Table of Contents
- The Enterprise Agent Platform Landscape in 2026
- Microsoft Foundry: The Azure-Native Agent Factory
- Amazon Bedrock AgentCore: AWS’s Agentic Orchestration Layer
- Gemini Enterprise: Google’s Unified AI Platform
- Head-to-Head Comparison: Identity, Governance, and Model Choice
- Pricing Models and the Hidden Cost of Egress
- Lock-In Risk and Multi-Cloud Realities
- Decision Table: Which Platform Fits Your Cloud Commitment?
- How PADISO Helps Mid-Market and PE-Backed Teams Ship Agentic AI
- Next Steps: From Platform Selection to Production ROI
The Enterprise Agent Platform Landscape in 2026
Mid-market CEOs, private equity operating partners, and heads of engineering are all asking the same question: which hyperscaler agent platform should we bet on? The answer matters because the choice locks in your identity fabric, governance model, data gravity, and ultimately your cost structure for years. Three platforms dominate the conversation: Microsoft Foundry (the renamed and expanded successor to Azure AI Foundry), Amazon Bedrock AgentCore, and Google Gemini Enterprise (which absorbed Vertex AI at Cloud Next in April 2026).
Each platform promises to turn foundation models into production-grade agents that reason, retrieve, act, and stay within guardrails. But they differ sharply in how they handle enterprise identity, multi-model routing, egress fees, and the balance between managed convenience and cloud lock-in. This guide is written for operators who need to ship agentic AI that drives measurable ROI—not just run a proof of concept. If you’re leading a mid-market firm, a PE roll-up, or a scale-up, you’ll find actionable comparisons, a decision table by existing cloud commitment, and the real pricing and lock-in trade-offs you won’t see in vendor whitepapers.
At PADISO, we’ve helped over 50 businesses generate $100M+ in revenue through strategic AI implementation and technology leadership. Our fractional CTO engagements and AI advisory services have put us in the trenches with teams choosing between Foundry, Bedrock AgentCore, and Gemini Enterprise. This article is the procurement comparison we wish every buyer had—written in the buyer’s own vocabulary.
Microsoft Foundry: The Azure-Native Agent Factory
Microsoft Foundry is Microsoft’s unified platform for building, deploying, and managing AI agents at enterprise scale. It’s deeply integrated with Azure Active Directory (now Microsoft Entra ID), Purview for data governance, and the entire Microsoft 365 ecosystem. For organizations already running on Azure, Foundry feels like a natural extension of their existing identity and compliance posture.
Identity and Governance
Foundry inherits Azure’s role-based access control (RBAC) and conditional access policies out of the box. Every agent action is tied to a managed identity, which means security teams can enforce least-privilege access using the same tooling they use for the rest of the estate. This is a decisive advantage for mid-market firms pursuing SOC 2 or ISO 27001 audit-readiness. Microsoft Purview provides data classification and lineage, so agents operating on sensitive documents can be audited down to the row level.
Model Choice and the Claude 5 Family
Foundry offers one of the broadest model catalogues in the market. You get first-class access to the Claude 5 family—Opus 5 and Sonnet 5 with 1M-token context windows, Fable 5 as the most capable widely released model, and Haiku 4.5 for cost-sensitive, high-throughput tasks. Microsoft also serves GPT-5.6 Sol and Terra, Gemini 3, and a curated set of open-weight models. This breadth lets you avoid single-model dependency: you can route cost-sensitive inference to Haiku 4.5 while reserving Opus 5 for complex reasoning chains that require long-horizon memory. For teams still running Opus 4.8 or Sonnet 4.6 for migration or cost-tiering reasons, Foundry maintains those endpoints as well.
Agent Orchestration and Tool Use
Foundry’s agent framework is built on top of the Azure AI Agent Service, which provides stateful conversation management, grounding on enterprise data via Azure AI Search, and a growing library of pre-built connectors to Dynamics 365, SharePoint, and the Power Platform. If your agentic workflow needs to interact with ERP data or CRM records, Foundry’s first-party integrations can cut months of custom development.
The Lock-In Trade-Off
Foundry’s tight coupling with the Azure control plane is both its greatest strength and its biggest risk. Moving an agent that depends on Purview, Entra ID, and Azure AI Search to another cloud is non-trivial. You’re not just moving a model endpoint; you’re re-architecting the entire trust boundary. For PE-backed companies that plan to hold assets for 3–5 years, this may be perfectly acceptable. For those that need multi-cloud portability, it’s a serious consideration.
Amazon Bedrock AgentCore: AWS’s Agentic Orchestration Layer
AWS Bedrock AgentCore is the agentic extension of Amazon Bedrock, designed to orchestrate multi-step reasoning, API calls, and knowledge base retrieval with minimal plumbing. It’s the newest of the three platforms, and AWS is leaning hard into its serverless, pay-per-use model to win over engineering teams that value operational simplicity.
Identity and Governance
AgentCore integrates with AWS IAM and AWS Organizations, which means every agent action is governed by the same fine-grained policies that control your S3 buckets and Lambda functions. For teams already running on AWS, this is a straightforward extension of existing security practices. However, the governance tooling for data classification and agent audit trails is less mature than Microsoft’s Purview—AWS Macie and CloudTrail fill some gaps, but stitching together a unified compliance narrative requires more custom work. If you’re pursuing SOC 2 or ISO 27001 audit-readiness via Vanta, the AWS integration is solid, but you’ll need to ensure your agent-specific controls are documented and tested.
Model Choice and the Claude 5 Family
Bedrock AgentCore provides native access to the full Claude 5 lineup—Opus 5, Sonnet 5, Fable 5, and Haiku 4.5—alongside models from AI21 Labs, Cohere, Meta, and Stability AI. AWS does not offer GPT-5.6 Sol/Terra or Gemini 3 natively, though you can always call external endpoints at the cost of added latency and egress. For teams that want to build on Claude as their primary reasoning engine, Bedrock AgentCore is arguably the most frictionless path: the integration is deep, the latency is low, and the pricing is transparent.
Serverless Agent Architecture
AgentCore’s serverless model means you don’t provision infrastructure. You define an agent, attach a knowledge base (backed by Amazon OpenSearch or Aurora), and specify action groups that map to Lambda functions. The platform handles state management, retries, and conversation history. This is a powerful abstraction for teams that want to ship fast without managing stateful infrastructure. However, debugging agent behavior across multiple action groups can be challenging—observability is still catching up to the complexity of agentic workflows.
Egress and Data Gravity
AWS charges for data egress, and agent traffic that pulls from on-premises or cross-cloud data sources will incur costs. If your agent’s knowledge base lives in S3 and your action groups call internal APIs within the same region, egress is minimal. But if your architecture spans clouds—say, your CRM is on Azure and your agent is on Bedrock—egress fees can become a meaningful line item. We’ve helped platform development teams in Seattle and San Francisco model these costs before committing to an architecture.
Gemini Enterprise: Google’s Unified AI Platform
Gemini Enterprise is Google Cloud’s answer to the enterprise agent platform race. It absorbed Vertex AI in 2026, creating a single surface for model access, agent building, and enterprise data grounding. Google’s differentiator is its native integration with Google Workspace and its strength in large-context reasoning—Gemini 3 models routinely handle 1M-token contexts, matching Claude Opus 5 and Sonnet 5.
Identity and Governance
Gemini Enterprise uses Google Cloud IAM, which integrates with Cloud Identity and supports SAML-based federation with external identity providers. For organizations already using Google Workspace, the identity story is seamless. Governance is handled through Cloud DLP, which can inspect and redact sensitive data flowing through agents, and Cloud Audit Logs, which provide an immutable record of agent actions. The compliance coverage is broad—Google Cloud holds over 100 certifications—but the tooling feels less opinionated than Microsoft’s Purview, which can be an advantage or a gap depending on your team’s maturity.
Model Choice and the Claude 5 Family
Gemini Enterprise offers native access to the Gemini 3 family, plus Claude Opus 5, Sonnet 5, Fable 5, and Haiku 4.5 via Google Cloud’s Model Garden. It also supports open-weight models like Kimi K3 and Llama derivatives. Google’s strength is its first-party Gemini 3 models, which excel at multimodal reasoning across text, images, and code. For teams that want to build agents that can reason over long documents, images, and structured data in a single prompt, Gemini 3 is a compelling choice. The Gemini Enterprise resources page provides detailed setup guides and reference architectures.
Pricing Model and Seat Licensing
Gemini Enterprise introduces a seat-based pricing model for Workspace-integrated agent experiences. As of this writing, publicly quoted pricing starts at $30 per user per month—but always confirm with a live quote, as enterprise discounts and volume commitments can shift the number significantly. For API-based agent usage, Google charges per token, with discounts for provisioned throughput. Egress fees apply when data leaves Google’s network, though Google’s global fiber backbone can reduce latency for multi-region deployments.
The Google Ecosystem Advantage
If your organization lives in Google Workspace—Gmail, Drive, Docs, Sheets—Gemini Enterprise can ground agents directly in that corpus. An agent that drafts responses to RFP questions by pulling from Drive documents and Sheets data, then routes for human approval, is a workflow that takes weeks to build on other platforms but days on Gemini Enterprise. For mid-market firms that run on Google Workspace, this is a significant accelerant.
Head-to-Head Comparison: Identity, Governance, and Model Choice
When you strip away the marketing, the three platforms compete on a handful of dimensions that matter to operators. Here’s how they stack up.
Identity Fabric
- Microsoft Foundry: Entra ID native. Conditional access, PIM, and RBAC are first-class. Ideal if you’re already an Azure AD shop.
- Bedrock AgentCore: IAM native. Fine-grained but requires more custom policy work for agent-specific boundaries.
- Gemini Enterprise: Cloud IAM with Workspace federation. Strong if you’re a Google shop; otherwise, you’ll bridge via SAML.
Governance and Audit Readiness
- Foundry: Purview + Compliance Manager. Best-in-class for data classification and audit trails. If you’re preparing for a security audit, Foundry gives you the most evidence out of the box.
- AgentCore: CloudTrail + Macie + Config. Auditable, but you’ll stitch the narrative yourself. Works well with Vanta’s AWS integration.
- Gemini Enterprise: Cloud DLP + Audit Logs. Solid coverage, but less prescriptive. You’ll need to define your own controls framework.
Model Choice Breadth
- Foundry: Widest catalogue. Claude 5 family, GPT-5.6 Sol/Terra, Gemini 3, open-weight models. Best for multi-model strategies.
- AgentCore: Strong Claude 5 support, plus AI21, Cohere, Meta. No GPT-5.6 or Gemini 3 natively.
- Gemini Enterprise: Gemini 3 first, Claude 5 family second, open-weight models available. Best for Gemini-first shops.
Pricing Models and the Hidden Cost of Egress
Pricing is where the procurement conversation gets real. All three platforms charge for token consumption, but the total cost of ownership diverges sharply when you factor in egress, seat licensing, and the cost of idle provisioned throughput.
Token Pricing
- Foundry: Pay-as-you-go per token, with provisioned throughput units (PTUs) available for predictable workloads. PTUs can reduce cost by 40–60% but require 1–3 year commitments.
- AgentCore: On-demand per token, with no upfront commitments. AWS recently introduced inference profiles that let you reserve throughput at a discount.
- Gemini Enterprise: Per-token for API usage, plus a seat-based license ($30/user/month, verify live) for Workspace-integrated agents. Provisioned throughput is available via Google’s Commitment Use Discounts.
Egress: The Silent Budget Killer
Egress fees are the most overlooked cost in agent platform selection. If your agent’s knowledge base sits in Azure and you’re calling Bedrock AgentCore, you’ll pay Azure egress and AWS ingress. If your action groups call APIs across clouds, every request adds up. For one PE-backed platform development engagement in Dallas, we modeled egress costs that would have consumed 12% of the annual AI budget if the team had chosen a cross-cloud architecture. The lesson: co-locate your agent platform with your data.
Lock-In vs. Flexibility
- Foundry: Highest lock-in, lowest operational overhead. If you’re all-in on Azure, this is the path of least resistance.
- AgentCore: Moderate lock-in. Portability is better than Foundry, but AWS-specific agent constructs (action groups, knowledge bases) aren’t portable to other clouds.
- Gemini Enterprise: Moderate lock-in, with the added nuance of Workspace entanglement. If you ground agents in Drive and Docs, moving off Google becomes a migration project.
Lock-In Risk and Multi-Cloud Realities
Private equity firms running roll-ups and portfolio value creation initiatives often inherit a patchwork of cloud commitments. One portfolio company runs on Azure, another on AWS, and a third on Google Cloud. Forcing a single platform across the portfolio can destroy value if it delays time-to-ship or inflates migration costs. The better approach is to select the platform that aligns with each company’s existing data gravity, then invest in a common governance and observability layer that works across clouds.
At PADISO, we’ve architected multi-cloud agent deployments that use a thin abstraction over the model API, keeping the business logic portable while accepting some platform-specific integration for identity and data access. This is the pragmatic middle ground: you accept lock-in where it reduces operational toil (identity, data grounding) and avoid it where it doesn’t add value (model routing, prompt templates).
For teams building platforms in Washington, D.C. with FedRAMP-aware requirements, lock-in is often a feature, not a bug—regulatory compliance demands tight coupling with a single, accredited environment. For commercial mid-market firms, the calculus is different: you want the freedom to switch models and negotiate pricing without a multi-month re-platforming effort.
Decision Table: Which Platform Fits Your Cloud Commitment?
| Existing Cloud Commitment | Recommended Platform | Why |
|---|---|---|
| Azure / Microsoft 365 | Microsoft Foundry | Entra ID, Purview, and Power Platform integrations accelerate time-to-value. Lock-in is acceptable if you’re already committed to Azure. |
| AWS | Bedrock AgentCore | Serverless model, deep Claude 5 integration, and IAM-native governance. Egress is minimal if your data is in S3 and your APIs are in the same region. |
| Google Cloud / Workspace | Gemini Enterprise | Native Workspace grounding and Gemini 3’s multimodal strengths. Seat licensing can be cost-effective for knowledge-worker agents. |
| Multi-cloud or undecided | Bedrock AgentCore (for portability) or Foundry (for governance) | AgentCore’s serverless abstraction is easier to wrap with a portable layer. Foundry wins if governance and compliance are the top concerns. |
| PE roll-up with mixed estates | Platform-by-platform, with a common observability layer | Don’t force consolidation. Ship value at each company on its native cloud, then layer in cross-portfolio governance as a Phase 2 initiative. |
This table is a starting point, not a final answer. Every decision should be stress-tested against your specific data gravity, compliance requirements, and the models you need. Our fractional CTO advisory in New York and Sydney often includes a 2–3 week platform selection sprint that ends with a written recommendation and a 12-month cost model.
How PADISO Helps Mid-Market and PE-Backed Teams Ship Agentic AI
Choosing a platform is only the first step. The real work is building agents that deliver measurable ROI—reducing cost, lifting EBITDA, accelerating time-to-ship, or passing an audit. That’s where PADISO’s CTO as a Service and Venture Architecture & Transformation engagements come in.
We work with mid-market brands, scale-ups, and private equity portfolios across the US, Canada, and Australia. Our engagements typically fall into three patterns:
- Agentic AI Ship: We design, build, and deploy production agents on Foundry, Bedrock AgentCore, or Gemini Enterprise in 6–12 weeks. This includes prompt engineering, tool integration, evaluation pipelines, and cost monitoring. If you’re a PE firm looking to inject AI-driven efficiency into a newly acquired company, this is the fastest path to a tangible EBITDA lift.
- Fractional CTO for Platform Selection: We embed a senior technology leader who owns the vendor evaluation, architecture decision, and build-vs-buy analysis. This is ideal for mid-market CEOs who need a fractional CTO to lead the AI transformation without the $300K+ fully loaded cost of a full-time hire.
- Security Audit Readiness: We use Vanta to get your agent platform and supporting infrastructure to SOC 2 or ISO 27001 audit-readiness in weeks, not months. This is critical when enterprise deals hinge on compliance.
Our case studies show what this looks like in practice: a financial services firm in Sydney that shipped an agentic compliance bot on Bedrock AgentCore, passing APRA CPS 234 audit-readiness in 8 weeks; a PE-backed logistics company in Dallas that consolidated three legacy platforms onto Microsoft Foundry and cut infrastructure costs by 34% while launching a customer-facing agent; a San Francisco scale-up that built a multi-tenant SaaS analytics platform with embedded Superset and ClickHouse, replacing per-seat BI and opening a new revenue stream.
We’re not a traditional consultancy that leaves you with a deck. We’re a founder-led venture studio that ships. When you engage PADISO, you get Kevin Kasaei and a team of senior operators who have built and exited companies, not just advised them.
Next Steps: From Platform Selection to Production ROI
Don’t let platform selection become a 6-month analysis-paralysis exercise. The market is moving too fast. Here’s a concrete plan:
- Map your data gravity: Where do your databases, documents, and APIs live today? That’s the strongest signal for platform choice.
- Define your agent’s trust boundary: What data will the agent access, and what compliance regime applies? This determines how much governance tooling you need out of the box.
- Model the total cost: Include tokens, provisioned throughput, egress, and the engineering time required to integrate identity and monitoring. Don’t just compare per-token prices.
- Run a 2-week spike: Pick your leading platform candidate and build a real agent that touches production data (with appropriate guardrails). Measure latency, cost, and developer experience.
- Engage a fractional CTO if you don’t have the in-house expertise to run the spike and negotiate enterprise agreements. The cost of a wrong decision—measured in migration, egress, and missed revenue—dwarfs the cost of expert guidance.
PADISO has helped platform development teams across Australia, the US, and Canada navigate this exact decision. Whether you’re a mid-market CEO evaluating your first enterprise agent platform, a PE operating partner planning a portfolio-wide AI rollout, or a scale-up CTO who needs an experienced sounding board, we’re ready to help you ship.
Book a call to discuss your agent platform strategy. We’ll bring the decision framework, the cost models, and the hands-on engineering capability to turn your AI investment into measurable ROI.