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Analyst Studio
Secure the front door. Email is where most attacks arrive — Analyst Studio is ThoughtSpot’s code-first ‘creator space’ for data teams — a cloud SQL IDE, Python/R notebooks, schema browsing, profiling and prep. It’s whereMode’s code-first BI now lives, feeding the governed model that powers AI-BI (Spotter). Data-readiness for AI.
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How it’s rated
Full scoreboard ↓Quick answer
The Postman platform family
This page covers Analyst Studio — the code-first creator space for data teams. The rest of the ThoughtSpot platform:
Quick facts
30-second orientation- Product
- Analyst Studio — code-first space for data teams
- Vendor
- ThoughtSpot (founded 2012 · CEO Ketan Karkhanis)
- Launched
- January 2025 — the creator space for data teams
- Mode is here
- Mode Analytics (acq. 2023) folded into Analyst Studio
- What it does
- SQL IDE + Python/R notebooks + data prep + profiling
- 2026 additions
- Agentic Data Prep + SpotCache (fixed-cost AI caching)
- The role
- Data-readiness for AI — feeds the model that powers Spotter
- India
- Indian-origin founders · huge Bangalore R&D
- Vs
- Mode (standalone), Hex, Databricks notebooks, Sigma, Count
- In India via
- TechBag — scoping, licensing, local support, GST
Understand code-first BI (and where Mode went) before you buy it
Most product pages skip this. We start here — so you buy a capability, not a buzzword.
What is Analyst Studio?
ThoughtSpot’s code-first ‘creator space’ for data teams — a cloud SQL IDE, Python/R notebooks, schema browsing, profiling and prep. It’s where Mode Analytics now lives, and it feeds the governed model that powers AI-BI.
Scattered data-team tools vs one code-first workspace (Analyst Studio) — the honest table
What consolidation actually replaces, dimension by dimension.
| Dimension | Unprotected / signature email | Analyst Studio (Postman) |
|---|---|---|
| Where data teams work | Scattered scripts & local tools | One code-first cloud workspace |
| SQL | Separate SQL editor | Native cloud SQL IDE |
| Notebooks | Local Jupyter, hard to share | Python/R notebooks, in the workspace |
| Data prep | Ad-hoc, code-only | Prep + Agentic Data Prep (grid) |
| Mode | Standalone Mode product | Folded into Analyst Studio |
| AI cost | Runaway warehouse spend | SpotCache — fixed-cost AI caching |
| The payoff | Analysis in isolation | Feeds the governed model (Spotter) |
| Best fit | (varies) | Code-first data teams on the modern stack |
ThoughtSpot Analyst Studio is a code-first creator space for data teams — SQL IDE, Python/R notebooks, schema browsing, profiling and prep. It’s where Mode’s code-first BI now lives, feeding the governed model that powers AI-BI (Spotter). 2026 adds Agentic Data Prep & SpotCache. Honest: code-first (not business-user), cloud-DW-oriented, premium/quote-priced. Best standalone notebook? Hex. All-in on Databricks? Its notebooks. Grid analytics? Sigma. TechBag scopes it, explains the Mode move & adds GST.
The five pieces of the platform
Vendors love diagrams; buyers need to know what they’re actually operating. Here’s the whole platform, demystified.
Cloud SQL IDE
A native cloud SQL IDE — write, run and iterate on SQL against your cloud data warehouse right in the browser, with schema browsing and result exploration. No local setup. Just open a tab and query. The analyst's home base.
Python & R Notebooks
Full Python and R notebooks alongside SQL — for advanced analysis, statistics, and data-science work that goes beyond what SQL alone can do. Code where you need it. SQL and notebooks in one workspace.
Data Prep & Profiling
Visual data profiling and data-prep tooling — inspect distributions, spot quality issues, and shape data into analysis-ready form before it flows downstream. See the data, then clean it. Prep where the data lives.
Mode, Folded In
This is where the acquired MODE ANALYTICS capabilities now live — Mode's pioneering code-first / SQL-and-notebook BI, no longer a standalone brand but folded into Analyst Studio. Mode's code-first DNA, now part of ThoughtSpot. The legacy, evolved.
Feeds the Semantic Model
The prep and business-logic work done here feeds the GOVERNED semantic model that powers the AI-BI layer (Spotter) — so data teams' rigorous work makes business users' AI answers trustworthy. Code-first work feeds AI-BI. Data-readiness for AI.
One agent on every machine, one console over all of them — modules attach without a second operational world.
Twelve capabilities. Explore, prep, analyse.
Analyst Studio is the code-first workspace where data teams prep data & build the model that powers AI — the creator space of portfolio, and paired with the human firewall.
Native Cloud SQL IDE
A browser-based SQL IDE — write and run SQL against your cloud data warehouse, iterate fast, no local setup. The analyst's home base. Open a tab, start querying.
Schema Browsing
Browse tables, columns and relationships in your warehouse right in the workspace — so you understand the data before you query it. Know the schema, write better SQL. See what's there.
Visual Data Profiling
Profile your data visually — distributions, nulls, cardinality, quality issues — so you understand and validate the data before you build on it. See the shape of the data. Catch problems early.
Data Prep & Cleaning
Shape, clean and transform data into analysis-ready form — the essential prep work that makes everything downstream reliable. Clean data in, trusted answers out. Prep where the data lives.
Agentic Data Prep (2026)
A spreadsheet-style prep interface — shape data in a familiar grid without heavy coding, AI-assisted. Prep that feels like a spreadsheet. Fast, approachable data shaping.
Business-Logic Development
Develop and encode the business logic — metrics, definitions, transformations — that becomes the trusted foundation for analysis and, ultimately, the governed model. Define the truth once. Consistent everywhere.
Python & R Notebooks
Full Python and R notebooks alongside SQL — for statistics, data science and advanced analysis that goes beyond SQL. Code where you need it. The full analytical toolkit.
Code-First BI (Mode heritage)
The code-first / SQL-and-notebook BI that came from Mode — build analyses and reports in code, for teams who want control, not drag-and-drop. Mode's DNA, folded in. For people who write SQL.
SpotCache — Fixed-Cost AI Caching (2026)
SpotCache provides fixed-cost caching for AI workloads — so heavy, repetitive AI query patterns don't blow up warehouse spend. Predictable cost for AI. Cache the heavy queries.
Live on the Cloud Warehouse
Work live against your cloud data warehouse (Snowflake, Databricks, BigQuery) — no extracts, current data, warehouse-scale. Live, current, at scale. Always the real data.
Team Collaboration
Data analysts, engineers and scientists collaborate in one shared workspace — SQL, notebooks and prep together — rather than scattered scripts and local tools. One home for the data team. Shared, not siloed.
Data-Readiness for AI (feeds Spotter)
The prep and modelling done here feeds the governed semantic model that powers the AI-BI layer (Spotter) — so data teams' rigorous work makes business users' AI answers trustworthy. Code-first work, AI-ready. The bridge to Spotter.
Watch Analyst Studio in action
The overview, getting started, and protecting M365 email.
ThoughtSpot's Analyst Studio — Day in the Life Demo
A day in the life in Analyst Studio.
Product Spotlight: Spotter 3
The AI-BI layer Analyst Studio feeds.
Stop Searching. Start Knowing: Automated Insights with SpotIQ
Automated insights on top of prepped data.
Want a live, India-context walkthrough on your own fleet?
Book a guided demo →The endpoint catches what arrives. Email stops it arriving.
Here’s what genuinely sets ThoughtSpot Analyst Studio apart (and where a rival leads).
The code-first workspace for data teams — SQL, notebooks and prep in one place
The single biggest reason data teams choose Analyst Studio is that it's a real, native CODE-FIRST workspace — a cloud SQL IDE, Python and R notebooks, schema browsing, visual profiling and data prep, all in one place — built for the analysts, engineers and scientists who actually write code, not for drag-and-drop business users. The problem it solves: technical data teams live in a scatter of tools — a SQL editor here, local Jupyter notebooks there, ad-hoc scripts, a separate profiling tool, and dashboards somewhere else — with no shared home. Work is siloed, hard to hand off, and disconnected from the governed analytics the business actually consumes. What Analyst Studio provides: one native cloud workspace where the data team does its real work — write SQL against the warehouse in a proper IDE, drop into Python or R notebooks for advanced analysis, browse the schema, profile the data visually, and prep it into shape — all in the browser, live on the cloud warehouse. It's the 'creator space' for the people who build the analysis. Why it matters: giving data teams one code-first home means faster, more collaborative work, less tool-sprawl, and — critically — a direct line from the team's prep and business-logic work into the governed model the business consumes. It's the technical foundation under the whole analytics stack. The value: Analyst Studio is a native code-first workspace — SQL IDE, Python/R notebooks, schema browsing, profiling and prep in one place — for the data teams who write code. For technical analytics, this matters. TechBag helps data teams adopt Analyst Studio. TechBag helps your data team work in one place.
Mode's code-first BI, now folded into Analyst Studio
A defining fact about Analyst Studio is that it's where MODE ANALYTICS now lives — Mode (acquired by ThoughtSpot in 2023 for roughly $200M) was the pioneering code-first / SQL-and-notebook BI product, and it is no longer a standalone brand: its capabilities have been folded into Analyst Studio. The story: Mode built its reputation as the tool for code-first data teams — SQL plus Python/R notebooks plus report-building — the workspace data analysts and scientists reached for when drag-and-drop BI wasn't enough. When ThoughtSpot acquired Mode, it didn't keep it as a separate product forever; it consolidated Mode's code-first heritage into Analyst Studio, the creator space for data teams. What this means for buyers: if you knew and liked Mode, the honest positioning is that Mode's code-first BI is now part of ThoughtSpot's Analyst Studio — the same SQL-and-notebook, code-first DNA, but inside the broader ThoughtSpot platform (and feeding the governed model that powers the AI-BI layer). Existing Mode users and teams evaluating code-first BI should understand: the standalone 'Mode' brand is legacy; the go-forward home is Analyst Studio. Why it matters: this consolidation is exactly the kind of thing buyers need told honestly — you're not choosing 'Mode vs ThoughtSpot', you're recognising that Mode's code-first capabilities now come as Analyst Studio inside ThoughtSpot. The value: Mode's pioneering code-first / SQL-and-notebook BI is now folded into ThoughtSpot's Analyst Studio — the same code-first DNA, inside the broader platform. For anyone who knew Mode, this matters. TechBag explains the Mode-to-Analyst-Studio transition honestly. TechBag helps you understand where Mode went.
Data-readiness for AI — the prep that makes AI answers trustworthy
A core strategic value of Analyst Studio is its role in the ThoughtSpot platform: it's where data teams do the rigorous prep and business-logic work that feeds the GOVERNED semantic model — the model that then powers the business-user AI-BI layer (Spotter). Analyst Studio is data-readiness for AI. The problem it solves: AI analytics is only as good as the data and model beneath it. A business-user AI analyst that answers questions in plain language is worthless — dangerous, even — if the underlying data is dirty, the metrics undefined, and the business logic inconsistent. Someone has to do the un-glamorous prep and modelling work first. What Analyst Studio provides: the code-first place where analysts and engineers clean and shape data, profile it, and develop the business logic and metric definitions that become the governed semantic model. That model is what grounds the AI-BI layer's answers — so the technical team's careful work in Analyst Studio is precisely what makes business users' AI answers trustworthy rather than hallucinated. The division of labour: data teams do rigorous, code-first prep and modelling in Analyst Studio; business users get governed AI answers in Spotter. Two ends of one platform. Why it matters: in the AI era, 'data-readiness' is the bottleneck — organisations with AI ambitions discover their data isn't ready. Analyst Studio is where that readiness gets built, and its tight connection to the governed model (and thus to Spotter) is ThoughtSpot's edge over standalone notebook tools that stop at analysis and don't feed a governed AI layer. The value: Analyst Studio is data-readiness for AI — the code-first prep and modelling that feeds the governed model powering trustworthy AI answers. For AI-ready analytics, this matters. TechBag helps you build data-readiness for AI. TechBag connects your prep work to trustworthy AI.
Modern additions — Agentic Data Prep and SpotCache (fixed-cost AI)
Analyst Studio keeps evolving: 2026 brings Agentic Data Prep (a spreadsheet-style prep interface) and SpotCache (fixed-cost caching for AI workloads) — two additions that broaden who can prep data and make AI costs predictable. Agentic Data Prep: not everyone on a data team wants to write transformation code for every prep task. Agentic Data Prep provides a spreadsheet-style interface — shape data in a familiar grid, AI-assisted — so prep is faster and more approachable, while still landing in the same governed workflow. It broadens who can contribute to data-readiness. SpotCache: heavy AI query patterns — the kind that agentic analytics generates — can be repetitive and expensive on a consumption-priced cloud warehouse. SpotCache provides FIXED-COST caching for those AI workloads, so the cost of running AI on your data is predictable rather than a runaway warehouse bill. That's a real, practical concern as organisations scale AI analytics. Why it matters: these additions address two of the biggest friction points in scaling data-team work and AI analytics — approachability of prep (Agentic Data Prep opens it up beyond hardcore coders) and cost-control of AI (SpotCache makes AI spend predictable). Together they show Analyst Studio evolving toward being the practical, cost-aware foundation for AI-era analytics, not just a code editor. The value: Analyst Studio adds Agentic Data Prep (spreadsheet-style, approachable prep) and SpotCache (fixed-cost caching for AI workloads, predictable AI spend). For scaling AI analytics affordably, this matters. TechBag helps you adopt these capabilities. TechBag helps you prep faster and keep AI costs predictable.
An India-origin analytics leader — and TechBag adds the local layer
ThoughtSpot is an India-origin analytics leader — founded by Indian-origin engineers with enormous India R&D — and for Indian data teams TechBag adds the local scoping, licensing and INR/GST support that make adopting a premium platform practical. The India story: ThoughtSpot was co-founded by Ajeet Singh (also a Nutanix co-founder) and Amit Prakash (ex-Google, who worked on ML for Google Ads) — both Indian-origin — and it has MASSIVE India engineering: R&D centres in Bangalore (since 2017), Trivandrum and Hyderabad, plus an India Customer Center of Excellence. India isn't a support office; it's core product engineering. That's a genuine point of pride and relevance for Indian buyers, and it means strong local talent familiarity — especially relevant for a data-team tool, given India's deep pool of data engineers and analysts. Well-suited to Indian data teams: Analyst Studio fits the Snowflake/Databricks-first modern data stacks that large Indian enterprises and GCCs are adopting, and India's GCCs run huge data-engineering teams for whom a shared code-first workspace is directly valuable. Where TechBag adds value: ThoughtSpot is a premium, quote-priced platform (in USD) — so TechBag adds local scoping (which capabilities: Analyst Studio, Spotter, Analytics, Embedded, Modeling), honest comparison (vs Hex, Databricks notebooks, Sigma, and the legacy Mode standalone), onboarding and semantic-model help, INR/GST invoicing and local support. The value: ThoughtSpot is an India-origin analytics leader with huge India R&D — and TechBag adds local scoping, onboarding, INR/GST and support. TechBag supplies it with local support. TechBag provides ThoughtSpot, made local for India.
The honest scope
Analyst Studio is ThoughtSpot's code-first 'creator space' for DATA TEAMS — a native cloud SQL IDE, Python/R notebooks, schema browsing, visual profiling and data prep — where analysts, engineers and scientists prep data and build the business logic that feeds the governed semantic model powering the AI-BI layer (Spotter). It's where the acquired Mode Analytics capabilities now live (Mode, acquired 2023 ~$200M, is no longer a standalone brand). 2026 adds Agentic Data Prep and SpotCache. ThoughtSpot (founded 2012; CEO Ketan Karkhanis; a Leader in the 2026 Gartner MQ for Analytics & BI; Indian-origin, huge India R&D). The honest framing — strengths, and where rivals lead: Analyst Studio's strengths are being a real code-first workspace (SQL + notebooks + prep in one place), the Mode heritage, live cloud-warehouse work, and — uniquely — feeding a governed model that powers a trustworthy AI-BI layer (data-readiness for AI). The competitive landscape is real: Hex is a strong, modern notebook/SQL workspace with polished collaboration — a genuine rival for code-first data teams; Databricks notebooks are the natural choice for teams already all-in on Databricks (and part of that platform's gravity); Sigma offers spreadsheet-style cloud analytics that appeals to teams wanting a familiar grid; and Count is a collaborative data-notebook/canvas tool. And the legacy Mode standalone is the product this capability came from — existing Mode users should understand it's now Analyst Studio. Honest caveats: this is a CODE-FIRST tool for TECHNICAL data teams (not a business-user, drag-and-drop tool — that's Spotter/Analytics); it's cloud-DW-oriented (best if you already run Snowflake/Databricks/BigQuery); ThoughtSpot overall is PREMIUM and historically opaquely priced; and dedicated notebook rivals like Hex are strong and worth weighing on pure notebook experience. So the honest positioning: for a code-first data-team workspace that feeds a governed AI-BI layer on the modern data stack, Analyst Studio is compelling; for the polished standalone notebook experience, weigh Hex; if you're all-in on Databricks, its notebooks; for spreadsheet-style analytics, Sigma; and if you were a Mode user, know it now lives here. TechBag scopes Analyst Studio honestly — the right capabilities, the Mode transition explained, comparison vs Hex/Databricks/Sigma, and licensing and supporting it locally with GST.
The numbers behind the platform
What your ThoughtSpot journey looks like
Scoping (& capabilities)
Which capabilities — Analyst Studio (code-first space), plus Spotter (AI analyst), Analytics, Embedded, Modeling? — and your cloud warehouse (Snowflake/Databricks/BigQuery). If you were a Mode user, TechBag explains the Analyst Studio transition and compares vs Hex/Databricks/Sigma honestly.
Connect & set up the workspace
Connect Analyst Studio live to your cloud data warehouse, set up the SQL IDE, notebooks, schema browsing and profiling for your data team. Get the code-first workspace running.
Prep & build business logic
Data teams prep and profile data, develop business logic and metric definitions — with Agentic Data Prep opening prep up beyond coders — that feed the governed semantic model. Build data-readiness for AI.
Feed AI-BI & control cost
The prepped model powers Spotter for business users; use SpotCache to keep AI query costs predictable; refine the model. TechBag supports you locally (GST).
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The review scoreboard
Modelled on Gartner Peer Insights structure. *Counts and breakdowns are illustrative pending verified review collection.
Quick poll — what’s driving your evaluation?
Talk to an advisor“Analyst Studio finally gave our data team one home — SQL IDE, Python notebooks and prep in the browser, live on the warehouse. No more scattered local scripts and ad-hoc tools.”
“We were Mode users, so it mattered that Mode's code-first BI now lives in Analyst Studio. Same SQL-and-notebook DNA, but inside ThoughtSpot and feeding our governed model. TechBag explained the transition clearly.”
“The real value is the connection to the model: the prep and business logic we build here feed the governed semantic model that powers Spotter for the business. Data-readiness for AI, done in one place.”
“Agentic Data Prep opened prep up beyond our hardcore coders — a spreadsheet-style grid means more of the team can shape data. And SpotCache made our AI query costs predictable instead of a runaway bill.”
“Honest: we did trial Hex, which is a lovely standalone notebook. For us the deciding factor was that Analyst Studio feeds a governed AI-BI layer — not just analysis in isolation. TechBag gave us that honest comparison.”
“As a code-first tool it's genuinely for our engineers and scientists, not business users — which is exactly what we wanted for the prep and modelling layer. We use Spotter for the business side.”
“Running live on Snowflake with schema browsing and visual profiling right there means we validate data before we build. That prep discipline is what keeps the downstream AI answers trustworthy.”
“That ThoughtSpot is India-origin with huge Bangalore R&D gave our team confidence and local relevance — and India has deep data-engineering talent. TechBag scoped the capabilities and handled GST.”
Where everyone sits — the grids
Analyst firms bury this view behind paywalls, and G2 retired its Grid. So here’s TechBag’s synthesis of the Code-first BI & notebook market — tap any vendor to see why it sits where it does.
TechBag Email-Security Grid
Execution strength vs product vision — the classic market map, minus the paywall.
Code-first space, feeds AI-BI. This page.
Detection × Portfolio Integration
The grid nobody publishes — how strong the email detection is vs how integrated with the wider security portfolio.
Code-first + feeds governed model.
Positions are TechBag’s illustrative synthesis of public review-platform data and vendor documentation — not a reproduction of any analyst graphic. Verify before relying on it.
Analyst Studio vs the code-first / notebook field
Mode (standalone — now folded in here), Hex, Databricks notebooks, Sigma and Count — honest lanes; the edge is a code-first data-team workspace that FEEDS a governed AI-BI layer. Best standalone notebook? Hex. All-in on Databricks? Its notebooks. Grid analytics? Sigma. We say so.
| Dimension | ThoughtSpot | Mode (standalone) | Hex | Databricks notebooks | Sigma | Count |
|---|---|---|---|---|---|---|
| Position | Code-first space, feeds AI-BI | Legacy — now folded in here | Modern notebook/SQL workspace | Notebooks for Databricks teams | Spreadsheet-style cloud analytics | Collaborative data notebook/canvas |
| SQL IDE + notebooks | SQL IDE + Python/R notebooks | SQL + notebooks (legacy) | SQL + Python notebooks | Notebooks (SQL/Python/R/Scala) | SQL + spreadsheet formulas | SQL + notebook cells |
| Data prep & profiling | Prep + Agentic Data Prep + profiling | Basic (legacy) | In-notebook prep | Spark/DLT prep (technical) | Spreadsheet-style prep | Canvas prep |
| Feeds a governed AI-BI layer | Feeds model → Spotter (Spotter) | No | Standalone (no AI-BI layer) | Genie/AI-BI (Databricks) | Standalone | Standalone |
| For technical data teams | Code-first for analysts/engineers | Code-first (legacy) | Code-first, polished | Very technical (Spark) | Semi-technical (grid) | Code-first canvas |
| Modern-data-stack (live warehouse) | Live on Snowflake/Databricks/BigQuery | Live query (legacy) | Live on warehouses | Native to Databricks | Live on warehouses | Live on warehouses |
| AI-cost control | SpotCache — fixed-cost AI caching | N/A | Warehouse-dependent | Cluster/DBU cost mgmt | Warehouse-dependent | Warehouse-dependent |
| Best fit | Code-first data teams that feed a governed AI-BI layer | Existing Mode users — now migrate to Analyst Studio | Best standalone modern notebook | Teams all-in on Databricks | Spreadsheet-style cloud analytics | Collaborative data canvas |
Which email-security approach fits you?
Honest fit signals — because the fastest way to lose your trust is to pretend one product wins every scenario.
Choose ThoughtSpot / Analyst Studio if…
- ✓You want a CODE-FIRST workspace for data teams — SQL IDE, Python/R notebooks, schema browsing, profiling and prep in one place
- ✓You want the prep and modelling work to feed a GOVERNED semantic model that powers a trustworthy AI-BI layer (Spotter) — data-readiness for AI
- ✓You were a Mode user — Mode's code-first BI is now folded into Analyst Studio
- ✓You're on the modern data stack (Snowflake/Databricks/BigQuery) — with TechBag adding scoping, the Mode transition & GST
Mode (standalone) if…
- ✓You knew the standalone Mode — note it's legacy; the go-forward home is Analyst Studio inside ThoughtSpot
Hex if…
- ✓You want the most polished standalone modern notebook/SQL workspace, without a governed AI-BI layer
Databricks notebooks if…
- ✓Your team is all-in on Databricks and you want notebooks native to that platform (Spark, Genie)
Sigma / Count if…
- ✓You want spreadsheet-style cloud analytics (Sigma) or a collaborative data notebook/canvas (Count)
What do email threats cost you?
Drag the sliders (data-team headcount; hours per week on tool-switching & scattered prep; hour cost as loaded rate). Estimates contrast scattered data-team tools (separate SQL editor, local notebooks, ad-hoc scripts) vs ThoughtSpot Analyst Studio (one code-first workspace — SQL IDE, notebooks, prep, profiling — feeding a governed model) — the wins are less tool-sprawl, faster prep, and data-readiness for AI. NB: premium pricing — TechBag scopes your case. Illustrative.
Loaded cost = salary + overheads per productive hour. Illustrative only — your TechBag quote models actual device counts and modules.
Three ways to consume it
ThoughtSpot is PREMIUM and quote-priced, in USD — no simple public list (Analyst Studio is part of the platform; pricing trends toward consumption/credits, with SpotCache offering fixed-cost caching for AI workloads). Enterprise deployments commonly land well into six figures (third-party estimates cite ~$137K/yr average for the platform; treat as indicative). TechBag scopes the capabilities you need (Analyst Studio + others), explains the Mode transition, and quotes current figures with INR/GST.
ThoughtSpot (premium, by quote)
Best for code-first data teams feeding AI-BI
- Analyst Studio (SQL IDE + Python/R notebooks + prep + profiling)
- Mode’s code-first BI folded in + Agentic Data Prep + SpotCache
- Live on your cloud warehouse (Snowflake/Databricks/BigQuery) — quote-based, USD
+ Platform add-ons
Best for a broader rollout
- Scoped to your estate
- Add-on modules as needed
- Phased, right-sized deployment
+ model help & local support
Best value with TechBag
- The Mode-to-Analyst-Studio transition explained for former Mode users
- Honest Hex / Databricks notebooks / Sigma comparison + semantic-model help
- TechBag adds onboarding, INR/GST invoicing & local support
Buy it for less — TechBag pricing beats list
Whatever the list prices above, TechBag negotiates a significantly better deal — with GST-compliant INR invoicing and local support. Ask us for your discounted quote.
Get an India-ready quote
Tell us your device counts and current tools — we’ll model it against what you spend today.
The 8 questions to ask every vendor
Take this into your next vendor call — including ours.
Want one home for your data team? Analyst Studio is a cloud SQL IDE + Python/R notebooks + schema browsing + profiling + prep.
Were you a Mode user? Mode's code-first BI is now folded into Analyst Studio — TechBag explains the migration.
Want trustworthy AI answers? The prep and modelling here feed the governed model that powers the AI-BI layer (Spotter).
Want prep beyond coders? A spreadsheet-style prep interface (2026) opens data shaping up to more of the team.
Worried about AI query spend? SpotCache provides fixed-cost caching for AI workloads — predictable cost.
On Snowflake/Databricks/BigQuery? Analyst Studio works live on the warehouse — no extracts, current data.
Weighing Hex (polished notebook), Databricks notebooks, or Sigma (grid analytics)? TechBag compares honestly.
ThoughtSpot is India-origin with huge Bangalore R&D — TechBag adds local scoping, onboarding, INR/GST and support.
Questions buyers ask
Ready to give your data team a code-first home — that feeds AI?
Scope ThoughtSpot Analyst Studio (the code-first creator space for data teams — SQL IDE, Python/R notebooks, schema browsing, profiling and prep, where Mode’s code-first BI now lives) — and let a TechBag advisor scope the capabilities, explain the Mode transition, help build the semantic model it feeds (so Spotter’s answers are trusted), compare vs Hex/Databricks/Sigma honestly, and add INR/GST and local support.
Stats, ratings, review counts and pricing are illustrative and sourced from public materials; verify before purchase.