Building a production-grade AI app is no longer the exclusive domain of large engineering teams. The rise of modern AI app builders, managed databases, and serverless compute has compressed what once took months into days. Yet shipping a working app that users depend on — one that handles transactional state, enforces data governance, and integrates live data — still requires disciplined planning.
This guide walks through every phase of AI app development, from defining your project goal to monitoring AI applications in production. Whether you are evaluating no code tools, comparing AI app builders, or designing agent orchestration flows, these steps give you a repeatable development process you can adapt to any use case.
Overview Of AI App Development
AI app development covers a broader surface than traditional web apps. A conventional web app reads and writes data and renders a user interface. An AI app additionally orchestrates one or more AI models, manages prompts, handles non-deterministic outputs, and — in agentic workflows — sequences tool calls across multiple steps.
The development process must account for all of these layers simultaneously. Modern AI applications also inherit governance and security requirements from the data platform that traditional web apps rarely face.
Define The Project Goal And Target User
Before choosing an AI app builder or writing a single line of code, clarity on purpose is essential. The best AI app development cycles begin not with tooling but with a crisp statement of who the app serves and what outcome it delivers.
Ask these questions early in app creation:
- Who is the primary user, and what task does the app help them complete faster?
- What data does the app need to read, write, or analyze to deliver that value?
- What does success look like at launch, and at ninety days after launch?
For data and analytics teams building on Databricks, these questions often point toward internal tools — holiday approval workflows, support triage apps, campaign monitoring dashboards. Internal tools are among the highest-ROI AI applications a data team can build: the audience is known, workflows are defined, and success is measurable.
Set Success Metrics And Launch Timeline
Map your success metrics before finalizing your concept. Useful metrics for AI apps include time saved per user session, reduction in escalations or errors, and the percentage of queries handled automatically.
Set a realistic launch timeline that accounts for data preparation, model evaluation, security review, and user testing. The best AI app builders automate boilerplate code, scaffold backend logic, and remove infrastructure setup from the critical path — but budget time for the steps that require human judgment.
Map User Journeys That Require AI
Start with user flows. Walk through each key task a user performs and mark the steps where AI features add distinct value: summarizing a long document, classifying an incoming request, generating a recommended action, or retrieving relevant records from a large corpus.
Not every step benefits from AI integration. Focusing AI capabilities on the highest-leverage moments keeps the development process lean.
List Must-Have Versus Nice-To-Have AI Features
Separate core features from enhancement features. A must-have AI feature makes the app unusable without it. A nice-to-have AI feature improves experience but does not block launch. For a support portal powered by AI apps, the must-have is surfacing predicted escalation risk for each ticket. The nice-to-have is a generative AI summary of the ticket history.
Build the must-haves first, ship to users, and layer in enhancements based on feedback.
Choosing An AI App Builder
The AI app builder market has expanded rapidly. Teams now have access to no code platforms that generate entire apps from a blank prompt, visual builders that expose backend logic through a visual editor, and full-stack frameworks that give app developers complete deployment control. The right choice depends on whether you need a no code tool for rapid prototyping or a full framework for production app building.
Shortlist Three AI App Builders To Evaluate
When building a shortlist of AI app builders, evaluate each platform across three dimensions.
Scope of support. Does the AI app builder handle only the user interface, or does it also scaffold database setup, manage api keys, configure config files, and provision built in databases? Full stack apps require end-to-end support across all of these layers. An app builder that only handles the front end forces you to assemble the rest of the stack yourself.
Target user. Some app builders target non technical users and prioritize user friendly interfaces and no code tools that require minimal coding knowledge. Others are designed for app developers who need precise control over code quality and deployment behavior. Matching the AI app builder to the team’s technical profile keeps the development process smooth. Choosing the best AI app builder means evaluating fit, not just features listed on a pricing page.
Platform integration. The best AI app builder for your team is the one that connects to the databases, identity systems, and deployment infrastructure you already use. An app builder that forces you to replicate data into its own proprietary store adds risk and cost that compounds as you add other apps.
For teams that already run analytics on Databricks, Databricks Apps is a strong choice. It provides serverless compute for Python and Node.js web apps, built-in OAuth, and direct access to governed lakehouse data — all without managing containers. Teams build apps ranging from a basic ui prototype to multi-step agent workflows, with all apps running on the same platform where their data lives.
Verify Code Export And Deployment Pipeline Support
Any serious AI app builder should support code export and CI/CD pipeline integration. Apps that live exclusively in a proprietary environment accumulate technical debt. Confirm that your chosen AI app builder allows code export, version control, and CI/CD pipelines.
Databricks Asset Bundles (DABs) address this requirement directly. DABs let teams define their entire stack — app code, database configuration, and data sync pipelines — in version-controlled YAML and Python files. A single databricks bundle deploy command deploys apps consistently across development, staging, and production environments.
Check Integrations With Your Data Sources
An AI powered app without reliable data is an empty shell. Verify that your chosen AI app builder can connect to the databases and data stores your use case requires: relational stores, data warehouses, google sheet exports, file storage, and third-party APIs.
Lakebase — Databricks’ fully managed PostgreSQL service — solves data integration at the platform level. Synced tables mirror Unity Catalog Delta tables into Postgres, so apps always query fresh, governed data. These tables sync automatically from lakehouse sources, meaning apps always reflect the latest state of upstream data within seconds of a change.
Compare Pricing And Deployment Limits
Evaluate pricing across the full development lifecycle. Start on the free tier or free plan to validate your concept, but assess every AI builder against production requirements before committing. Many AI app builders offer a generous free plan for prototyping but impose limits on compute, concurrent users, or model calls. Understand what triggers a move from the free plan to a premium plan, and whether pricing scales predictably.
Audit deployment limits carefully as well. Enterprise features like role-based access controls, audit logging, and custom domain support are often gated to higher paid plans. Compare paid plans before committing, because every app you add to the platform will fall under the same pricing model. Many teams start on a free plan to validate their first AI app before upgrading to paid plans that support production workloads.
Selecting An AI Model Strategy
Decide Between Pre-Trained Models And Fine-Tuning
Most AI app development projects begin with a pre-trained model and a prompt. Large language models available through managed endpoints handle a wide range of tasks — classification, summarization, extraction, and generation — without requiring fine tune cycles upfront.
Fine-tuning earns its cost when a pre-trained AI model consistently underperforms on domain-specific data. If the AI app requires the model to reason over proprietary terminology or classify inputs according to a custom taxonomy, fine tune the model on representative examples from your own dataset. Using your own model — fine tuned on internal data rather than generic benchmarks — typically produces meaningfully better accuracy for domain-specific tasks.
Plan for ongoing fine tune cycles as production data drifts from training distributions. A model that performs well at launch may degrade quietly as the distribution of incoming inputs shifts, making scheduled fine tune reviews essential.
Evaluate Model Latency And Inference Cost
Every AI model call adds latency to the app and cost to the inference budget. Measure baseline latency on representative inputs before committing to a model. For apps where users expect sub-second responses — dashboards, chat assistants, real-time recommendations — model latency is a hard constraint.
Inference cost compounds at scale. Fine tune a smaller, cheaper model if a larger model’s cost profile makes it impractical for the target use case. Build inference cost into your financial model early.
Test Model Accuracy On Representative Samples
Run offline evaluations on a representative sample before deploying any AI model to production. Build a labeled evaluation set covering the edge cases your app will encounter — ambiguous inputs, incomplete records, adversarial queries — and measure precision, recall, and task-specific accuracy against that set.
Automated evals are not optional for production apps. They are the foundation of a responsible development process and the primary quality gate for enterprise AI applications.
Designing Core AI Features And AI Prompts
Prioritize Two To Four AI Features For MVP
The most common mistake in AI app development is attempting to build too many AI features at once. Narrow the MVP to two to four AI features that directly address the highest-priority user jobs. Each additional feature multiplies the surface area for failure and extends the testing burden on the entire app.
For a reverse ETL-powered support portal, the MVP features might be: escalation risk scoring from lakehouse ML predictions, recommended action generation based on ticket type, and natural language search over historical tickets.
Create And Reuse Prompts For Each Feature
Write prompts as reusable templates, not one-off strings buried in app code. Each AI feature should have a named prompt template, a version, and a clear contract for its input and output format. Treat prompts the same way you treat database queries — they are part of your core logic and deserve the same engineering discipline as any other component of the app.
Parameterize prompts to accept dynamic context — ticket content, user history, product version — while keeping the instruction structure stable. Stable instructions combined with dynamic context produce more consistent outputs and make fine tune iterations more tractable.
Define Structured Output Schemas For Reliability
Instruct the model to return structured data rather than free-form text wherever the output feeds downstream logic. JSON schemas or typed response formats make outputs programmatically reliable and remove the need for brittle parsing logic. For apps where multiple steps depend on each other’s outputs, consistent typed formats between steps are essential.
Design Retrieval (RAG) Flows For External Data
Retrieval-augmented generation connects a model to external databases at inference time, grounding outputs in current facts without requiring fine tune cycles. Design RAG flows for any AI feature that needs to answer questions about documents, tickets, or records that change frequently.
In a Databricks-native architecture, RAG flows query Unity Catalog tables, vector search indexes, and Lakebase Postgres tables through a unified access layer — with platform-level governance applied automatically.
Building With AI Assistant And AI Agents
Plan Where An AI Assistant Will Speed Up Development
An AI assistant embedded in the app development environment — editor chat, inline code suggestions, automated test generation — can compress time from app idea to working app. Plan specifically where AI speeds up development: scaffolding data models, generate code for boilerplate patterns, writing unit tests for backend logic, and drafting documentation are all high-leverage targets.
Use AI-assisted tooling for acceleration, not replacement. Every change generated by the coding assistant needs human review before it enters the codebase. AI-assisted generation is fastest when a developer can immediately recognize whether the output is correct — which requires the developer to understand the domain and the system design.
Manual edits remain essential for catching subtle errors that automated generation misses, especially in apps with complex backend logic or fine-grained permission requirements.
Enable Human Review For Every AI-Generated Change
Establish a workflow where no AI-generated change reaches production without explicit human approval. This requirement maintains code quality and prevents errors before they reach apps running in production.
Integrating An AI Assistant Into The Editor
Enable Chat Edits For UI And Workflow Changes
Modern AI app builders expose chat-based editing interfaces that let developers describe a change in natural language and apply it to the codebase. Enable these chat edits for repetitive user interface modifications — restyling components, adding form fields, reordering layout elements — where writing code manually adds no additional insight.
Reserve natural language prompts for well-scoped, reversible changes. Open-ended natural language instructions applied to complex logic produce unpredictable results and generate extra manual work to fix.
The key difference between productive and counterproductive use of an AI assistant in app building is specificity: narrow, concrete requests produce usable outputs; vague requests produce noise.
Log Assistant Actions For Auditability
Every action taken by AI-assisted tooling in the development environment should be logged: what was requested, what was generated, and whether it was accepted or rejected. Logs provide an audit trail and create a training dataset for improving accuracy on your specific codebase over time.
Requiring manual approval before production deploys. Gate every production deployment behind a manual approval step, regardless of how much of the build was automated. DABs support this pattern natively through CI/CD pipeline integration. Deployments to staging are automated; promotions to production require an explicit gate in the pipeline.
Orchestrating AI Agents For Multistep Flows
Define Agent Responsibilities And Tool Access
AI agents extend AI app development from single-step model calls to multistep workflows where the model acts as a planner and tools — database queries, API calls, document retrievals — are its actuators. In agent mode, the model decides which tools to call and in what order to accomplish a stated goal.
Define clear boundaries for each agent: what tools it can access, what data it can read and write, and what decisions require human confirmation. An AI agent builder like LangGraph, combined with Unity Catalog functions as governed tools, gives you fine-grained control over what each agent is allowed to do.
Databricks supports native integration with LangGraph, making it straightforward to build AI agents that orchestrate across governed data assets. For the cybersecurity investigation agent in Databricks’ hands-on guide, two Unity Catalog functions serve as agent tools: one retrieves threat details for a given threat type, the other retrieves user information for a source IP. Each execution step is persisted in Lakebase for stateful checkpointing using LangGraph checkpointing, enabling investigations to pause and resume across sessions with full context intact.
Creating failure recovery steps for each agent task. Agents operating over real world scenarios encounter failures: tools return empty results, external services time out, and models hallucinate invalid arguments. Build explicit failure recovery steps for each agent task — retry with backoff, fall back to a simpler query, escalate to human review — and test those recovery paths as rigorously as the happy path.
Testing agent sequences with realistic inputs. Run agent sequences against realistic inputs before deploying apps with agent capabilities to users. Synthetic test cases miss the edge cases that real data exposes. Seed your test suite with anonymized examples covering the full distribution of input types the agent will encounter.
Data Preparation For AI Applications
Inventory Internal Data Sources To Connect
Build a complete inventory of the databases and internal data sources your AI app needs before writing any data access code. For each source, document: the data format, update frequency, owning team, access control model, and any compliance restrictions. Enterprise AI applications often depend on dozens of internal data sources spread across multiple systems — cataloging them first prevents integration surprises later.
This inventory drives decisions about sync mode, schema design, and governance configuration. Data from Unity Catalog Delta tables can sync directly into Lakebase, making them available to apps as structured data through a standard Postgres connection. Lakebase supports three sync modes — Snapshot, Triggered, and Continuous — allowing teams to match data freshness to app requirements and balance cost accordingly.
Cleaning and labeling data for training or evals. Data quality is the primary determinant of model performance. Clean training and evaluation data — removing duplicates, correcting labels, filling structural gaps — before using it to fine tune or evaluate any model. Track data lineage from source to model so that quality issues in incoming data can be traced back to their origin and corrected upstream.
Enforce Data Retention And Access Policies
Define data retention policies before data enters the AI app pipeline. Specify how long training data, evaluation data, and inference logs are retained, who can access them, and when they are deleted.
Access policies for apps should extend the data governance model established for the underlying data. Unity Catalog enforces row-level and column-level permissions consistently across all access paths — including Lakebase — ensuring that the same policies that govern lakehouse tables propagate automatically to the apps that consume them.
Security, Privacy, And Guardrails For AI App
Building AI apps without a security-first mindset introduces risk at every layer: the model layer, the data layer, the app layer, and the deployment layer. Security concerns discovered after a breach are far more expensive than concerns addressed during the development process.
Apply Input Moderation Before Model Calls
Filter user inputs before passing them to any model. Input moderation catches prompt injection attempts, personally identifiable information, and content that violates usage policies. Apply moderation as a preprocessing step, not an afterthought, and log rejected inputs for review.
Encrypt Data In Transit And At Rest
All data transmitted between apps, databases, and model serving endpoints must be encrypted in transit using TLS. Data stored in the app database must be encrypted at rest. Lakebase enforces TLS for all Postgres connections and provides encrypted storage out of the box, satisfying both requirements without additional configuration.
Implement Role-Based Access Controls
Implement access controls at every layer of the stack. Database roles should be scoped to the minimum permissions required for each component — read-only roles for reporting views, write roles for state tables.
Databricks Apps integrates with Unity Catalog to enforce permission policies consistently. When apps are deployed, each app’s service principal receives only the permissions explicitly granted — no implicit elevation, no credential sharing. This extends enterprise grade security from the lakehouse all the way to the apps that surface its data.
Testing, Evals, And Quality Assurance For AI Applications
Build Automated Evals For Core Model Tasks
Automated evaluations are the backbone of responsible AI app development. For each core model task — classification, generation, retrieval — define an evaluation set, a scoring rubric, and a pass/fail threshold. Run evals on every model change before shipping apps to production — apps that pass evals consistently earn user trust faster.
MLflow, integrated natively into Databricks, supports tracing, logging, and evaluation of model behavior. For the cybersecurity agent example, MLflow tracing captures every tool call, intermediate state, and model output across a full investigation thread — making it possible to audit agent behavior and catch regressions before they affect users.
Run Unit And End-To-End Tests For Workflows
Unit tests validate individual components — a prompt template, a data transformation, a schema validation function. End-to-end tests validate complete workflows from user input to final output, including database reads and writes, model calls, and rendering of the app’s user interface.
Both test types are necessary for full stack apps and apps with multi-component workflows. Unit tests catch component-level bugs quickly; end-to-end tests catch integration failures that only appear when components interact.
Measuring drift and retraining models on schedule. Production apps degrade over time as the distribution of inputs shifts from the training distribution. Measure statistical drift on incoming inputs and model outputs on a regular schedule, and trigger fine tune cycles when drift crosses a defined threshold.
Schedule retraining reviews quarterly at minimum, and build the retraining pipeline as a repeatable workflow so it can be executed reliably when needed.
Deployment, Scalability, And Cost Optimization For AI Powered Apps
Choose Hosting That Supports Your Peak Load
Size your hosting environment for peak load, not average load. AI apps often experience burst traffic — a product launch, an internal rollout, a scheduled batch of agent runs — that can exceed average load by an order of magnitude. Apps sized correctly from day one scale gracefully; apps that are under-provisioned create incidents and erode user trust.
Serverless compute handles burst traffic gracefully by scaling horizontally without manual intervention. Databricks Apps runs apps on serverless compute that scales automatically, eliminating the need to pre-provision capacity or configure scaling policies.
Implementing model caching to cut inference costs. Many model calls in production apps answer the same or similar questions repeatedly. Implement semantic caching — caching responses by embedding similarity rather than exact string match — to serve repeated queries from cache rather than incurring inference costs.
For apps built on Databricks, in-memory caching using libraries like fastapi-cache reduces load on Lakebase model serving and model serving endpoints simultaneously, improving both latency and cost efficiency.
Create Blue-Green Deploys For Safe Rollouts
Blue-green deployment maintains two identical environments — one serving live traffic, one receiving the new deploy. Traffic shifts only after validation, and rollback is a single switch with no downtime.
Pair blue-green deploys with DABs for complete infrastructure reproducibility. Because DABs define the entire stack in code — compute for apps, database instance, synced table configuration — both environments can be provisioned from the same bundle with environment-specific variable overrides.
Integrations, Workflows, And App Builders Ecosystem
Connect Databases And Third-Party APIs Securely
AI apps rarely operate on a single database. They integrate relational stores for transactional state, warehouse tables for analytical context, third-party APIs for external enrichment, google sheet exports for ad hoc inputs, and vector indexes for semantic search. Each integration point is a potential failure mode and a potential security vector.
Secure every external connection: use api keys stored in secrets management systems rather than hardcoded in app code. Databricks Secrets provides a managed secrets store that apps access at runtime without exposing credentials. Build api keys rotation into your operational runbook from day one, because forgotten or leaked credentials are among the most common sources of security incidents in production apps.
Adding webhooks for real-time event handling. Webhooks push events from external services into apps in real time, enabling reactive workflows — triggering an agent run when a new support ticket arrives, updating a prediction score when a model is retrained, notifying a manager when an approval deadline is reached.
Design webhook handlers to be idempotent so that the same event delivered twice produces the same result as the event delivered once. This keeps apps stable and prevents duplicate records across apps that write to shared state tables.
Document Integration Points For Maintainability
Every integration between apps and external systems should be documented: the endpoint, the authentication method, the data contract, the error handling strategy, and the owner.
Documentation is not optional for production apps — it is the primary tool for onboarding new team members and diagnosing failures quickly. Well-documented apps outlast the individuals who built them — apps that are hard to document are usually hard to maintain.
Comparing Popular AI App Builders
The market for app builders ranges from no code tools designed for non technical users to full-stack frameworks designed for experienced developers. Understanding understanding the categories helps teams select the right AI app builder for their use case and avoid committing to a platform that cannot support long-term requirements.
Build A Small Prototype On Each Shortlisted Builder
The most reliable way to compare AI app builders is to build the same small prototype on each. Choose a representative scope — a form that reads from a database, calls a model, and writes a result back — and implement it on each shortlisted app builder from scratch.
This process exposes real friction: how long does it take to connect databases, how much coding knowledge is required, how does the AI app builder handle api keys and authentication, and how clean is the generated output? Real apps built during evaluation reveal integration surprises that marketing documentation conceals.
No code tools typically win on time-to-prototype for simple apps. For full stack apps with complex backend logic, enterprise grade security requirements, and unified data governance, purpose-built platforms like Databricks Apps provide more sustained value despite a higher initial setup investment. The best AI app builder is the one that removes friction at the specific layer where your team spends the most time — not the one with the longest feature list. When evaluating which is the best AI app builder for your organization, weight production fit over free-plan simplicity.
Measure Time To Functional Prototype For Fairness
Time to a functional prototype is the most objective comparison metric for AI app builders. Measure from project initialization to a working app that a user could actually interact with. Include time spent reading documentation, debugging integration issues, and resolving authentication problems.
Teams that skip this step and rely on feature comparisons alone frequently discover late in the development process that their chosen AI app builder does not support the specific pattern their app requires. Finding the best AI app builder means building something real on each platform, because the best AI app builder for a no code prototype may not be the best AI app builder for a production, enterprise-grade AI app.
Record Whether Builders Support Agent Orchestration
As AI app development matures, agent orchestration is becoming a standard requirement. Record whether each AI app builder on your shortlist supports agent mode, provides an AI agent builder interface, and integrates with orchestration frameworks like LangGraph.
Builders that treat AI agents as first-class concepts — with thread management, checkpointing, and governed tool access built in — serve complex apps more reliably than those that treat agents as a plugin. An app builder that supports complete apps with agent capabilities — including long-term memory, governed tool access, and multi-session continuity — is materially more powerful than one limited to single-turn model calls.
Monitoring, Observability, And Maintenance For AI Powered Apps
Track Latency, Error Rates, And User Satisfaction
Instrument every AI app for observability from day one. Apps that lack observability are nearly impossible to debug when something goes wrong. Track latency at each layer — database query time, model inference time, total response time — and set thresholds that trigger alerts when performance degrades.
Monitor error rates by component and by user segment. Collect satisfaction signals — correction rate, session abandonment, explicit ratings — as leading indicators of model quality alongside infrastructure metrics. These signals tell you whether your apps are actually working for users, not just whether the underlying systems are responding.
Set Alerts For Model Performance Regressions
Model performance regressions in production apps are often subtle. A model may continue returning valid-looking responses while accuracy on a specific input category quietly degrades.
Set automated alerts on evaluation metrics — not just infrastructure metrics — so that model regressions surface before they accumulate into visible failures. Pair these alerts with runbooks that define who responds, what they check, and when a model fine-tuning cycle is warranted.
Schedule Periodic Security And Compliance Reviews
Security controls that were adequate at launch may become insufficient as apps scale or compliance requirements change. Schedule periodic security and compliance reviews — quarterly for enterprise apps — that audit permissions, encryption configurations, encryption configurations, data retention practices, and the security of all external connections.
Platform-level governance simplifies these reviews significantly. When governance controls are enforced by Unity Catalog rather than by custom code within individual apps, auditors have a single, consistent control plane to examine rather than a patchwork of per-app security implementations.
Roadmap And Best Practices For AI App Development
Release A Minimal AI-Powered App And Iterate Quickly
The single most important best practice in AI app development is shipping early. A minimal AI powered app in the hands of users delivers more insight than weeks of internal planning. Real users expose edge cases, workflow gaps, and usability problems that no amount of design review anticipates.
Compress the time from concept to shipping apps by using managed services — serverless compute, managed databases, pre-built authentication — that eliminate infrastructure work. The development process should focus on the AI features and core logic that differentiate the app.
Databricks Apps and Lakebase remove the infrastructure layer entirely, letting teams build apps and deploy them in minutes. Internal tools, generative AI interfaces, and data apps that once required dedicated DevOps support can now ship from the same data team that builds the underlying analytics. Whether you are starting with simple internal tools or scaling enterprise AI applications, removing infrastructure overhead is what enables teams to move fast.
Collect User Feedback To Refine Prompts And Models
User feedback is the primary input for prompt refinement and fine tune prioritization. Log every interaction where a user corrects, dismisses, or flags a model output. Analyze those interactions to identify systematic errors — instructions that are ambiguous, contexts that are missing, output formats that don’t match downstream needs.
Refine prompts incrementally, running automated evals after each change to confirm improvement on the target metric without degrading other outputs. Use fine tune cycles for errors that prompt engineering alone cannot correct.
Plan For Long-Term Model Governance And Audits
Enterprise apps operate under increasing regulatory scrutiny. Plan for long-term model governance before it becomes urgent: document every model in production, establish a process for responding to audit requests, and build model lineage tracking into the platform from the start.
Databricks MLflow provides model versioning, experiment tracking, and lineage visualization natively. For AI apps built on Databricks, model governance is a first-class platform capability — making it easier to satisfy audit requirements as regulatory expectations evolve.
Building and scaling AI applications is a multi-disciplinary challenge. The teams that ship reliable AI apps fastest choose platforms where app hosting, database management, authentication, and governance are integrated by default — then invest engineering effort in the AI features and workflows that create real value for production AI applications.
Databricks Apps and Lakebase provide exactly this foundation: serverless compute for web apps and AI apps, a fully managed Postgres database with native lakehouse integration, and a unified governance layer through Unity Catalog. Together, they transform how teams build apps: entire app stacks — transactional state, analytical context, deployed user interfaces, and AI agents — run on a single platform, with one security model, one deployment pipeline, and one governance framework.
That is the foundation that turns a promising concept into a production AI app that users trust.
