Gemini Enterprise and Vertex AI: Google’s AI for the Enterprise

In 2025, Google Cloud extends Gemini’s capabilities into the enterprise as a tightly integrated part of Vertex AI and Gemini Enterprise. The goal is to move generative AI from a sandbox experiment to production-grade intelligence that can reason across data types, ground outputs in trusted sources, and orchestrate multi-step workflows with safety and governance baked in. This article unpacks how Gemini 3 and the Gemini API are deployed at scale in Vertex AI, explores core capabilities like adapter-based tuning, grounding, and function calling, and provides a practical playbook for enterprise teams aiming to win with AI at scale.

Overview: Gemini 3, Gemini API, and Vertex AI

Gemini represents Google’s family of foundation models designed for broad enterprise use cases—from multimodal reasoning and complex planning to robust tool use and coding assistance. Gemini 3, the latest generation, emphasizes advanced multimodal understanding, deep reasoning, and agentic capabilities that let it interact with data, tools, and systems in a controlled, auditable way. In enterprise contexts, Gemini is surfaced through Vertex AI and Gemini Enterprise, giving teams a single plane of control for model selection, tuning, grounding, and governance. The combination aims to reduce time-to-value for internal use cases such as automated compliance reviews, product design prototyping, customer support automation, and data-driven decision support.

From the vendor side, Google highlights three practical angles that matter in production:

• Advanced tool use and planning: Gemini 3 can plan multi-step actions, call external tools, and chain tasks across enterprise systems. This makes it suitable for complex workflows like procurement approvals, contract analysis, and operational planning.
• Multimodal reasoning: The model can process and synthesize information across text, images, video, audio, and structured data, enabling richer insights from diverse datasets.
• Enterprise-grounded outputs: Grounding features connect AI outputs to trusted data sources (web, internal documents, and third-party data) to improve factuality and reduce hallucinations.

These capabilities are reinforced by Vertex AI’s enterprise-focused features, such as private networking, encryption at rest with customer-managed keys, configurable resource controls, and HIPAA-ready workloads. For teams building AI agents, Vertex AI Agent Engine and the new Vertex AI Agent Builder provide pathways to deploy and manage agents with observability, memory management, and IAM-based access control. The rapid cadence of 2025 release notes demonstrates ongoing improvements to grounding, function calling, and lifecycle tooling across Gemini models in Vertex AI. (cloud.google.com)

Key capabilities for production: Adapter-based tuning, grounding, and function calling

Enterprises want models that can be molded to specific domains, stay aligned with brand and policy, and operate reliably within a broader IT ecosystem. Google’s Gemini on Vertex AI supports several capabilities designed to deliver that outcome:

• Adapter-based tuning (parameter-efficient tuning): Rather than retraining the entire model, adapters insert small, trainable modules into a frozen base model. This enables domain adaptation with lower compute, faster iteration, and tighter control over costs. It pairs with various tuning modes (supervised tuning, preference tuning, and adapter sizing) to tailor outputs for your data and use cases. In Vertex AI, adapter tuning is documented as a practical approach to customizing Gemini with modest data footprints and clear governance. (cloud.google.com)
• Grounding: Grounding enhances factuality by anchoring outputs to trusted sources, such as Google Search data or customer data, and even specialized third-party data. This is a core enterprise requirement to reduce hallucinations and increase confidence in model results. Grounding with Google Search is generally available in Vertex AI, and the enterprise-ready grounding service is being expanded to support third-party data sources for domain-specific scenarios. (cloud.google.com)
• Function calling: Function calling enables the model to trigger concrete actions within a business process—such as launching an approval workflow, querying a CRM, or invoking a data pipeline—rather than returning only text. The Vertex AI release notes consistently highlight improvements to function calling and expert-mode capabilities, which are critical for production-grade AI agents. (cloud.google.com)
• Code execution and tool use: Gemini’s API supports tool interaction and code execution within conversations, enabling agents to perform tasks that require reasoning with live data or code. This is particularly valuable for software engineering workflows, data analysis, and automated document processing. (cloud.google.com)

In practice, many enterprises start with a baseline Gemini model and progressively layer adapters, grounding, and tools. As teams mature, they add capabilities such as memory, session-level observability, and structured evaluation to maintain safety, compliance, and performance over time. The 2025 Vertex AI release notes shed light on ongoing enhancements to agent engine observability, memory management, and lifecycle tooling that help ops teams stay in control at scale. (docs.cloud.google.com)

Practical note: For teams evaluating tuning options, supervised fine-tuning and adapter sizing are described in Vertex AI docs, with clear trade-offs between cost, latency, and model fidelity. Preference tuning complements supervised tuning by incorporating human feedback to shape model behavior over time. These capabilities are explicitly outlined in Vertex AI model tuning guides. (docs.cloud.google.com)

Deployment patterns on Vertex AI: Architecture, tooling, and best practices

Behind every enterprise AI deployment is an architectural blueprint that blends model capabilities with data governance, security, and operations. A practical blueprint for Gemini on Vertex AI typically includes:

• A centralized model hosting layer on Vertex AI, with Gemini 3 or other Gemini variants registered in Model Garden or Model Registry, accessible via endpoints or managed API calls. Enterprises often provision high-reliability endpoints with configured autoscaling, concurrency controls, and memory management policies. The Vertex AI release notes emphasize agent-based architecture and observability features to monitor performance and reliability in production. (cloud.google.com)
• Grounding and data integration: Start with grounding to external sources (Google Search, internal knowledge bases, and connected third-party data). This requires data connectors, data security attestations, and a governance layer to control which data sources are allowed for grounding. The enterprise grounding capability was highlighted as a cornerstone of “enterprise truth” in Vertex AI materials. (cloud.google.com)
• Tool orchestration and memory: Agents can orchestrate multi-step workflows across systems, with memory components to retain context across sessions. The Vertex AI Agent Engine provides observability and memory management features to support long-running agent tasks, while the release notes detail observability enhancements and memory revisions as part of lifecycle management. (docs.cloud.google.com)
• Security and compliance: Enterprise deployments leverage Private Service Connect, private VPCs, CMEK for data at rest, and HIPAA-ready workloads where applicable. These capabilities are repeatedly highlighted in Vertex AI enterprise updates and product pages, reflecting Google Cloud’s emphasis on governance and security for production AI. (cloud.google.com)

From a governance perspective, it’s prudent to separate development, testing, and production environments, implement access control with IAM, and establish a predictable cost model (throughput provisioning, autoscaling limits, and memory usage controls). The Vertex AI release notes from 2025 demonstrate a clear trajectory toward more predictable capacity planning and safer production runtimes, which is essential for enterprise adoption. (cloud.google.com)

Deployment patterns: concrete considerations and best practices

• Start with a minimal viable grounding layer: Ground Gemini outputs against a trusted data source (for example, a company knowledge base or a curated web ground). This helps reduce risk while you iterate on prompts and tool usage. Monitor the quality of grounded responses and adjust the grounding data scope as needed.
• Use parameter-efficient tuning (adapters) for domain-specific tasks: If your team’s use case involves classifying, extracting, or summarizing domain-specific content (legal, financial, clinical), adapters allow you to tailor the model without incurring prohibitive compute costs. When you scale, consider a phased approach to increase adapter size and complexity only after baseline performance proves stable. (cloud.google.com)
• Leverage function calling for orchestration: Treat the model as a decision engine that can trigger downstream systems—CRM lookups, workflow approvals, or data pipelines—rather than a standalone text generator. Architect endpoints to receive function outputs, apply audit trails, and surface actionable results in your business apps. (cloud.google.com)
• Plan for observability from day one: Instrument latency, throughput, error rates (including 429s), and memory usage. Vertex AI’s dashboards and the Gen AI evaluation services provide the lenses you need to detect drift, latency spikes, or governance gaps early. (docs.cloud.google.com)
• Security by design: Use Private Service Connect, CMEK, and IAM-based access to control who can deploy and query Gemini endpoints. HIPAA-ready configurations should be considered where regulated data is involved. These capabilities are part of the enterprise feature set described in Vertex AI release notes and product pages. (cloud.google.com)

Code samples and quickstarts in Google’s Gemini API documentation illustrate how developers can programmatically interact with Gemini via Vertex AI, including code execution tools and end-to-end API calls. These serve as practical starting points for teams building internal automation and agent-driven applications. (cloud.google.com)

Governance, security, and cost considerations for enterprise AI

Adopting Gemini in production requires disciplined governance and cost management.

• Cost controls: Throughput provisioning and the option to cap resource usage are part of Vertex AI’s enterprise tooling, helping organizations scale AI workloads without runaway spend. The release notes highlight provisioned throughput as a scalable and predictable mechanism for Gemini workloads. (docs.cloud.google.com)
• Data governance and data residency: Private networking, private service connect, and CMEK are standard features in enterprise-level Vertex AI deployments. Enterprises can align AI usage with data residency policies and compliance requirements, including HIPAA, where applicable. (cloud.google.com)
• Grounding and safety: Grounding data sources reduces hallucinations and improves confidence in model outputs, which is crucial for regulated domains like finance, healthcare, or legal. The enterprise grounding story emphasizes combining Google Search grounding with proprietary data and third-party datasets to improve factuality and trust. (cloud.google.com)
• Lifecycle management: Agents and prompts evolve; memory and versioning features let teams manage updates safely. Observability tooling, session management, and memory revisions are part of Vertex AI’s ongoing enhancements to support enterprise lifecycles. (docs.cloud.google.com)

In short, production-grade Gemini on Vertex AI is not just about the model—it’s about a robust platform stack that covers tuning, grounding, tool use, governance, and operations in a repeatable, auditable way.

Observability, lifecycle management, and ongoing optimization

Operational discipline is a first-order requirement for enterprise AI. Google’s 2025 updates emphasize:

• Observability: Sessions, traces, logs, and events for AI agents are now accessible in the Google Cloud console, enabling teams to monitor agent health and behavior across long-running tasks. This is essential for auditability and performance tuning in production. (docs.cloud.google.com)
• Memory management and evaluation: Memory revisions and Gen AI evaluation services provide structured ways to test and compare agent configurations, enabling safer, faster iteration cycles. (docs.cloud.google.com)
• Compliance-ready runtimes: Express mode, token throughput controls, and memory handling options help ops teams apply strict governance while maintaining responsiveness. (docs.cloud.google.com)

From a practical standpoint, teams should implement a governance model that includes:

• Clear ownership for data sources used in grounding.
• Auditable prompts and tool calls with change history.
• Regular evaluation cycles to detect drift in model outputs or tool behavior.
• A cost-accountability framework that ties usage to business outcomes (e.g., reduced cycle times, higher customer satisfaction, or improved risk management).

Adoption playbook: Practical steps to start with Gemini in an enterprise

1. Define the business outcomes and success metrics: Start with a specific workflow (for example, automated contract analysis or multi-channel customer support) and define measurable goals (accuracy, cycle time, cost per interaction).
2. Pick a grounding strategy: Decide whether to ground to internal documents, Google Search, or third-party data providers. Start small and iterate to quantify value.
3. Choose a tuning approach: For early pilots, begin with adapter-based tuning to adapt to your domain with modest data. If you have a labeled dataset and a strong need for domain precision, consider supervised fine-tuning in Vertex AI. As your data grows, you can explore preference tuning to capture user preferences. (cloud.google.com)
4. Implement tool calls carefully: Model-driven orchestration should be designed to trigger deterministic downstream actions with fallback handling, retries, and clear audit logs.
5. Build governance into the pipeline: IAM-based access, data policies, and monitoring dashboards should be part of the initial design rather than afterthoughts.
6. Instrument and iterate: Use Vertex AI’s observability tools to monitor latency, throughput, and 429 errors, and run regular evaluations to detect drift and regressions. (docs.cloud.google.com)
7. Plan for scale and security: As you move from pilot to production, enable private networking, CMEK, and HIPAA-compliant workloads where applicable. These capabilities are central to enterprise deployments on Vertex AI. (cloud.google.com)

Below are a few concrete code examples to illustrate practical integration patterns. These are representative starting points designed to be adapted to your environment and data:

Appendix: code examples and practical guidance

Example 1: Quick Gemini API call via Vertex AI quickstart (Python)

# Purpose: send a multimodal prompt to Gemini via Vertex AI's Gemini API (Python example)
# This example uses a high-level client approach; replace IDs with your own project/endpoint.
from google.cloud import aiplatform

# Initialize client and resources
project_id = "YOUR_PROJECT_ID"
location = "YOUR_REGION"  # e.g., us-central1
endpoint_id = "YOUR_ENDPOINT_ID"
endpoint = f"projects/{project_id}/locations/{location}/endpoints/{endpoint_id}"

data = {
    "instances": [{"content": "Summarize the attached quarterly report and extract key risks."}],
    "parameters": {"temperature": 0.2, "max_output_tokens": 512}
}

client = aiplatform.gapic.PredictionServiceClient()
response = client.Predict(name=endpoint, instances=data["instances"], parameters=data["parameters"])
print(response)

This pattern demonstrates the typical structure: target an endpoint, pass instances (user content), and tune generation with parameters. Adapt to your endpoint naming and data format for your use case.

Example 2: Gemini API quickstart with Python SDK (code execution and tool use)

# Purpose: run a small reasoning task with code execution enabled (Gemini API quickstart style)
from google import genai
from google.genai.types import HttpOptions, Tool, ToolCodeExecution, GenerateContentConfig

client = genai.Client(http_options=HttpOptions(api_version="v1"))
model_id = "gemini-2.5-flash"  # or a newer enterprise model

code_execution_tool = Tool(code_execution=ToolCodeExecution())
response = client.models.generate_content(
    model=model_id,
    contents="Compute the 15th Fibonacci number and then return its prime factors.",
    config=GenerateContentConfig(tools=[code_execution_tool], temperature=0.3)
)
print(response)

This sample shows how to leverage built-in code execution or tool-calling capabilities to enhance reasoning tasks, a common pattern for internal automation workflows.

Example 3: Tuning a Gemini model with adapters (curl-style conceptual pattern)

# Purpose: submit a supervised-tuning job using adapters for a domain-specific task
curl -X POST \
  -H "Authorization: Bearer $(gcloud auth print-access-token)" \
  -H "Content-Type: application/json" \
  https://TUNING_JOB_REGION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/TUNING_REGION/tuningJobs \
  -d '{
    "baseModel": "gemini-2.5-pro-002",
    "adapter_size": "ADAPTER_SIZE_EIGHT",
    "epoch_count": 5,
    "tuned_model_display_name": "acme-domain-gemini-adapter-8",
    "validationDataConfig": {
      "gcsSource": {
        "uris": ["gs://your-bucket/tune/validation.json"]
      }
    }
  }'

Notes:

• Adapter size options (1, 4, 8, 16) represent the trainable parameter subset and trade off data needs and compute. This curl pattern reflects the tuning API structure described in Vertex AI documentation. Adjust endpoints, dataset URIs, and model identifiers to match your environment.

If you’re just getting started, the Gemini quickstart and tuning docs provide concrete references and code you can adapt quickly to your stack. The key is to start small (a single workflow, one data source, one grounding source) and build up to integrated agent architectures that span data, tools, and user interactions. (cloud.google.com)

Appendix: image prompts (for hero visuals)

1. A corporate operations center at 16:9, with multiple large displays showing real-time dashboards, Gemini and Vertex AI logos, and data-flow diagrams that imply grounding, tuning, and tool calls in an enterprise context. The room is modern, blue-toned, and conveys calm, controlled AI governance.
2. An architecture diagram in a 16:9 frame: Gemini models at the top, connected to Vertex AI on the left, data sources (Google Search, internal data lakes, third-party data) on the right, and downstream tools (CRM, ERP, document processing) at the bottom, with grounding lines in bright accent colors to show data flow and governance boundaries.
3. A cross-functional AI studio scene: engineers, lawyers, and product managers collaborating around a holographic table that displays prompts, adapters, and tool-call sequences, with a transparent overlay of policy compliance checkmarks and risk lanes.

FAQs

1. What is Gemini Enterprise and how does it relate to Vertex AI?

• Gemini Enterprise is Google’s enterprise-focused deployment of the Gemini family of models, integrated with Vertex AI to provide a unified platform for model hosting, grounding, tuning, tool calls, and governance. Enterprises can run Gemini models in secure, managed endpoints with enterprise-grade security, observability, and data governance.

2. What is adapter-based tuning (adapter tuning) and why is it useful in production?

• Adapter tuning is a memory-efficient, parameter-efficient way to tailor a large model to a domain-specific task by injecting small trainable modules (adapters) into a frozen base model. It reduces the compute and data requirements of full fine-tuning while delivering domain-relevant behavior, making it especially attractive for regulated industries and teams with limited labeled data.

3. How does grounding improve model outputs for enterprise use cases?

• Grounding anchors model outputs to verifiable sources, such as Google Search results, internal knowledge bases, and trusted third-party data, reducing hallucinations and increasing the reliability of the responses. Enterprises often combine multiple grounding sources to support diverse workflows, from customer support to scientific research.

4. What governance and security controls should I expect when deploying Gemini in Vertex AI?

• Expect private networking options (e.g., Private Service Connect), customer-managed encryption keys (CMEK) for data at rest, IAM-based access controls, and the ability to run workloads in HIPAA-compliant environments when appropriate. Release notes and product pages emphasize these controls as foundational for production-grade deployments. (cloud.google.com)

5. How do I start an enterprise project with Gemini on Vertex AI?

• Start with a narrowly scoped pilot that includes grounding a single data source, a small adapter-tuned model, and a well-defined automation workflow. Use Vertex AI’s observability dashboards to monitor latency, throughput, and tool-calling outcomes, then iterate by expanding adapters, refining grounding, and increasing governance coverage as you scale. (docs.cloud.google.com)