Dataglade
AI market intelligence terminal for institutional investors, with source-linked fundamentals and cited AI research.
Dataglade is the strongest fit for buy-side and sell-side analysts who need defensible numbers, not plausible ones — click-through source links on every fundamental and citation-backed GladeAI answers are the differentiators, and they matter more in finance than chat fluency. Analyst estimate tracking across 100+ institutions and live options flow plus insider trades round out a real research workflow. Compare against Bloomberg Terminal when you need decades of market plumbing and a keyboard-driven workflow, and against AlphaSense when transcript and document search is the core job rather than screenable fundamentals. Pricing is quote-based through sales, so bring a defined seat count and a
Verified 1d ago · liveness 54/100 · cite: rightaichoice.com/tools/dataglade
- Hedge fund analysts
- Wealth managers
- Investment bankers
- Consulting firms
- Individual hobby investors
- Basic portfolio tracking
- Non-financial professionals
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Skip Dataglade if you are a solo investor who wants a low-cost screener or portfolio tracker, rather than an analyst workflow that includes live options flow, insider trades, and source-linked fundamentals.
Pricing is negotiated with sales rather than listed, so the number depends on seat count, coverage, and data entitlements — the initial quote is rarely the final invoice.
Budget for enterprise data-terminal money, not SaaS-seat money: pricing is arranged through sales and scales with seats, coverage, and entitled feeds. That puts it in the same bracket as established professional data terminals and above AI research tools you can buy on a card. The value case is strongest for firms already paying separately for analyst-estimate data and options-flow feeds — consolidating those is where the cost math works.
In short
Dataglade — AI market intelligence terminal for institutional investors, with source-linked fundamentals and cited AI research. Best for Hedge fund analysts, Wealth managers, Investment bankers. Contact Sales pricing.
Viability Score
How well maintained and how widely used is Dataglade? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Natural language querying over financial data (GladeAI)
- Fundamentals, ratios, and multiples for 48,000+ companies
- Comparable company analysis across comp sets
- Normalized vs. as-reported value toggle
- Source-linked fundamentals (click any number to view its source)
- Live options flow monitoring
- Real-time insider trade tracking
- Earnings results compared against market expectations
- Earnings call summaries and transcript search
- KPI extraction from earnings calls
- Real-time analyst estimates, price targets, and revisions from 100+ institutions
- Supply chain analysis via AI
- SWOT, MECE, and BCG analysis generation
- Stock screening with 100+ criteria
- Link internal data for proprietary AI insights
About Dataglade
Dataglade is a market intelligence platform built for hedge funds, wealth managers, investment banks, consultancies, and fintechs. It covers fundamentals, ratios, multiples, comparables, earnings, and analyst estimates for 48,000+ companies across 50+ markets and asset classes, alongside live options flow and real-time insider trades. Its AI layer, GladeAI, answers natural-language questions such as "Trace NVIDIA's supply chain, and explain risk factors it faces" or "What legal battles is Boeing currently engaged in?", and generates structured outputs including SWOT, MECE, and BCG analyses with citations to earnings calls, financial filings, and real-time news. Two things separate it from a generic chatbot: every fundamental number links to its source so you can validate it yourself, and you can securely link your own internal data so the AI answers against proprietary datasets. Analyst estimate tracking pulls revisions, price targets, and recommendations from Goldman Sachs, J.P. Morgan, Morgan Stanley, and 100+ other institutions. Screening covers 100+ criteria spanning market data, fundamentals, ratios, and multiples. Delivery is via the terminal, an API, and enterprise deployment; the vendor states it is trusted by firms trading $1+ billion daily.
Behind the Verdict
Dataglade sits in the gap between a classic data terminal and a general-purpose AI assistant. The classic terminal gives you clean data but forces you to assemble the narrative yourself; a chatbot gives you narrative but no way to check whether the numbers are real. Dataglade does both, and its answer is the source link: click any fundamental on the platform and you land on where it came from. GladeAI's company deep-dives — supplier relationships, industry trends, competitive pressures, management commentary — carry citations to earnings calls, financial filings, and real-time news, which is the only way an analyst can put AI output in front of a PM. The workflow coverage is broader than the marketing copy suggests. Earnings results land with market expectations side by side, KPIs discussed on each call are extracted, and call summaries plus transcript search sit next to the numbers. Analyst estimate tracking surfaces revisions, price targets, and recommendations from Goldman Sachs, J.P. Morgan, Morgan Stanley and 100+ other institutions as they are released. On the market-structure side you get live options flow and real-time insider trades — the two signals analysts usually buy from a separate vendor. Screening at 100+ criteria with comp-set ratio comparison is the workhorse feature, and the analyses layer (SWOT, MECE, BCG) turns a prompt into a formatted document rather than a wall of text. The genuinely differentiated capability is linking your own internal data. Being able to query proprietary holdings, models, or notes alongside public fundamentals in one place is what an in-house data team would otherwise spend a quarter building. Where it does not fit: coverage claims stop at 48,000+ companies and 50+ markets, so if your mandate is a thin frontier market, verify that market is actually in scope before you commit. Price is quote-based, so the total cost depends on seats, coverage, and data entitlements negotiated with sales — budget accordingly. And the platform assumes financial literacy; there is no hand-holding for a generalist who wants a plain-English stock pick.
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Real-world workflow fit
Concrete scenarios for the personas Dataglade actually fits — and what changes day-one when you adopt it.
Earnings season: pull Apple's latest results against market expectations, read the call summary and KPIs, then ask GladeAI to trace NVIDIA's supply chain risk factors with citations before writing the morning note.
Outcome: A sourced, defensible note in the time it previously took to assemble the raw numbers.
Screen for large-cap stocks with low multiples, compare ratios across the resulting comp set, and check analyst estimate revisions from the tracked institutions before a client review.
Outcome: A repeatable, criteria-driven shortlist with numbers you can show the client and defend.
Connect to the API and pull fundamentals, ratios, and multiples for portfolio companies into an internal research or client-facing product.
Outcome: Production financial data behind your own UI without building a data pipeline from scratch.
Use Cases
- Ask "What were the positives and negatives of Apple's last quarter?" and get a sourced rundown.
- Trace NVIDIA's supply chain and get risk factors it faces, with citations.
- Compare ratios and multiples across a peer set of REITs in one table.
- Monitor live options flow and insider trades for market-moving activity.
- Pull analyst estimate revisions, price targets, and recommendations as they are released.
- Link internal holdings or research data and query it alongside public fundamentals.
- Validate a fundamental number by clicking through to its original source.
- Screen for large-cap stocks with low multiples or small-caps with rapid revenue growth.
Limitations
- Dataglade is built for professional investors, so the platform assumes financial literacy and a real research workflow — it is not a plain-English stock picker for beginners.
- Pricing is quote-based and arranged through sales rather than published as self-serve tiers, which means your total cost depends on seats, coverage, and data entitlements you negotiate; get those in writing before rollout.
- Coverage is stated at 48,000+ companies across 50+ markets and 30+ asset classes, so confirm your specific mandate and any frontier markets are in scope during the demo.
- The AI analyses are citation-backed, but you still own the investment judgment — treat SWOT, MECE, and BCG outputs as a first draft, not a finished memo.
as of 2026-10-07
Verification history
We have re-verified Dataglade 9 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
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Free to cite with attribution — this page re-verifies continuously.
Where the pricing makes sense
The company stage and team size where Dataglade's pricing actually pencils out — and where peers do it cheaper.
Budget for enterprise data-terminal money, not SaaS-seat money: pricing is arranged through sales and scales with seats, coverage, and entitled feeds. That puts it in the same bracket as established professional data terminals and above AI research tools you can buy on a card. The value case is strongest for firms already paying separately for analyst-estimate data and options-flow feeds — consolidating those is where the cost math works.
Setup time & first value
How long it actually takes to get something useful out of Dataglade — broken out by persona, not the marketing-page minute.
Expect the fastest path to value on a terminal seat, where core workflows are available as soon as your account and entitlements are provisioned. The API path takes longer because it depends on your application and data mapping. Linking internal data to the AI for proprietary insights is the heaviest lift and should be scoped with the vendor during onboarding rather than assumed to be same-day.
Switching to or from Dataglade
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Bloomberg Terminal: move screening, comp-set ratio work, and question-driven research to Dataglade, keeping Bloomberg for the market plumbing you still need.
- →From AlphaSense: keep deep document search there if it is core, and bring screenable fundamentals plus citation-backed analyses into Dataglade.
- →From a spreadsheet-plus-chatbot workflow: replace manual fundamentals lookups with source-linked numbers and GladeAI queries that cite earnings calls and filings.
- →From a standalone options-flow or insider-trade feed: consolidate live options flow and real-time insider trades into the same research surface.
- ↗To Bloomberg Terminal: move back if you need decades of market plumbing and keyboard-driven workflow breadth Dataglade does not claim.
- ↗To AlphaSense: move if transcript and document search is your primary job rather than screenable fundamentals.
- ↗To an in-house data warehouse plus an LLM: move if your team already maintains the pipeline and only wants the AI layer.
Resources & Guides
Tutorials & Learning
YouTube returned 3 videos for “Dataglade”, and we withheld 3: 3 could not be judged, because “Dataglade” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Dataglade.
Official links
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Featured Head-to-Head Comparisons
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