Daloopa vs Canalyst: Which Model Data Fits Your Team?

Daloopa and Canalyst both spare analysts from typing historicals into Excel, in opposite ways. Canalyst sells finished, drivable company models built by its own analysts; Daloopa sells source-linked data points that flow into the models you build yourself. For teams that want the historicals cited to the line plus their own definitions, documents and analysis on one platform, the third option on this page is Kepler.

Canalyst has been part of AlphaSense since AlphaSense bought Tegus in 2024. We build Kepler, so read the last section with that in mind. Facts about Daloopa and Canalyst come from their sites, documentation and public reporting as of November 2026, linked inline.

Daloopa vs Canalyst at a glance

	Daloopa	Canalyst	Kepler
What you get	Source-linked data points for your own models	4,000+ finished, drivable Excel models	Statements and KPIs cited to the line, on your firm’s definitions, plus the analysis on top
Coverage	6,000+ public companies	4,000+ models	14,000+ companies in 120+ countries
History	Up to 14 years	Back as far as 2009	Full filing history, as filed or as restated
Forecasts	None (historicals and guidance)	Five years of placeholder forecasts to edit	Your own forecasts and playbooks, applied every time
How numbers are captured	Automated tagging, then analyst review	Entered by sector analysts, then QA-checked	Statements from XBRL in code; KPIs read at ingest with scale and unit
Link to source	Every data point hyperlinked	Audit to source filing via the Premium add-in; source links in guidance tab and update log	Exact page, line or cell for every figure, formula for every calculation
Restatements	Original and restated values both kept	Not described in AlphaSense’s model guide; update log records changes	Either view on any date; recasts flagged
Your firm’s definitions	Your spreadsheet formulas	Your edits inside Canalyst’s structure	Built into your ontology, applied to every company
Excel	Add-in, data sheets, Scout agent	Model download, Modeling Tools add-in	Live formulas with cited inputs
AI tools	MCP connector for Claude, ChatGPT, Microsoft 365 Copilot	Inside AlphaSense’s platform	App, Claude, ChatGPT, Slack, API, MCP
Pricing	Not published; free tier with three data sheets	Through an AlphaSense contract	Through our team

Canalyst: the finished model

Canalyst built its name on complete company models. Its help guide describes 4,000+ pre-built models that are fully linked and drivable, with the hundreds or thousands of metrics a company reports in filings, earnings presentations, supplementals and press releases. Sector-focused analysts source each historical from primary documents and run it through tech-enabled QA checks. Every model follows the same layout: segments, operating metrics and margins at the top, the full three statements as reported below, historicals to the left of a black line and forecasts to the right. History reaches back as far as 2009, and forecasts run five fiscal years out as placeholders the user is meant to edit, tuned to guidance or consensus where possible.

The models come with useful tabs: a guidance tab comparing actuals to what management guided, with a link to the source file; an update log showing every revision and the documents behind it; and a front page where you can pull real-time prices and consensus through your Bloomberg, Capital IQ, FactSet or Eikon add-in. Premium Models subscribers also get the Canalyst Modeling Tools Excel add-in, which updates a customized model in one click, audits model data to the source filing and drops in DCF and bull-bear-base templates.

Ownership has changed twice. Tegus bought Canalyst in August 2022, AlphaSense bought Tegus in 2024, and the models now live in AlphaSense’s Industry Comps and Models module, next to its financial data tools.

Strongest at: a complete, consistently structured model you can drive on day one.

Daloopa: the source-linked data layer

Daloopa starts from the data point instead of the model. It covers 6,000+ public companies with up to 14 years of history, and each value is hyperlinked to its source document. According to its documentation, an automated tagging pipeline captures values soon after a filing or earnings release, and an analyst-reviewed capture follows. When a company restates, Daloopa keeps both the original and the restated value, and a taxonomy maps each company’s own series names to standard metrics for peer comparison.

The data goes where analysts work: data sheets for a full ticker history, an Excel add-in that connects an existing model and refreshes it after a print, and Scout, an Excel agent that became generally available in September 2026 to build models or add a new quarter. Builders get an API, cloud delivery to Snowflake, Databricks and S3, and an MCP connector for Claude, ChatGPT and Microsoft 365 Copilot. The plans page lists a free tier with up to three data sheets and paid plans without published prices.

Strongest at: source-linked inputs flowing into models your team already owns, and into AI tools.

Head to head

Coverage and history. Daloopa covers more companies (6,000+ against 4,000+ models) and up to 14 years. Canalyst’s history reaches back to 2009 for many models, and every model includes forecasts, which Daloopa doesn’t provide.

Structure. Canalyst hands you its structure; Daloopa fits into yours. A Canalyst model is ready to drive, but AlphaSense’s guide warns against deleting Canalyst rows, columns or worksheets if you want the updater to keep working. Daloopa’s add-in connects to the model you already have, so the structure, and every formula in it, stays your team’s to maintain.

Link to source. Both tie numbers back to documents. Daloopa links every data point by default. Canalyst’s audit-to-source feature sits in the Premium add-in, and its guidance tab and update log carry source links.

Restatements. Daloopa’s data model keeps original and restated values side by side. AlphaSense’s model guide doesn’t describe restatement handling; the update log records what changed in each update.

Excel and AI. Canalyst is an Excel-first product inside AlphaSense’s platform. Daloopa reaches Excel, an API, cloud warehouses and the major AI assistants through MCP.

Pricing. Neither publishes prices. Canalyst comes through an AlphaSense contract; SpendHound reports average AlphaSense spend of $10,131 a year for smaller customers and $139,455 for enterprise customers. Daloopa sells Core, Premium and Fundamentals API plans through sales, with a free tier.

Who each one is for

Canalyst fits analysts who want a complete, consistently laid-out model on day one, with forecasts to drive and a guidance history, and who already use or plan to use AlphaSense.

Daloopa fits analysts and quant or data teams who keep their own models and templates and want source-linked inputs refreshed after each print, or a fundamentals feed for their own AI tools.

The third option: Kepler

We build Kepler.

Both products answer one question well: what did the company report? Teams still need to apply their own definitions, check the footnotes, rebuild histories when a company recasts its segments, and write the note. We built Kepler so all of that runs on one platform, built around your firm.

Statements assemble in code from each filer’s own XBRL, with no model in the path, as originally filed or as restated on any date. In our paper Reclassified to Conform, 194 of 200 companies reshaped a statement in ways XBRL tags can’t follow, so we rebuild each history to keep every line’s meaning fixed across years and flag every recast. KPIs and segment data in MD&A tables, footnotes and earnings decks are read at ingest by extraction models we train, each stored with its value, scale, unit and passage. Every cell opens the line it came from; every derived figure opens its formula and inputs. Coverage spans 14,000+ companies in 120+ countries, from 26M+ SEC filings and 40M+ IR documents and transcripts.

Our engineers built this layer before at Palantir, and on site with your team they build your firm’s ontology: your coverage lists, your KPI names, your adjusted EBITDA and your templates, applied to every company. Your own models and notes connect read-only and sit beside the filings. Agents update models and comps the morning a company files and send what changed, with sources, to Excel, PowerPoint, Slack, Claude or ChatGPT, and your engineers can build on the same engine through our API and MCP server. One permission check governs every read, every query and calculation is logged with its lineage, and we hold SOC 2 Type II.

Questions model-heavy teams bring to Kepler

• “Build Snowflake’s three statements and key KPIs for the last 16 quarters from its filings, every cell cited, with restated periods flagged.”
• “Map every company in our medtech coverage to our segment definitions and show where a recast broke the history.”
• “When each name in this list files, roll the model forward a quarter and send me what changed against our estimates.”

Not the right fit if: you’re one analyst who only wants a downloadable model template, with no need for firm-wide definitions, your own documents or agents.

See Kepler vs Daloopa for the full comparison, or Tegus alternatives for the rest of what Tegus covered.

How to decide

1. Decide who owns the structure. If you want a finished model, Canalyst. If your team owns its templates, Daloopa or Kepler.
2. Count your coverage. Check how many of your names each one covers, including small caps and non-US listings.
3. Test a recast. Pick a company that reorganized its segments, ask each tool for the history as originally filed and as restated, and click through to the source.
4. Test your definition. Ask for one adjusted metric on your firm’s definition across your peer set, and see where the formula lives. Our guide to checking AI numbers lists the full set of tests, and Daloopa alternatives and AlphaSense alternatives cover more options.

Build a company you model in Kepler at app.kepler.ai and click any cell, or book a demo on your coverage list.

Frequently asked questions

What is the difference between Daloopa and Canalyst?

Canalyst sells finished company models: 4,000+ fully linked, drivable Excel models built and updated by sector analysts, with history back as far as 2009 and placeholder forecasts five years out. Daloopa sells source-linked data points: fundamentals and KPIs on 6,000+ public companies, up to 14 years back, each hyperlinked to its source, delivered into your own models, an API or AI assistants.

Who owns Canalyst now?

AlphaSense. Tegus bought Canalyst in August 2022, and AlphaSense acquired Tegus in 2024. Canalyst models are now sold inside AlphaSense's Industry Comps and Models module, and Premium Models subscribers get the Canalyst Modeling Tools Excel add-in, which updates a customized model in one click and audits model data to the source filing.

Which covers more companies, Daloopa or Canalyst?

Daloopa says it covers 6,000+ public companies globally with up to 14 years of history. Canalyst offers 4,000+ pre-built models, with historical data going back as far as 2009 and forecasts five fiscal years ahead that are meant to be edited. Kepler covers 14,000+ companies in 120+ countries, with statements built from each filer's XBRL.

How does each one link numbers to the source?

Every Daloopa data point carries a hyperlink to the filing or presentation it came from. Canalyst's Modeling Tools add-in lets Premium Models subscribers audit model data to the source filing, its guidance tab links each guided metric to the file it came from, and its update log links the documents behind each update. Kepler opens the exact page, line or cell for every figure, with the formula for anything calculated.

Is there an alternative to both Daloopa and Canalyst?

Yes. Kepler, which we build, assembles statements from each filer's XBRL with every cell cited, as filed or as restated, reads KPIs from MD&A tables, footnotes and decks, and applies your firm's own definitions through an ontology our engineers build with you. Agents update your models and comps when a company files, in Excel, Claude, ChatGPT or the Kepler app.