go into a model, an IC memo or an LP report, your documents and definitions, your playbooks, and agents that watch the book while you’re away.

How to choose

Start with the part of AlphaSense you would be replacing:

• Expert calls: Third Bridge or AlphaSights. Third Bridge content is also reachable inside Bloomberg ASKB, Hebbia and Claude.
• Real-time market data and news: Bloomberg.
• Fundamentals and estimates: FactSet or S&P Capital IQ Pro.
• Questions across a data room or peer set: Kepler, which reads every document once and keeps it in your ontology for the next deal, or Hebbia for a one-off grid over a set you choose.
• Licensed content inside your own AI tools: Bigdata.com.
• AI built around your firm, with numbers you can prove: Kepler.

Then, before your renewal date:

1. Find your notice window. Vendr reports that AlphaSense contracts commonly auto-renew unless canceled 60 to 90 days ahead.
2. Look at what your analysts actually open. If most usage is filings and transcripts, many tools cover it. If it’s expert calls and broker research, few do.
3. Trial on live work. Use a current deal or coverage list, click through every citation, and ask the same question twice. Our guide to checking AI numbers lists the tests.

For the wider field, see the best AI tools for investment research and the best AI tools for hedge funds.

See what Kepler does with your coverage list at app.kepler.ai, or book a demo on your own documents.

Frequently asked questions

What is the best alternative to AlphaSense?

Kepler, for buy-side firms that want AI built around their own firm instead of a licensed library. Our engineers build your firm's ontology, Kepler reads every document in full before you ask, and code computes every number and cites it to the exact page, line or cell, so a figure in a model, an IC memo or an LP report holds up. Agents watch your coverage on schedules and new filings, and Kepler works in Excel, PowerPoint, Slack and the AI assistants your analysts already use.

What's the difference between AlphaSense and Bloomberg?

Bloomberg is a market data terminal first: real-time prices, analytics, news, Bloomberg Intelligence and research from more than 800 providers, with ASKB, generally available since October 2026, as its conversational AI. AlphaSense is a search platform built around a library of more than 500 million documents and is strongest on broker research and expert-call transcripts. Each is built around the content it licenses; for AI built around your own firm, with every number computed in code and cited to the line, our pick is Kepler.

Who are AlphaSense's main competitors?

In AI built around the firm itself, Kepler, where our engineers build the firm's ontology, read every document in full and compute every number in code. In market intelligence, Bloomberg (with its ASKB AI), FactSet and S&P Capital IQ Pro. In expert research, Third Bridge and AlphaSights. In AI for document-heavy work, Hebbia and Rogo. Frontier assistants such as Claude and ChatGPT now have finance editions too.

How much does AlphaSense cost?

AlphaSense doesn't publish prices. SpendHound reports average spend of $10,131 a year for smaller customers and $139,455 a year for enterprise customers, across 160 AlphaSense customers. Seats, expert-call usage and premium content drive the total. Vendr reports that contracts commonly auto-renew unless canceled 60 to 90 days ahead, so check your notice window before you trial an alternative.

Who owns Tegus now?

AlphaSense. It announced the acquisition in June 2024 and closed it in July 2024. Tegus brought its expert transcript library, Canalyst financial models and BamSEC. AlphaSense now cites 300,000 proprietary expert interviews in its library.

Can AlphaSense search my firm's internal documents?

Yes, through its Enterprise Intelligence offering, which connects SharePoint, Box, Google Drive, Egnyte and other systems, mirrors permissions, and accepts uploads and an ingestion API. We go further on the firm's own data: we read every document in full at ingest and resolve it into the firm's ontology, so funds, holdings and house definitions become records the AI reasons over, with every number computed in code and cited to the line.