Text to SQL Solution cost in 2026: what you actually pay

A production text to SQL solution costs $12,500 to $38,500, or ₹8,00,000 to ₹25,60,000, to build with us, plus your own token spend and a care plan from $1,000 or ₹68,000 a month. What moves a project along that range is schema complexity, how much of a semantic layer already exists, and whether answers must respect row-level permissions.

This article breaks the budget into the parts a quote is actually made of, shows what each scope tier buys, sets out the running cost that arrives after go-live, and names the situations where the cheapest correct answer is not to build at all.

What you are buying, and what you are not

A text to SQL solution translates a business question into a query against your warehouse, runs it, and returns a number or a chart with the query visible. It is not a chatbot bolted onto a dashboard, and the language model is the least expensive part of it. Most of the money goes into everything that stands between a question and a trustworthy answer.

Four components carry the cost. A semantic layer that defines what revenue, active customer and churn actually mean in your tables. A retrieval step that selects the right tables and columns from a schema too large to fit in a prompt. A safety layer that enforces read-only access and row-level filters. And an evaluation suite of real questions with known-correct SQL, which is the only way anyone will believe the numbers. Why text-to-SQL needs a semantic layer explains why the first of these is not optional.

You are not buying a replacement for your BI tool. You are buying the ability to answer the questions that never made it into a dashboard, which is where most analyst time actually goes. The value is in the tail of one-off questions, not in the twenty charts that already exist.

What does each scope tier cost?

The short answer: a single-warehouse pilot sits near the bottom of the range, a multi-source governed deployment near the top. The table below is how we scope natural language data querying engagements.

What moves the price

Two companies with the same warehouse size can receive quotes that differ by a factor of two. These are the variables that explain it.

• Schema hygiene. Twenty documented tables with consistent keys is a different project from four hundred tables where three columns are called status.
• Existing semantic definitions. A maintained dbt or metrics layer removes weeks. Definitions that live in an analyst's head add them.
• Row-level security. If a regional manager must see only their region, permissions have to be enforced in the query, not in the prompt; see row-level security for AI analytics.
• Number of source systems. One warehouse is straightforward. A warehouse plus a production Postgres plus a MongoDB cluster is three dialects and three access models.
• Surface. A web console is the cheapest. Slack, Teams or an embedded panel inside your own product each add integration and permission work.
• Accuracy bar. Internal exploration tolerates a wrong query the user can see. A number that goes into a board pack or a regulatory return needs a far larger eval set and an approval path.
• Charting and follow-ups. Returning a table is cheap; returning a correct chart and supporting a follow-up question that refines the previous one is a product, not a feature.

What does it cost to run?

Running cost has three parts and only one of them is the model. First, token spend: you pay this through your own provider accounts, and published per-million-token rates such as OpenAI's pricing documentation are the basis of any forecast. A question that goes through schema retrieval, generation, a repair attempt and a short explanation typically costs a few cents; our LLM inference cost calculator turns your expected question volume into a monthly figure, and how to forecast your monthly bill covers the method.

Second, warehouse compute. This one surprises people. Natural language invites questions nobody would have bothered to write by hand, and a badly shaped query can scan far more than a dashboard ever did. Query cost guards, row limits and result caching belong in the build, not in a follow-up project. On a usage-priced warehouse this line can exceed model spend within a quarter of launch, and it is almost never in the quote you were given.

Third, care. Schemas change, definitions get revised and models are deprecated. Care plans start at $1,000 or ₹68,000 a month for business-hours support in IST, $2,500 or ₹1,60,000 for 24x5 with a four-hour response, and $5,250 or ₹3,40,000 for 24x7 with a named engineer. The AI add-on at $750 or ₹40,000 covers evals, cost monitoring and prompt regression, which is exactly the work this system needs; maintenance and support describes what each tier includes. Starting prices for every programme are listed on the pricing page.

Build, buy or extend what you have

Several BI vendors now ship a natural language feature, and if your data already lives in one governed model, that feature is the cheapest option by a wide margin. Our Eazy Insights AI product sits in the same space for teams who want governed answers without a build. A custom engagement earns its price when answers must span systems the BI tool does not own, when permissions are more granular than the tool supports, or when the querying has to live inside your own product rather than in a separate console.

The comparison that matters is not licence versus build price. It is the three-year total: licences scale per seat, a build is a fixed price plus running cost, and the crossover usually arrives sooner than finance expects.

When this is the wrong purchase

If two analysts serve the whole company and the question queue is a day long, a text to SQL solution will not repay $12,500. Hire, or buy the analysts better tooling. If your definitions are contested, meaning finance and growth report different revenue, the system will industrialise the disagreement rather than resolve it. Fix the semantic layer first; that work is valuable whether or not you ever add natural language.

And if the goal is to remove analysts, expect disappointment. What these systems reliably remove is the queue of simple, repetitive questions, which is roughly half of it. Anyone quoting on the promise of replacing an analytics team is selling a demo. What 95% accuracy really means is worth reading before you sign anything with an accuracy number in it.

What the money actually buys, week by week

On a focused pilot at $12,500 or ₹8 lakh, roughly the first fortnight goes to the question inventory and schema audit, the next three weeks to the semantic layer and retrieval over the schema, and the final week to the evaluation suite and the permission tests. Model selection takes an afternoon. Two further weeks of that schedule run in parallel with your team writing the question inventory, which is the one input we cannot produce for you: it has to come from the people who currently queue up behind an analyst. Clients are consistently surprised that the cheapest line in the budget is the one they spent the most time debating; we benchmark two or three models against the same eval set and route between them.

The hidden costs are documented separately in the hidden costs of text to SQL solution that quotes leave out, and they are worth reading before you compare two quotes that look similar.

A budget checklist

• Count the tables that must be queryable, not the tables you own
• Establish whether a maintained semantic or metrics layer already exists
• Decide the accuracy bar, and who signs off that it has been met
• Confirm whether row-level permissions are required on day one
• Forecast monthly question volume and multiply by a realistic per-question cost
• Budget warehouse compute separately from model spend
• Choose the care tier before launch, not after the first schema change breaks something
• Ask every vendor for a fixed price and a named scope, and treat hourly quotes with suspicion

Related reading

Natural-language reporting: turning questions into dashboards covers what happens after the query runs, and conversational analytics in Slack and Teams sets out what the chat surface adds and costs. For a sense of how in-product answers change usage, the in-app copilot case study is the closest worked example we publish.

Budget for the semantic layer and the eval suite, treat the model as a line item you can change later, and you will pay roughly what this should cost.