The 7 Best Enterprise AI Platforms in 2026 for Dev Teams

Most vendors that market to enterprises call their platforms production-ready and built for scale, and the phrase “enterprise AI platform” gets stretched the same way. It can mean infrastructure you build on top of, tools that handle prompts and evaluations, or an app builder where your team creates the software employees and customers use. If you pick the wrong one, you can end up with expensive lock-in and AI initiatives that slip by months.

The choice is getting harder. Vendors are renaming products and adding agent features quickly, and the platform you choose will eventually hold data and workflows that many teams depend on. Those teams need to be able to defend that platform in a security review and keep it running long after the pilot.

You’ll get the four layers a true enterprise AI platform covers and the criteria for evaluating infrastructure, AI-layer, and application delivery platforms. You’ll also get a side-by-side comparison of seven platforms by use case, deployment, governance, and app layer, a decision guide organized by cloud stack, a pattern for connecting an infrastructure platform to an application layer, and an explanation of how the pricing models work.

What is an enterprise AI platform?

An enterprise AI platform is a system that brings company data, AI models, security, and governance together so an organization can build, run, and scale AI apps across many teams. It replaces a patchwork of disconnected AI tools with one system that controls which data each user’s AI requests can reach and keeps a record of AI activity for compliance audits. “Enterprise” here means scale: AI that works across many teams and users, beyond one person working alone.

That makes it different from three other kinds of AI products:

• A standalone AI tool, like an employee using ChatGPT on their own without IT involved, isn’t tied into how your company manages data, identity, or audits. Enterprise versions of tools like ChatGPT do add governance and security controls, but they’re a different product from the consumer version most people default to.
• A raw API, like OpenAI’s API by itself, is a building block. You’d still have to build the governance, security, and deployment around it yourself.
• A product-specific copilot, like Copilot in Word, helps you draft and rewrite inside that one product. Microsoft 365 Copilot reaches further across the suite, but it’s still a helper built into existing software. You can’t use it as a platform for building new AI applications from scratch.

A true enterprise AI platform includes four core layers that work together:

• Data layer: Connects to your company’s data and only lets AI see what a given user is allowed to see.
• Model layer: Works with multiple LLMs (large language models), so teams can switch or route between them without rebuilding workflows.
• Governance layer: Monitors model behavior, enforces usage policies, and logs decisions for audits and compliance.
• Application layer: Gives you the tools (visual builders, SDKs, APIs) to build AI-powered applications on top of everything else.

Once you know these four layers, it’s easier to separate what a vendor delivers from what they promise.

Why enterprise AI platforms matter for dev teams

When every team runs its own AI experiment, none of it scales. People use tools nobody approved. There are no consistent rules for who can see what data, no record of what the AI did, and nobody watching for cost creep.

An enterprise AI platform gives every team one shared system. It manages access and keeps an audit trail of AI decisions, which gives compliance reviews one place to look. No team has to write its own access checks and audit trail every time it ships a new AI feature.

For dev teams, the benefit is time. When a platform has solid developer tooling, handles data access correctly, and makes deployment straightforward, your team can spend its hours on features instead of security scaffolding and data-access bugs.

What to look for in an enterprise AI platform

The platforms in this guide fall into three groups. Most are infrastructure-first, which is what most AI vendors build for enterprise organizations.

• Infrastructure-first platforms: These supply the model, data, and governance foundation that teams build on. Microsoft Foundry, AWS Bedrock, Gemini Enterprise Agent Platform, Databricks Mosaic AI, and IBM watsonx fall into this group.
• AI-layer tools: These help teams manage the prompts, evaluations, and model routing behind AI they already run. Vellum is the AI-layer tool on this list.
• Application delivery platforms: These provide the layer users interact with directly, including the screens, data, and logic of the app itself. Bubble is the application delivery platform on this list.

Infrastructure platform criteria

• Permission-aware RAG: With RAG (retrieval-augmented generation), the AI retrieves relevant documents from your company’s data before it responds, so its answers are grounded in what your organization knows. A permission-aware system also checks access at the moment someone asks a question, so a sales rep can’t pull up financial records that only the finance team is allowed to see.
• Multi-model support: According to Menlo Ventures’ enterprise AI research, a handful of providers handle most enterprise LLM usage. That concentration is a risk if one of them raises prices, changes a model, or goes down. If a platform can route queries to different LLMs without rebuilding your workflows, you have options when that happens.
• Deployment flexibility: Find out whether the platform can run inside your own VPC (virtual private cloud, a private slice of a cloud provider’s infrastructure), meet your data residency requirements, or run fully on-premises. On-premises matters for workloads that can’t move to the public cloud.
• Governance and auditability: Look for tools that monitor how the model behaves in production, log its decisions for compliance audits, and restrict access to sensitive operations. Gartner research found that organizations using AI governance platforms are 3.4 times more likely to achieve high governance effectiveness than those that don’t, which is worth weighing in financial services, healthcare, or government.
• Developer experience: Evaluate SDK quality, CI/CD integration, versioning, and rollback support. If the platform supports AI agents, check how it handles agent orchestration, including memory, tool use, and human-in-the-loop approval gates. Deloitte’s State of AI in the Enterprise research found that only one in five companies has a mature governance model for autonomous agents, so controls at the platform level matter.
• Cost controls: Per-run visibility, token budgets, rate limiting, and usage alerts keep a successful pilot from producing a surprise bill at scale. Check whether alerts fire per workflow or only at the account level.

Application delivery platform criteria

• App generation: Look for a platform that can generate a working app from a description, including the UI, database, workflows, and logic, so you aren’t starting from a blank canvas. A testable version lets you start collecting user feedback and iterating.
• Visual editing and control: AI generation gives you a starting point, and you’ll usually need to refine the result. The best platforms show you how the app works and let you edit any layer (design, data, workflows, logic, privacy rules) without going back to a prompt. Check that reviewers can see what an AI-generated change touched and can roll it back before it reaches users.
• Built-in security and governance: This means SOC 2 Type II compliance, SSO, and privacy rules you can configure without writing backend code. For organizations, these are what make an app deployable.
• Hosting and deployment: The platform should handle hosting, auto-scaling, and deployment so your team doesn’t have to manage infrastructure separately. One-click deployment and built-in version control reduce the work of keeping a production app running.

The 7 best enterprise AI platforms for dev teams at a glance

This table compares each platform’s primary use case, deployment options, built-in governance, model support, and whether it includes an application layer. RAG capabilities and mobile options are covered in each platform’s section below.

The 7 best enterprise AI platforms for dev teams

The first five platforms provide infrastructure your teams build apps on. Vellum is a specialized tool for testing and improving the AI you already run, and Bubble is a visual app builder for the application layer, with hosting and security built in.

1. Microsoft Foundry: best for governed multi-model development on Microsoft Azure

Microsoft Foundry is Microsoft’s unified platform for building, evaluating, and deploying AI applications. It replaces Azure AI Foundry and still runs within the Azure ecosystem.

Foundry connects to OpenAI models through Azure OpenAI. It also supports Anthropic models, open-source models through the model catalog, and fine-tuning.

For organizations already on Azure, Foundry connects to Microsoft Entra ID for identity management and works with Defender, Purview, Azure Monitor, and API Management for security and observability. Much of the compliance scaffolding you’d otherwise assemble yourself is already covered.

Developer tooling includes Python SDKs and REST APIs, plus GitHub Actions support for AI agent evaluations in preview. Microsoft has also offered Prompt Flow in the Foundry classic portal for LLM workflow development. This area changes quickly, so confirm current tooling and naming before you finalize anything.

The main constraint is the dependency on the Azure ecosystem. Integrating data from outside Azure, like AWS S3 or Google Cloud, takes significant engineering work, and teams unfamiliar with Microsoft’s tooling conventions will need time to ramp up.

Best for:

• Organizations running on Microsoft Azure or Microsoft 365 with data in SharePoint, Azure Blob, or SQL databases. Foundry can reach that data within the identity and security setup you already have.
• Dev teams that need governed access to OpenAI models with enterprise security controls already in place. Azure OpenAI runs under the same Entra ID identity management as the rest of your Azure stack.
• Teams building RAG-powered applications where the knowledge base lives in Microsoft’s ecosystem. Keeping retrieval and data on Azure avoids the extra integration work that non-Azure sources need.
• Organizations with existing Azure DevOps or GitHub Actions pipelines. GitHub Actions support for agent evaluations lets you add AI checks to the CI/CD process you already run.

Limitations:

Foundry is tightly coupled to Azure, so multi-cloud setups and non-Azure data sources need additional integration work.

Pricing:

Consumption-based across the Azure and Microsoft Foundry services and models you use. Check whether any separate platform fees apply to your Foundry configuration.

2. AWS Bedrock: best for private model customization and managed agents on AWS

Amazon Bedrock gives your team API access to foundation models, the large, general-purpose AI models like GPT or Claude that power most AI applications today. AWS runs the infrastructure, including the GPU clusters and model servers. The list of foundation model providers and the models each offers changes often, so check AWS’s documentation for the current list.

Bedrock Agents helps you build multi-step AI agents, and Bedrock Knowledge Bases handles RAG by pulling from sources like S3, Confluence, Salesforce, and SharePoint. You can also fine-tune models on your own proprietary data.

Everything runs inside your AWS account, and private connectivity through PrivateLink keeps inference traffic inside your VPC.

Fine-tuning data stays within the AWS network. It’s encrypted in transit and at rest and protected by AWS access controls. Retention and sharing terms vary by provider, so check them for whichever model you plan to customize.

Bedrock Flows adds a visual workflow builder for linking prompts, agents, and AWS services, but your team still has to build the end-user application on top.

Best for:

• Organizations running on AWS with data in S3, RDS, or other AWS services. Knowledge Bases can pull from those sources without moving data out of your AWS account.
• Dev teams that need VPC-isolated model inference with AWS-native access controls. PrivateLink keeps inference traffic off the public internet.
• Teams building multi-step AI agents with governed access to internal knowledge bases. Bedrock Agents and Knowledge Bases work together, so agents can retrieve from sources your access controls already govern.
• Regulated industries with strict data residency and isolation requirements. Fine-tuning data stays within the AWS network and is encrypted in transit and at rest.

Limitations:

The application layer is your team’s responsibility. Teams new to AWS should expect a ramp-up period.

Pricing:

Pay-per-token for model inference. Knowledge Bases and Agents have separate usage-based costs. There’s no platform subscription fee.

3. Gemini Enterprise Agent Platform: best for BigQuery-native AI apps and Agent Builder

Google’s platform is now Gemini Enterprise Agent Platform. Google renamed it from Vertex AI in April 2026, so you may still see Vertex AI terminology in existing documentation and user references. It includes access to Gemini models, open-source models through Model Garden, and tools for RAG, fine-tuning, and agent creation.

If your source of truth is in BigQuery (Google’s cloud data warehouse), the platform connects to it directly, which helps when you need AI grounded in structured enterprise data. Agent Builder gives teams a simpler path to shipping conversational agents and search applications without deep machine learning (ML) expertise.

The platform includes MLOps tooling for managing the model lifecycle beyond inference. Check that access controls behave as expected for your data setup before you finalize your architecture.

Like the other cloud-native platforms here, it’s built for teams already on GCP. Bringing in data from outside Google Cloud adds complexity, and teams with existing OpenAI dependencies should weigh the cost of switching to Gemini as their primary model provider.

Best for:

• Organizations on Google Cloud with data in BigQuery, Cloud Storage, or Google Workspace. The platform connects natively to those sources, so you avoid cross-cloud integration work.
• Dev teams building search or conversational AI applications grounded in structured enterprise data. Agent Builder lets them ship those applications without deep ML expertise.
• Teams that need MLOps tooling alongside model inference, including pipeline management, feature stores, and vector search. Managing the model lifecycle and inference in one platform reduces the number of tools to govern.
• Organizations using Google Workspace that want AI grounded in Workspace data. Confirm current connector support and permission behavior in the documentation before you plan around it.

Limitations:

The GCP dependency makes multi-cloud setups more complex. Teams evaluating multi-step agent workflows should test Agent Builder directly against their requirements.

Pricing:

Consumption-based. Gemini model inference is priced per token, with separate usage costs for platform services like pipelines and monitoring.

4. Databricks Mosaic AI: best for lakehouse-native RAG and fine-tuning-on proprietary data

Mosaic AI is Databricks’ AI and machine learning toolset, built into its Lakehouse Platform (a combined data warehouse and data lake). Data and ML teams can fine-tune LLMs on proprietary data, build RAG pipelines, and serve models at scale without leaving Databricks.

If your most valuable data lives in Delta Lake (Databricks’ open-source storage format), Mosaic AI integrates with it through Unity Catalog. Unity Catalog governs permissions, auditability, and lineage, so the AI only accesses what it’s allowed to and everything it touches gets logged.

That level of governance matters most in regulated industries where auditors need full visibility. Also check Unity AI Gateway’s rate limiting and credential management for your setup.

Mosaic AI is built for data scientists and ML engineers. Teams can train and deploy custom models, including fine-tuned open-source models, inside Databricks.

Application teams usually pair Databricks with a separate application delivery layer. The platform covers the data and model layers and leaves the UI and user-facing app to another tool.

Best for:

• Organizations with significant data already in Databricks, Delta Lake, or a broader Lakehouse architecture. Mosaic AI works on that data where it sits, under the permissions Unity Catalog already enforces.
• ML engineering and data science teams building custom or fine-tuned models on proprietary datasets. Training, fine-tuning, and serving all happen in the same environment as the data.
• Dev teams that need RAG grounded in governed enterprise data with column- and row-level access controls. Retrieval respects the same access rules your data team already maintains.
• Organizations in regulated industries that need fine-grained audit trails on AI data access. Unity Catalog logs lineage and every access, which gives auditors the visibility they ask for.

Limitations:

It isn’t built for shipping applications. Your team still has to build the application layer separately, and getting value from fine-tuning takes ML engineering skill.

Pricing:

Databricks Unit (DBU) consumption-based pricing with pay-as-you-go options and no upfront costs. Committed-use or enterprise agreements are available for scale, governance, or volume-discount needs.

5. IBM watsonx: best for hybrid and on-premises deployments with strict governance

watsonx is IBM’s AI platform. It has three parts: watsonx.ai for building and fine-tuning models, watsonx.data as the underlying data store, and watsonx.governance for monitoring, auditing, and explaining what your models do in production.

Governance is IBM’s main focus, since regulated industries need to explain AI decisions to auditors. The platform tracks model behavior, evaluates outputs, and keeps documentation ready for an audit.

You can deploy watsonx through IBM Cloud, install it yourself, or run it hybrid or on-premises. That flexibility matters most when your organization can’t move sensitive workloads to the public cloud. Implementation and procurement timelines vary by deployment model, so confirm them with IBM during evaluation.

Best for:

• Large enterprises in regulated industries, including financial services, insurance, government, and healthcare. watsonx.governance produces the model documentation and evaluation records that auditors in those industries expect.
• Organizations that need on-premises or hybrid AI deployment because data can’t leave their controlled environment. You can run the platform where the data already lives.
• Teams that need automated governance and audit documentation built into the platform. Monitoring and documentation run continuously, so audit prep doesn’t turn into a separate project.
• Organizations already using IBM infrastructure or with existing IBM enterprise agreements. Building on existing contracts and environments can shorten procurement.

Limitations:

It’s more complex to implement than cloud-native platforms, and smaller teams may find it more than they need.

Pricing:

Pricing varies by watsonx component and deployment model. IBM offers runtime plans, subscriptions, and enterprise licensing depending on the product and environment.

6. Vellum: best for agent orchestration, evals, and cost-controlled LLM workflows

Vellum helps teams already running LLMs in production use them with more discipline. It gives you one place to manage prompts and see what your AI is doing in real time. It works across AI providers, which lets you switch or compare models without rebuilding your workflows.

Vellum is built around its evaluation framework. You define test cases and run them against new prompt or model versions before anything ships to production. This catches regressions, where a change quietly makes your AI’s answers worse, before users see them.

Vellum also includes a workflow builder for designing multi-step LLM pipelines, with versioning and rollback if something goes wrong.

Vellum doesn’t build your app. It covers prompts, models, and evaluations, and leaves the frontend, database, and deployment to other tools. Most teams pair it with their existing engineering setup.

Best for:

• Dev teams already running LLMs in production that need structured prompt versioning, evaluations, and rollback. Test cases run against every new version before it ships.
• Product teams managing multiple AI features across a codebase that need observability and cost controls at the workflow level. One view shows what each workflow is doing and what it costs.
• Organizations with a multi-model strategy that want to route between providers without re-engineering application code. Comparing models becomes a configuration change.
• Teams building RAG pipelines that need to test retrieval quality before deploying changes. Evaluations catch a drop in answer quality before users notice it.

Limitations:

Vellum isn’t a full application platform. You’ll still need to build the frontend, manage your own database, and handle hosting separately.

Pricing:

Vellum offers a free starting option and custom paid plans, including enterprise options. Verify current plan limits, usage drivers, and enterprise pricing on Vellum’s pricing page.

7. Bubble: best for AI-powered app development with visual control across web and mobile

Bubble is a visual development platform for building web and native mobile apps with AI. Every layer is fully visual, including the design, logic, data, and privacy rules, so the people responsible for an app can see how it works and change it themselves.

Work starts with a planning session with the Bubble AI Agent. You describe the internal tool or customer-facing app you need, and the Agent asks clarifying questions and drafts a blueprint before it builds anything.

It then generates the pages, the workflows (including backend workflows for scheduled tasks and bulk data processing), and the database with its data types and fields, explaining what it built in plain language. Workflows read as steps in the visual editor, so product and operations people can follow the logic alongside your engineers.

When the app needs to change, you can ask the Agent or edit it by hand. The Agent shows what it’s about to do and summarizes what changed, and everything it touched is visible in the editor.

The Agent can also set up API calls and install plugins directly. Version control lets your team test changes before deploying them and roll back if something isn’t right.

Security is built into the same platform. SOC 2 Type II compliance comes standard, paid plans include hosting and auto-scaling, and SSO is available on the Enterprise plan.

The Agent creates privacy rules automatically as it builds, and you can review and adjust them visually. On paid plans, the security dashboard flags issues while you build, so gaps surface before a security review finds them after launch.

With Bubble for native mobile apps, you build for web and native iOS and Android from one editor with a shared backend. You can connect outside AI services like OpenAI through the API Connector to add chatbots, content generation, or data analysis to a production app.

Organizations including Seagate and CLEBER build on Bubble. Seagate’s internal team shipped the first version of an admin console in two months (February 2024).

CLEBER, Mexico’s largest auto dealer group, saved an estimated $100,000 and launched in three months against an estimated year with traditional development (December 2025). For more on how organizations use Bubble, see Bubble’s 2025 Enterprise Survey.

If your team also needs heavier ML or RAG infrastructure, you can pair Bubble with one of the infrastructure-first platforms covered earlier in this guide.

Best for:

• Enterprise innovation teams, product managers, and builders who want to build internal tools or customer-facing apps with AI and keep full visual control over how they work. The Agent produces a first version your team can review, and the logic stays readable for whoever maintains it next.
• Organizations that want to build for web and native iOS and Android from a single platform with a shared backend. With native mobile app generation (beta), the Agent builds iOS and Android apps from a prompt. See what’s included.
• Organizations that need SOC 2 Type II compliance, visual privacy rules, and SSO on the Enterprise plan without assembling them from separate vendors. Security, hosting, and scaling all come from one platform, which gives InfoSec a single place to review.
• Dev teams integrating AI features (chatbots, content generation, data analysis) into production applications through the API Connector. The Agent can set up the API calls, and your team can inspect each one in the editor.

Limitations:

Bubble isn’t a model-training or managed RAG infrastructure platform. If your team has complex custom ML needs, you’ll want a separate AI infrastructure layer alongside it.

Bubble apps run on Bubble and can’t be exported as source code, so organizations that require full code ownership should weigh that. If you plan to generate native mobile apps with the Agent, check what’s included before committing.

Pricing:

Getting started is free, and the Free plan is for building and testing.

• Paid plans: Billed annually, plans start at $29/month for web only, $42/month for mobile only, and $59/month for web and mobile. Growth, Team, and Enterprise plans are also available.
• Starter Web price change: On October 20, 2026, the Starter Web plan moves to $32/month billed annually or $39/month billed monthly. Builders who buy an annual Starter Web plan before that date keep the $29/month price for a year.
• AI credits: Every plan includes a monthly allowance of AI credits, but building doesn’t depend on them. Generating an app with the Agent and editing in the visual editor don’t use AI credits.
• Workload units: App usage is measured separately in workload units. They track the server work your live app does and are separate from AI credits.
• Enterprise plan: The Enterprise plan offers custom infrastructure, advanced security, scalability, centralized management, dedicated support, and flexibility in hosting location.

Get started for free

Which enterprise AI platform fits your team?

Each of the following scenarios points to a different platform, depending on where your data lives and how much infrastructure you want to own.

Many organizations combine platforms. A common pattern uses Databricks or Bedrock for the AI and data layer and Bubble for the application layer.

How to pair an AI infrastructure platform with an application layer

A common architecture splits the work between two layers:

1. Keep model access, RAG, and governance in the infrastructure layer. That’s where your data, models, and usage policies already live.
2. Build the user interface and business logic on an application delivery platform. This is the layer employees or customers interact with.
3. Connect the two through governed APIs. The infrastructure layer exposes model access and retrieval to apps through APIs it controls.
4. Pass user identity through those APIs. Permission-aware retrieval keeps working, so users only see the data they’re authorized to access.
5. Keep audit logs in each layer. Separate logs let each layer meet its own compliance requirements.

How this works on Bubble

On Bubble, you connect your application layer to Azure OpenAI, Amazon Bedrock, or other model providers through the API Connector. The Agent can set up these API calls, and responses are parsed into usable data automatically, with no manual mapping.

With this setup, your team builds the application layer visually while your existing infrastructure continues to handle model governance.

EZRA, a coaching platform that’s part of Adecco Group, builds coaching apps on Bubble alongside its engineering team, and those apps integrate directly with the company’s Azure platform and Azure OpenAI (June 2025). “Low-code solutions should complement your pro-code solutions. Thinking in a binary way just doesn’t lead to benefit,” says Paul Stamou, VP of engineering at EZRA.

How enterprise AI platforms are priced

Most enterprise AI platforms use one of two pricing models: You pay for what you use, or you sign an agreement upfront. Knowing which one applies helps you forecast costs and avoid a surprise bill later.

Consumption-based pricing charges for what you use. That usually means a per-token fee for model inference, plus hourly or usage-based fees for things like vector databases, data pipelines, and agent orchestration. It works well when you’re starting small, but costs can climb quickly once production traffic reaches your application.

Subscription and enterprise agreements trade some of that flexibility for predictability. You typically commit annually in exchange for dedicated capacity, deeper governance features, and custom infrastructure. For mission-critical workloads, that predictability and the built-in security are often worth more than pay-as-you-go pricing.

Start building with the right enterprise AI platform

Infrastructure-first platforms give you control at the model and data layers, and Vellum covers the AI-layer tooling in between.

For the application layer, Bubble lets your organization build apps with AI without giving up control of how they work. Your team can read the logic, review what the Agent changed before it ships, and roll back when needed.

A good first step is to describe an internal tool to the Agent and review the plan it drafts. Generating an app with the Agent doesn’t use AI credits. Get started for free and see what your team can build.

Bubble. Built for big ideas.

Frequently asked questions about enterprise AI platforms

What counts as an enterprise AI platform vs. a copilot or SDK?

An enterprise AI platform (sometimes called an enterprise AI operating system) gives you one centralized control plane for data access, governance, model orchestration, audit logging, and deployment, built to support many teams at once. A copilot only assists inside a single product, and a raw SDK leaves you to build governance and security yourself.

What are the main enterprise AI platforms available in 2026?

The main infrastructure-first platforms are Microsoft Foundry (formerly Azure AI Foundry), AWS Bedrock, Gemini Enterprise Agent Platform (formerly Vertex AI), Databricks Mosaic AI, and IBM watsonx. Vellum covers AI-layer tooling like prompt management and evaluations, and Bubble is a fully visual app builder for the application layer, with enterprise security controls built in.

What is Google’s enterprise AI platform?

Google’s enterprise AI platform is Gemini Enterprise Agent Platform, which Google renamed from Vertex AI in April 2026. It includes Gemini models, open-source models through Model Garden, tools for RAG, fine-tuning, and agent creation, and native integration with BigQuery and other GCP services.

How much does an enterprise AI platform cost?

Most enterprise AI platforms are either consumption-based, with per-token inference plus usage fees for services like vector databases and agents, or sold through upfront enterprise agreements with committeduse and dedicated capacity. Microsoft Foundry, AWS Bedrock, and Gemini Enterprise Agent Platform are consumption-based, Databricks and IBM watsonx offer both, and Bubble has a Free plan plus paid plans by platform, with AI credits included and app usage measured in workload units.

Can we deploy in our VPC with private networking and data residency?

Yes, several platforms support private or controlled deployment: AWS Bedrock keeps inference inside your VPC through PrivateLink, IBM watsonx runs on-premises or hybrid, and Google Cloud and Databricks offer region-based deployment. For Microsoft Foundry and every other vendor, confirm VPC connectivity, private endpoints, BYOK (bring your own key) encryption, and region isolation during your security review.

How do you connect an AI infrastructure platform to an app?

Most organizations expose model access, RAG, and governance from the infrastructure platform through governed APIs that pass user identity, then build the user-facing app on an application delivery platform. On Bubble, you connect to Azure OpenAI, Amazon Bedrock, or other model providers through the API Connector, and the Agent can set up the API calls.