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Databricks Genie Code
The nightly pipeline failed again. Someone is pasting a stack trace into a chatbot that has never seen your schema — Databricks Genie Code plans, writes, runs and repairs Python and SQL in notebooks, the SQL editor, Lakeflow pipelines and MLflow — acting with your Unity Catalog rights, asking before each tool action, and now also from the terminal in Beta.
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Same software. Better outcome — at a lower cost.
How it’s rated
Full scoreboard ↓Quick answer
The Databricks platform family
This page covers Databricks Genie Code — the coding agent for data engineers and scientists; Genie One and Genie Agents for business users have their own page. The rest:
Quick facts
30-second orientation- Product
- An agentic coding assistant for data engineering, data science and dashboard authoring on Databricks
- Formerly
- Databricks Assistant, renamed Genie Code on 11 March 2026 when Agent mode became generally available
- Maker
- Databricks, Inc., San Francisco; founded 2013; Bengaluru R&D centre; India country manager since Sep 2025
- Works in
- Notebooks, SQL editor, Lakeflow Pipelines Editor, AI/BI dashboards and MLflow; CLI in Beta since Oct 2026
- Agent mode
- One prompt can find assets, generate and run code, repair errors and chart results; tool use needs approval by default
- Models
- Agent mode runs on Azure AI Services, OpenAI on Databricks and Anthropic on Databricks models
- Price
- 150 free DBUs per user a month, then $0.07 per DBU in US East and $0.074 in Mumbai and Central India
- Promotion
- Usage above the allowance is 25% off to 31 Jan 2027; Genie Code is outside the free Genie One offer
- India
- India Geo: chat and cell actions stay in-Geo; Agent mode runs only with cross-Geo processing switched on
- In India via
- TechBag — a measured pilot on real pipelines, DBU forecasting, quote itemised in INR with GST
Understand AI coding agents for data work before you buy one
Most product pages skip this. We start here — so you buy a capability, not a buzzword.
A coding agent for data work
Genie Code plans, writes, runs and fixes notebook, SQL and pipeline code inside your Databricks workspace.
A general chatbot and copy-paste vs Databricks Genie Code — the honest table
What consolidation actually replaces, dimension by dimension.
| Dimension | A general chatbot and copy-paste | Databricks Genie Code |
|---|---|---|
| Context | Paste the schema into a chat | Reads the catalog, notebooks and pipelines itself |
| Failures | Copy the stack trace, search, retry | Runs the code, reads the error, repairs it |
| Access | A model with no idea of grants | Acts only with your Unity Catalog rights |
| Routine runs | Cron scripts nobody owns | Scheduled tasks and a Lakeflow Jobs task |
| Licence | A seat for everyone who might try | 150 DBUs a user a month, then metered |
| What it is NOT | — | A general app-code assistant, or one that runs off Databricks |
The cheapest test is five engineers for a month inside the allowance: compare build and fix times on two real workloads, and read the compute the agent triggered.
The four pieces of the platform
Vendors love diagrams; buyers need to know what they’re actually operating. Here’s the whole platform, demystified.
Editors
Genie Code sits as a side pane or full page beside notebooks, the SQL editor, the Lakeflow Pipelines Editor, AI/BI dashboards and MLflow, reading the asset you have open.
Agent loop
In Agent mode it gathers relevant tables and files, writes code, runs it on your compute, reads errors and retries, pausing for approval before each tool action unless auto-approve is on.
Governance
It acts with the signed-in user’s Unity Catalog rights, so it cannot read tables that person cannot; MCP connectors and the CLI’s model access route through Unity Gateway.
Billing
Model work draws on a 150-DBU monthly allowance per user, then bills as Serverless Real-time Inference; clusters, warehouses and jobs it starts are billed on their own SKUs.
Engineers prompt from an editor or the CLI, the agent runs code on your compute under Unity Catalog rights, and model DBUs and triggered compute bill apart.
Nine capabilities. Code, automate, control.
Databricks Genie Code turns one prompt into working data code, run and repaired inside your workspace under your own Unity Catalog permissions.
Write and fix Python and SQL
Generates cells, explains failures and repairs them in place, using the schemas and files the notebook can already reach.
Build Lakeflow pipelines
In the Lakeflow Pipelines Editor it creates, updates and debugs production pipelines, with chat beside the graph and metrics.
Agentic machine learning
Drafts feature code, trains and tracks models in MLflow, and helps debug deep-learning jobs on AI Runtime (Public Preview).
Multi-step work from one prompt
Agent mode retrieves assets, writes and runs code, fixes errors and plots results; since June 2026 the pane works only this way.
Unattended recurring runs
Scheduled tasks (GA September 2026) rerun a prompt on a timetable, and a Lakeflow Jobs task (Beta) runs one inside a job.
Genie Code in the terminal
The Beta CLI from 6 October 2026 runs the agent in your shell on top of the Databricks CLI, with model access through Unity Gateway.
Unity Catalog permissions
Every read and write runs as you, so table grants, row filters and column masks bind the agent exactly as they bind the user.
Approve or auto-approve tools
By default it asks before running code or editing a notebook; an auto-approve mode, added June 2026, skips those prompts.
Skills, instructions, MCP
Workspace skills, custom instructions and MCP servers shape how it works; effort levels trade answer depth against DBU cost.
Watch Databricks Genie Code in action
Three videos from Databricks’ official channel, all from 2026: the March launch video, an end-to-end demo from the same day, and a Data + AI Summit session on agentic machine learning.
Genie Code: Your autonomous AI partner for data engineering, science and analytics.
The ten-minute launch video from rename day, 11 March 2026, setting out what Genie Code does for data teams.
Genie Code in action - An end-to-end demo.
A ten-minute end-to-end demo of Genie Code working through a data task inside the Databricks workspace.
Agentic machine learning with Genie Code (includes demo)
A 23-minute Data + AI Summit 2026 session with a demo of Genie Code driving machine-learning work end to end.
Want a live, India-context walkthrough for your environment?
Book a guided demo →Most AI assistants have never seen your catalog. Databricks Genie Code works inside it.
Here’s what genuinely sets it apart — and exactly where it stops.
It knows the lakehouse it is coding against
General assistants see a repository; Genie Code sees the catalog. It works from the tables, notebooks and pipelines in your workspace and acts with your Unity Catalog grants, so a generated join uses real column names and cannot touch data you are not cleared for. That matters most on messy, shared lakehouses.
From one prompt to a running pipeline
Agent mode, GA since March 2026, chains the steps a data engineer would do by hand: find the source, write the transformation, run it, read the stack trace and try again. Scheduled tasks and the Lakeflow Jobs task (Beta) let the same agent rerun work unattended, and the October 2026 CLI brings it to the terminal.
A metered price with a standing allowance
There is no seat licence. Each person gets 150 DBUs a month, worth about $10.50 at US East rates, and only use beyond that is billed, at a quarter off list until 31 January 2027. Light users may never pay; heavy users scale with real work. Admins can set budgets through Unity AI Gateway.
Where it stops
It is built for data work on Databricks, not general application code across your estate, and the CLI is still Beta. Clusters and warehouses it starts cost extra, and service principals get no allowance. In India, Agent mode, which the pane now always uses, sends prompts outside the India Geo unless cross-Geo is switched off.
The numbers behind the platform
What your Databricks Genie Code rollout looks like
Choose two real workloads
Pick one broken or slow pipeline and one notebook analysis, and record how long each takes to build or fix by hand today.
Decide the Geo setting
Agree with your DPO whether cross-Geo processing stays on for Agent mode, set approval rules and turn on Gateway budgets.
Pilot with five engineers
Let a small group use Agent mode on the two workloads, reviewing every diff, and log DBUs per user from the system tables.
Add schedules and skills
Move one recurring check to a scheduled task, add workspace skills and instructions, and test the Beta CLI in a sandbox.
Forecast and commit
Project usage beyond 150 DBUs a user plus the compute it triggers, then take TechBag’s INR quote with GST for the rollout.
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“It writes joins with our actual column names because it reads the catalog. Copilot in VS Code kept guessing at the schema.”
“Agent mode fixed a broken pipeline overnight from one prompt, but I still read every diff before it goes near production.”
“Scheduled tasks now run our weekly data-quality checks and leave a chat we can pick up. That replaced a fragile cron script.”
“Our DPO stopped the rollout for a month: Agent mode needs cross-Geo processing from our Mumbai workspace and the pane uses nothing else.”
“The allowance covers most of our analysts. Two heavy users blew past it, and the warehouse they kept starting cost more than the DBUs.”
“Feature engineering and MLflow tracking from a prompt saved our small team real hours on demand-forecast models.”
Where everyone sits — the grids
Analyst firms bury this view behind paywalls, and review-site grids score satisfaction, not fit for your estate. So here’s TechBag’s synthesis of the AI coding assistant market — tap any vendor to see why it sits where it does.
TechBag AI Coding Assistant Grid
Execution strength vs product vision — the classic market map, minus the paywall.
150 DBUs a user free, then $0.07 per DBU in US East.
Coding-agent Depth × Data-platform Context
The grid nobody publishes — how far each assistant can act on its own vs how much it knows about the warehouse, catalog and pipelines it is coding against.
Agent mode default; Unity Catalog grants; Lakeflow and MLflow.
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.
Databricks Genie Code vs the AI coding field
Against Snowflake CoCo, GitHub Copilot, GitLab Duo, Cursor and Kiro — on data context, agent depth, governance, models, price, allowance, India processing and exit.
| Dimension | Databricks Genie Code | Snowflake CoCo | GitHub Copilot | GitLab Duo | Cursor | Kiro (AWS) |
|---|---|---|---|---|---|---|
| What it is | Lakehouse coding agent | Snowflake coding agent | General AI pair coder | AI layer for GitLab | AI-native editor | AWS agentic IDE |
| Where it runs | Workspace plus CLI | App, CLI, Snowsight | Editors, CLI, app | GitLab and IDEs | Cursor’s editor | Kiro IDE and CLI |
| Data-platform context | Catalog-aware | Account-aware | Repository context | DevOps context | Repo plus MCP | Specs and MCP |
| Agentic depth | Agent mode by default | Agent across tools | Coding agents | Duo Agent Platform | Agents and Bugbot | Specs, hooks, agents |
| Pipelines and ML | Lakeflow and MLflow | dbt and Airflow | Any code in repo | GitLab CI focus | Build your own | Via MCP servers |
| Governance and approvals | Approve each tool use | Snowflake roles | SAML SSO, Enterprise | GitLab permissions | SSO, SCIM, audit | SSO on Enterprise |
| Models and extensions | Three providers | Model choice by cost | Credit pool per seat | Credits, models unclear | MCPs, skills, hooks | Claude, open weights |
| Pricing model | Allowance, then DBUs | Tokens in AI credits | Seat with AI credits | Tier plus credits | Flat per user | Credits per user |
| Published entry price | $0.07 per DBU | $2.00 per AI credit | $19 a seat | $29 Premium seat | $40 a Teams user | $20 a Pro user |
| Included vs extra | Compute is extra | Trial credits only | 1,900 credits a seat | $12 or $24 credits | Limits unstated | $0.04 per extra credit |
| India processing | Chat in, agent out | Account in India | Not documented | Self-host in India | No region named | Not stated |
| Self-hosting | Managed only | Managed only | Cloud service | Self-managed option | Vendor-run agents | AWS service |
| Lock-in and exit | Bound to Databricks | Bound to Snowflake | Easy to drop | Bound to GitLab | Editor migration | Leans on AWS |
| Best fit | Databricks data teams | Snowflake data teams | Company-wide coding | GitLab estates | Agent-first devs | AWS builders |
Which approach fits you?
Honest fit signals — because the fastest way to lose your trust is to pretend one product wins every scenario.
Choose Databricks Genie Code if…
- ✓Your data engineers and scientists spend their day in Databricks notebooks, pipelines and MLflow
- ✓You want generated code that respects Unity Catalog grants rather than a model guessing at schemas
- ✓You prefer a free monthly allowance per user and metered overage to buying a seat for everyone
Compare alternatives if…
- ✓Most of your code is application code — GitHub Copilot, Cursor or Kiro cover far more of it
- ✓Your warehouse is Snowflake — CoCo plays the same role there, billed in AI credits
- ✓Agent prompts must be processed inside India — no tool in this table documents that by default
Do not expect…
- ✓Free compute — clusters and warehouses it starts are billed on their own meters
- ✓A self-hosted edition, or a GA terminal tool: the CLI is still in Beta
- ✓An allowance for service principals, or inclusion in the Genie One free promotion
Databricks Genie Code is one of 36 developer tools products TechBag carries. The Developer Tools guide narrows them to a shortlist and shows the reasoning. →
What does hand-fixing data code cost you?
Drag the sliders (routine data-engineering tasks a year; engineer-hour cost). Estimates model the time spent writing boilerplate, chasing stack traces and fixing broken notebook and pipeline code, at an assumed 1.5 hours per task, with 70% of it removed when the agent drafts and repairs the code. Both figures are assumptions, and DBU and compute charges are not netted off. Illustrative.
Loaded cost = salary + overheads per productive hour. Illustrative only — your TechBag quote models your actual environment and modules.
Three ways to consume it
Metered, with an allowance. Every named user receives 150 DBUs of Genie Code usage a month at no charge, drawn from one pool shared with Genie One and Genie Agents. Past that, usage is billed on the Serverless Real-time Inference SKU at $0.07 per DBU in US East and $0.074 in AWS Mumbai or Azure Central India, with a 25% promotional discount until 31 January 2027. Service principals get no allowance, and clusters or warehouses it starts are billed separately. TechBag quotes in INR with GST.
Genie Code
Best for data engineers and scientists
- 150 DBUs per user a month at no charge
- Then $0.07 per DBU in US East, $0.074 in Mumbai
- 25% off overage until 31 Jan 2027
+ Platform add-ons
Best for a broader rollout
- Scoped to your estate
- Add-on modules as needed
- Phased, right-sized deployment
+ Compute it triggers
Billed on its own SKUs
- Clusters, SQL warehouses and jobs it starts
- Not covered by the allowance
- Service principals pay every DBU
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 requirements 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.
Agent mode needs cross-Geo processing in India and the pane now runs only that mode; is legal content with that?
How many of our users will exceed 150 DBUs a month, and what does each heavy user cost after the 25% promotion?
Which clusters, warehouses and jobs will the agent start, and who owns the compute bill it triggers?
Who may switch on auto-approve for tool actions, and on which workspaces holding production data?
Which scheduled or job-task runs will execute as service principals, which receive no free allowance at all?
Are partner-powered AI features enabled, and which model providers has our security team approved?
Is the Beta Genie Code CLI acceptable on engineers’ laptops, and is its model access routed through Unity Gateway?
Will usage draw on a Databricks commit or the Azure bill? Ask TechBag to quote either in INR with GST.
Questions buyers ask
Ready to evaluate Databricks Genie Code?
Bring one broken pipeline and one notebook analysis, or let a TechBag advisor run a measured pilot, settle the cross-Geo question and forecast DBUs past the allowance in INR.
Stats, ratings, review counts and pricing are illustrative and sourced from public materials; verify before purchase.