Natural language to SQL
Ask questions in plain English. Get safe, runnable SQL.
Fastero reads your warehouse schema, generates SQL in the correct dialect, and shows you the query before anything runs. Connect Postgres, BigQuery, Snowflake, or seven other sources — then ask questions instead of writing joins by hand.
How it works
From question to answer in four steps.
No semantic layer to configure. No dbt project to maintain. Connect a warehouse, ask a question, and get schema-aware SQL that you can inspect, edit, execute, and save.
Connect a database
Add credentials for any supported warehouse. Fastero reads the schema — tables, columns, types, foreign keys — and indexes it for the AI. No modeling layer required.
Ask a question in plain English
Type something like “Show me customers who churned this month and their last activity.” The AI uses your schema context to generate SQL in the correct warehouse dialect.
Review, edit, or auto-execute
The generated SQL is shown as editable code. Inspect joins, tweak filters, or approve it. For recurring checks, enable auto-execute so results return without manual approval.
Save as a report or trigger an alert
Pin the query as a saved report. Or connect it to a trigger — run it on a schedule, and route results to Slack, email, or a webhook when a threshold is crossed.
9 native connectors + file uploads
One workspace, every warehouse dialect.
Each connector understands the target dialect so generated SQL uses the correct syntax — DATE_TRUNC vs DATEPART, LIMIT vs TOP, backticks vs double quotes. No manual dialect switching.
PostgreSQL
Postgres
MySQL
MySQL
Microsoft SQL Server
T-SQL
Oracle
PL/SQL
Amazon Redshift
Redshift SQL
Google BigQuery
Standard SQL
Snowflake
Snowflake SQL
Amazon Athena
Presto/Trino
Google Sheets
Structured data
Also works on uploaded files (CSV, Excel, Parquet) — the AI generates SQL against an in-memory table.
Honest comparison
What makes this different from ChatGPT or ThoughtSpot.
ChatGPT generates SQL without schema context or execution. ThoughtSpot delivers sub-second analytics but costs $100k+/yr and requires weeks of semantic modeling. Fastero sits in the middle: schema-aware, executable, affordable, and ready in 5 minutes.
| Capability | Fastero | ChatGPT | ThoughtSpot |
|---|---|---|---|
| Schema context | Reads your live schema — tables, columns, types, FKs | None — you paste DDL manually | Full semantic layer (requires modeling) |
| Executes on your data | Yes, read-only by default | No — generates SQL only, can’t run it | Yes, sub-second on modeled data |
| Follow-up refinement | Chat-over-data: ask follow-ups, AI refines the query | Stateless per prompt (unless you re-paste context) | Drill-down in UI, limited free-form follow-up |
| Triggers and alerts | Query results feed cron/webhook triggers | Not applicable | Threshold alerts on pinboards |
| Team workspace | Shared queries, saved reports, RBAC | Personal chat history only | Full enterprise workspace |
| Pricing for a 5-person team | Free tier available, paid from $49/mo | $20/user/mo (ChatGPT Plus) | $100k+/yr minimum (enterprise sales) |
| Setup time | 5 minutes — add connection, start asking | Instant (but no data access) | Weeks — requires semantic modeling |
Security & safety
Read-only by default. No data leaves your warehouse.
Read-only connections
Every connection starts as SELECT-only. Write operations require an explicit admin toggle per connection. The AI cannot generate INSERT, UPDATE, or DELETE unless write mode is enabled.
Schema metadata only to LLM
The language model receives table names, column names, and types. Raw row data from your warehouse is never sent to the LLM. Query results stay within your Fastero workspace.
Role-based access control
Team members see only the connections and saved reports their role permits. Admins control who can add connections, enable auto-execute, or share results externally.
Audit trail
Every generated query, execution, and modification is logged with the user, timestamp, and connection. Admins can review what was asked and what SQL ran.
No persistent data storage
Fastero does not copy your warehouse data. Query results are held in memory for the session and discarded. Saved reports store the query, not the result set.
AWS-hosted infrastructure
Runs on SOC 2-certified AWS infrastructure with encryption at rest and in transit. Connections use SSL/TLS. Credentials are encrypted with AES-256 and never logged.
Real questions, real answers
Queries your team actually asks every week.
These are not synthetic demos. They are the kinds of questions that currently result in a Jira ticket, a 2-day wait, or a half-remembered SQL query someone wrote last quarter.
“Show me customers who churned this month and their last activity”
Identify at-risk patterns without waiting for the BI team to build a dashboard.
“What’s our daily revenue trend for the last 90 days, broken by plan tier?”
Weekly revenue check without a standing Looker query or manual spreadsheet pull.
“Which sales reps have no activity this week?”
Monday morning pipeline hygiene. Set it as a trigger and get the answer in Slack every Monday at 9am.
“List orders over $10k that shipped late in Q2, with the assigned account manager”
Ad-hoc root cause analysis that would take 20 minutes to write by hand across 4 joined tables.
Honest limitations
What NL2SQL is not good for.
Every tool has boundaries. Knowing them before you start saves you from a bad evaluation. Here is where Fastero's NL2SQL will disappoint you:
Complex multi-table joins with ambiguous schemas
If your warehouse has 15 tables named "users" across schemas with no foreign keys, the AI may pick the wrong one. You can correct it in the editor — but the first attempt won’t always be right.
Real-time dashboards on billions of rows
Fastero generates and executes ad-hoc SQL. It does not maintain a pre-aggregated semantic layer. If you need sub-second response over a 10B row fact table, you need ThoughtSpot or a materialized view.
Production data pipelines
NL→SQL is for exploration and reporting, not for generating dbt models or production ETL. The queries are ad-hoc — useful for answers, not for building your data warehouse.
Highly specialized SQL dialects
The AI handles standard SQL well across all 10 connectors. Exotic dialect features (Oracle CONNECT BY, Snowflake FLATTEN with deep nesting) may require manual editing after generation.
FAQ
Frequently asked questions.
Does Fastero NL2SQL send my data to OpenAI or another third party?
The AI reads your schema metadata (table names, column names, types) to generate SQL. Query results stay within your Fastero workspace. No raw data from your warehouse is sent to a third-party LLM — the LLM only sees schema structure and your natural language question.
Can the AI accidentally run a DELETE or DROP statement?
No. Connections are read-only by default. The AI generates SELECT statements only unless an admin explicitly enables write mode for a specific connection. Even with write mode enabled, every generated statement is shown for approval before execution unless auto-execute is turned on.
How does this compare to just asking ChatGPT to write SQL?
ChatGPT has no access to your schema, cannot execute the query, and loses context between prompts. Fastero reads your live schema, generates dialect-correct SQL, executes it on your warehouse, and lets you ask follow-up questions that refine the query iteratively.
What happens when the AI generates wrong SQL?
The SQL is shown in an editor before execution. You can edit it, or ask a follow-up like “That joined the wrong table — use orders.customer_id instead.” The AI learns from corrections within the conversation and regenerates.
Can I save queries and share them with my team?
Yes. Any query can be saved as a report, which is visible to your team workspace. Saved reports can also be connected to triggers that run on a schedule and send results to Slack, email, or a webhook.
Is there a limit on query complexity or result size?
There is no hard limit on SQL complexity. Result sets are capped at 10,000 rows in the UI for performance, but the full result is available for download. For larger analytical workloads, consider connecting the query to a Streamlit app for custom rendering.
Related pages
ThoughtSpot alternatives
When enterprise AI analytics is overkill for your team size
Best AI data analysis tools
Side-by-side comparison of AI-powered analytics platforms
Hosted Streamlit apps
Turn query results into interactive Python dashboards
AI Studio
The full agent workspace with 89 tools beyond SQL generation
Stop writing SQL for questions you ask every week.
Connect your warehouse, ask a question in plain English, and get schema-aware SQL you can inspect before it runs. Free to start — no credit card required.