SIGNLD vs ThoughtSpot: search on a model vs reasoning on a graph

Q: Which one costs less for a mid-market company?

ThoughtSpot's entry tier has historically started near $1,250 per month with higher tiers quoted individually, plus warehouse compute charged separately since queries run live. SIGNLD's current plans, including a free tier, are listed on /pricing and do not add a separate compute bill. ThoughtSpot's worksheet model and SIGNLD's automatically resolved graph solve related but distinct problems for different buyers inside the same company. Related reading in this series: SIGNLD vs AtScale: cubes, semantic layers, and the question nobody can answer and SIGNLD vs Databricks: Unity Catalog is a catalog, not a Knowledge Graph.

ThoughtSpot lets a user search a semantic model built from worksheets, with joins and column metadata someone defined in advance, then returns a chart. SIGNLD skips the worksheet: it resolves entities across connected systems into a Knowledge Graph and returns a ranked, traceable answer instead of a table to interpret.

TL;DR

ThoughtSpot searches a semantic model built from worksheets, joins, and column metadata that a data team defines against a live warehouse connection. SIGNLD reasons across a Knowledge Graph that resolves entities automatically from 800+ connected systems, with no worksheet or join to define. Choose ThoughtSpot when you have a governed warehouse model and want fast search across it. Choose SIGNLD when the model does not exist yet and the question spans systems the warehouse never absorbed.

For the wider context, see our roundup of the best business knowledge graph platforms in 2026.

What is ThoughtSpot actually built for?

ThoughtSpot is a search and AI analytics platform that runs live queries against a connected warehouse, such as Snowflake, BigQuery, or Databricks. Its core unit is the worksheet: a data team defines tables, joins between them, column synonyms, and metric formulas, and that worksheet becomes the searchable surface. A user types a question, ThoughtSpot maps it onto the worksheet, and returns a chart or Liveboard.

Spotter is ThoughtSpot's agentic layer, adding multi-turn conversational analysis on top of the same modelled worksheets. Spotter's answer quality is bound by the worksheet underneath it: if a join is missing or a synonym is undefined, the question either fails or returns the wrong table. Pricing is partly public, with an entry tier historically priced near ,250 per month for a bounded seat count, and Pro and Enterprise tiers priced by quote based on data volume, seats, and embedded usage.

The semantic model is the product's central asset. It is reusable across every user who searches the same worksheet, which is exactly why building and maintaining it correctly matters so much to whether ThoughtSpot performs well.

What is SIGNLD actually built for?

SIGNLD is a decision intelligence platform from Inzata Analytics. It connects read-only to the systems a company runs, including ERP, CRM, and the spreadsheets an operations team keeps outside any warehouse, and unifies them into a private Knowledge Graph. Entity resolution happens automatically: the same customer, job, or invoice gets linked across systems without anyone defining a join.

A leader asks a plain-language question and receives a Decision Brief: the finding, evidence linked back to source records, a confidence score, and a recommended action. Inference runs on a single-tenant AWS Bedrock instance. There is no warehouse requirement and no worksheet to build; the first system connects in 15 minutes and the first answer follows in minutes.

SIGNLD is built for a CEO, CFO, or COO running 10 to 40 or more siloed systems with no dedicated data team, not for a data engineer curating a searchable model.

Where the two genuinely differ

ThoughtSpot's unit of work is a worksheet: tables joined together, with metric definitions and synonyms that a data team maintains over time. Search quality is a direct function of that worksheet's completeness. A well-curated worksheet makes ThoughtSpot fast and precise; a stale one produces confident wrong answers, because the search index still matches a question to whatever the worksheet contains.

SIGNLD's unit is a node and edge in a Knowledge Graph, resolved from raw system data without a person writing the join first. This changes what happens when a question spans systems nobody thought to connect ahead of time. ThoughtSpot requires the worksheet to already include both tables and the join between them. SIGNLD resolves the connection as part of ingesting the second system, so the question does not wait on a modelling backlog.

The two also differ in what a returned answer looks like. ThoughtSpot returns a chart or table matched to your question, which a person still has to read and interpret against what they already know. SIGNLD returns a Decision Brief with a ranked finding, cited evidence, and a confidence signal, so the interpretation step is partly done before the answer arrives.

A few differences worth listing directly:

ThoughtSpot searches a modelled worksheet with defined joins; SIGNLD reasons across a graph with entities resolved automatically.
ThoughtSpot's output is a chart or Liveboard; SIGNLD's output is a Decision Brief with source citations.
ThoughtSpot requires a live warehouse connection; SIGNLD works without a warehouse and treats spreadsheets as first-class sources.
ThoughtSpot's Spotter reasons within one modelled dataset; SIGNLD's private LLM reasons across everything connected to the graph.
ThoughtSpot rewards a data team that curates the model continuously; SIGNLD is operated directly by the person asking the question.

	ThoughtSpot	SIGNLD
what it models	a semantic model of worksheets, joins, and column metadata over a live warehouse connection	business entities, metrics, and their relationships resolved automatically as systems connect
who builds it	an analyst or analytics engineer defining joins, synonyms, and column properties	no one authors it, the graph forms from the read-only connections you approve
time to first cross-system answer	weeks, because every source must land in the warehouse and be joined in the model first	minutes after the relevant systems connect
source traceability	drills from a search result to the underlying warehouse rows in the model	citations back to the source record in the originating system, with a confidence signal
where inference runs	ThoughtSpot's hosted service with Spotter over commercial model providers	a single-tenant AWS Bedrock instance, private LLM powered by AWS Bedrock, never trained on your data
who it is for	teams with a governed warehouse and analysts who maintain the model	CEOs, CFOs, and COOs who need cross-system answers without a warehouse project

Who has to build the model?

In ThoughtSpot, a data team or analyst defines each worksheet: which tables it draws from, how they join, what a metric like gross margin actually sums and filters, and which synonyms map to which columns so search understands informal phrasing. New data sources need new worksheets or extended joins before they are searchable, and that work continues as the warehouse schema evolves.

In SIGNLD, no one builds a worksheet. Connecting a system triggers automatic entity resolution across the graph, matching records that refer to the same real-world thing. A leader still reviews the evidence behind a brief before acting on it, but the ongoing curation labor ThoughtSpot assigns to a data team is not a separate, recurring project in SIGNLD.

How long until you get a cross-system answer?

In ThoughtSpot, time to a first cross-system answer depends on whether the relevant tables are already joined in a worksheet. If the join exists, search across it is close to instant. If it does not, someone has to define the join, test it, and often add synonyms before a business user's question resolves correctly, which commonly takes days to weeks depending on the data team's backlog.

In SIGNLD, the first system connects in 15 minutes and the first answer follows in minutes, because entity resolution runs automatically as each system's data arrives. A question spanning three systems that were never joined in any warehouse, such as which clients are drifting based on support tickets, invoices, and usage logs together, does not wait on a modelling ticket.

Where ThoughtSpot is the better choice

ThoughtSpot is genuinely better at fast, ad hoc search over a governed warehouse model at scale. Once a worksheet is well curated, hundreds or thousands of business users can pivot, drill, and re-slice at conversational speed, with numbers that match what the data team already publishes because the query runs live against the same governed source. Liveboards and drilldown are mature, fast, and built for exactly this kind of broad self-service.

If your organization already has a data team maintaining clean warehouse models and a large population of users who need to explore that data themselves, ThoughtSpot converts that investment into reach better than a decision intelligence platform does. SIGNLD is not built for thousands of concurrent self-service users slicing a governed dataset; it is built for a smaller leadership and operations group asking cross-system decision questions.

What running both looks like

Some organizations run ThoughtSpot for broad self-service search inside a governed warehouse and SIGNLD for the questions that span systems the warehouse never absorbed, such as operational spreadsheets or a newly acquired subsidiary's separate CRM. ThoughtSpot owns the worksheet-backed exploration; SIGNLD owns the answer that needs evidence from systems outside that worksheet.

Related reading: Decision Brief in the concepts glossary, What is a knowledge graph for business, SIGNLD vs Tableau, SIGNLD vs Power BI, and the difference between a dashboard and a decision go further into the surrounding ideas. That distinction matters most when a mid-market company has neither team yet and has to decide which gap to close first.

Your data stays yours in SIGNLD through read-only connections, a single-tenant AWS Bedrock instance for the private LLM, and a policy of never training on customer data. See /security and the connectors list for what can be connected, and /concepts for how the Knowledge Graph and Topic model relate.

Key takeaways

ThoughtSpot searches a semantic model built from worksheets, joins, and column metadata that a data team defines against a live warehouse connection.
ThoughtSpot's unit of work is a worksheet: tables joined together, with metric definitions and synonyms that a data team maintains over time.
ThoughtSpot is genuinely better at fast, ad hoc search over a governed warehouse model at scale.
Only if those systems are first loaded into the warehouse and joined into a worksheet.
If the model you would need in ThoughtSpot does not exist yet and the question cannot wait for a modelling backlog, start with the graph that builds itself.

FAQ

Is ThoughtSpot a semantic layer or a BI tool?

Both. ThoughtSpot's worksheets function as a semantic layer, defining joins, metrics, and synonyms over a warehouse, and its search interface and Liveboards are the BI layer built on top of that model. The two are tightly coupled: search quality depends directly on worksheet quality.

Does SIGNLD need worksheets or joins defined manually?

No. SIGNLD resolves entities automatically across connected systems as part of building its Knowledge Graph. There is no worksheet, join, or synonym list for a person to create or maintain before asking a question.

Can ThoughtSpot answer questions across systems outside the warehouse?

Only if those systems are first loaded into the warehouse and joined into a worksheet. ThoughtSpot's live query architecture is warehouse-native, so a spreadsheet or a system not yet ingested is invisible to search until someone adds it.

How is Spotter different from a SIGNLD Decision Brief?

Spotter reasons conversationally within one modelled warehouse dataset and returns analysis in response to follow-up questions. A Decision Brief spans every connected system in the graph and attaches source citations and a confidence score to each claim, prioritizing traceability over conversational depth.

Which one costs less for a mid-market company?

ThoughtSpot's entry tier has historically started near ,250 per month with higher tiers quoted individually, plus warehouse compute charged separately since queries run live. SIGNLD's current plans, including a free tier, are listed on /pricing and do not add a separate compute bill.

ThoughtSpot's worksheet model and SIGNLD's automatically resolved graph solve related but distinct problems for different buyers inside the same company.

Try SIGNLD free

If the model you would need in ThoughtSpot does not exist yet and the question cannot wait for a modelling backlog, start with the graph that builds itself. Try SIGNLD free and connect your first system today, or Browse all articles for more comparisons.