AI Agent Platforms Compared: Gemini Enterprise Agent Platform, Microsoft Foundry, Amazon Bedrock AgentCore, LangSmith Deployment, CrewAI AMP Suite
The AI agent platform market has split into hyperscaler-native offerings and framework-native platforms — and several of them were renamed or retired in the last year. This is the engineering comparison — lock-in, cost drivers, production readiness, and build-vs-buy rubric — with platform names and status checked against each vendor's documentation on 1 November 2026.
The enterprise AI agent platform market in 2026 has two families. Hyperscaler-native platforms — Google's Gemini Enterprise Agent Platform, Microsoft Foundry's Agent Service, and Amazon Bedrock AgentCore — trade flexibility for tight cloud integration. Framework-native platforms — LangSmith Deployment and the CrewAI AMP Suite — trade cloud-integration ease for model and infrastructure portability. The right choice depends less on feature lists and more on where your data lives, how much customisation your agents need, and how much operational burden you want to absorb internally. Lock-in ratings, cost-driver notes and 'best fit' verdicts below are our own assessment; platform names, status and capabilities are taken from each vendor's documentation.
What changed since this comparison was first published (checked 1 November 2026): Google's documentation says Vertex AI Agent Builder 'has transitioned to become part of Gemini Enterprise Agent Platform', and its release notes list Agent Runtime (previously Agent Engine). AWS's documentation says Amazon Bedrock Agents is now Bedrock Agents Classic, no longer open to new customers from 30 July 2026 and in maintenance mode, with Amazon Bedrock AgentCore as the recommended platform. Microsoft's platform is now Microsoft Foundry (formerly Azure AI Studio, then Azure AI Foundry), with Foundry Agent Service for agents. LangGraph Platform has been renamed LangSmith Deployment, and CrewAI's commercial offering is the CrewAI AMP Suite.
Why This Decision Is Harder Than It Looks
Every major cloud now ships a managed agent platform. Every major framework now ships a managed platform of its own. Each is backed by marketing that promises the fastest path to production. The actual comparison requires looking past the landing pages to the architecture: what exactly is managed, what must you still build, where does lock-in compound, and how does the pricing scale as your usage grows.
The stakes of this decision are higher than a typical vendor evaluation because agent platforms sit at the intersection of your data, your identity system, your operational workflows, and your model subscriptions. A wrong choice at the platform layer ripples into data-integration patterns, observability tooling, security posture, and finops. Unlike a monitoring tool that can be swapped with a few weeks of configuration work, swapping an agent platform late in a programme is closer to a re-platforming project than a migration.
This article compares five platforms enterprise architects commonly evaluate in 2026 — Gemini Enterprise Agent Platform (formerly Vertex AI Agent Builder), Microsoft Foundry Agent Service, Amazon Bedrock AgentCore (the successor to Bedrock Agents), LangSmith Deployment, and the CrewAI AMP Suite — across the dimensions that matter for a build-vs-buy decision: what is in the box, lock-in risk, cost drivers, and best-fit use cases.
Gemini Enterprise Agent Platform (Google Cloud; formerly Vertex AI Agent Builder)
What Is in the Box
Google's documentation describes Agent Builder as 'a suite of products that help developers build, scale, and govern AI agents in production' and says it has become part of Gemini Enterprise Agent Platform. The release notes list the Agent Development Kit (ADK), Agent Runtime (previously Agent Engine), Memory Bank, Sessions, Agent Gateway, Agent Registry, Agent Identity, Agent Observability and Model Garden. Model Garden covers Gemini and third-party models; the release notes mention Anthropic Claude and DeepSeek models among those available.
Lock-In Risk
Our assessment: medium. ADK is open source and describes itself as model-agnostic and deployment-agnostic, so agent code is portable. Agent Runtime, Memory Bank, Gateway and Registry are Google Cloud services, and the smoothest integration is with Gemini, BigQuery and Google Cloud's retrieval services. If your wider stack is already Google, the platform lock-in is largely already present.
Hidden Costs
Pricing spans several components: model inference, agent runtime, memory and sessions, retrieval, data processing and network egress. We do not quote figures because Google has renamed and repackaged these components in the past year; build the cost model from the current pricing page for each component you actually use.
Best Fit
Enterprises already standardised on Google Cloud with BigQuery as the warehouse and Gemini as the primary model, where the integration savings are substantial. For organisations not already on Google, it is in our view rarely the lowest-friction choice.
Microsoft Foundry Agent Service (Microsoft Azure)
What Is in the Box
Microsoft describes Foundry Agent Service as a managed platform for building, deploying and scaling agents, with three agent types: prompt agents (configuration only), voice-based prompt agents, and hosted agents (your own code and framework in a container, built with Microsoft Agent Framework, LangGraph, the OpenAI Agents SDK, the Anthropic Agent SDK or custom code). It works with models from the Foundry model catalog, provides toolboxes (web search, file search, code interpreter, MCP servers, functions), tracing and evaluations with Application Insights, Microsoft Entra agent identity with RBAC and private networking, and publishing to Microsoft Teams, Microsoft Copilot and the Entra Agent Registry. A2A v1.0 is generally available. Microsoft Copilot Studio is a separate low-code studio for agents and workflows aimed at makers rather than engineers.
Lock-In Risk
Our assessment: high within Microsoft 365 and Azure — which is often exactly what the customer wants. Entra identity, Teams and Copilot publishing and Foundry toolboxes are Microsoft-specific. Mitigating factors from Microsoft's documentation: hosted agents can run frameworks other than Microsoft's, and you can call the Responses API from an agent you run elsewhere.
Hidden Costs
Microsoft's documentation lists the cost model by agent type: per-call inference plus tool usage for prompt agents, container compute on top for hosted agents, and Voice Live and speech usage for voice agents. Application Insights ingestion, search, storage and telephony services can add to the bill. Check the current pricing pages and model your own volumes.
Best Fit
Enterprises standardised on Microsoft 365 and Azure with Entra ID identity, particularly where agents must act on SharePoint, Teams or other Microsoft 365 data or be published into Teams and Copilot. For code-first construction, see our comparison of Microsoft Agent Framework in our agentic AI frameworks comparison.
Amazon Bedrock AgentCore (Amazon Web Services; successor to Bedrock Agents)
What Is in the Box
AWS describes AgentCore as an agentic platform for building, deploying and operating agents using any framework and foundation model. Its modular services include a managed Harness (declare a model, system prompt and tools), Runtime, Memory, Gateway (turns APIs and Lambda functions into MCP tools), Identity, Code Interpreter, Browser, Observability (OpenTelemetry-compatible), Evaluations, Policy and Registry. AWS says AgentCore works with frameworks such as CrewAI, LangGraph, LlamaIndex, Google ADK, the OpenAI Agents SDK and Strands Agents, and with models in or outside Bedrock. The original Amazon Bedrock Agents is now Bedrock Agents Classic: no longer open to new customers from 30 July 2026, in maintenance mode with no new features, and still working for existing accounts.
Lock-In Risk
Our assessment: medium. Because AgentCore accepts any framework and model, your agent code and model choice stay portable; Runtime, Memory, Gateway, Identity and Policy are AWS services. If you have Bedrock Agents Classic workloads, AWS documents features with no direct AgentCore equivalent today — stage-specific prompt overrides, automatic parameter elicitation, and routing-mode multi-agent collaboration — so migration is a planning item for complex agents.
Hidden Costs
AWS describes AgentCore pricing as consumption-based across runtime, memory and gateway, with no upfront commitments or minimum fees. Model inference, Knowledge Bases storage, Lambda invocations and data transfer are billed separately. Bedrock Agents Classic itself carries no charge beyond the underlying resources. Check the current AgentCore pricing page.
Best Fit
AWS-first enterprises that want to run their chosen framework and models inside the same operational perimeter as their other AWS workloads, and teams migrating existing Bedrock Agents Classic agents.
LangSmith Deployment (LangChain)
What Is in the Box
LangSmith Deployment (renamed from LangGraph Platform in October 2025) is the managed deployment target for LangGraph agents. The LangGraph README lists durable execution, human-in-the-loop interrupts, short- and long-term memory, tracing and evaluation through LangSmith, and deployment infrastructure designed for long-running, stateful agents. The underlying LangGraph framework is open source and not tied to one model provider.
Lock-In Risk
Our assessment: low-to-medium. Agent code (LangGraph graphs, tools, state schemas) is the same whether you use the managed service or self-host. Moving off the managed service means taking responsibility for the durable execution layer but does not require rewriting agent logic. LangSmith observability is tightly coupled to the LangChain ecosystem.
Hidden Costs
Platform charges sit on top of your model costs, which stay with whichever provider you use. Self-hosting shifts cost to infrastructure and operations. Tracing and evaluation volume is a line item teams tend to underestimate; check the current LangSmith pricing and budget for it explicitly.
Best Fit
Enterprises that want strong stateful-orchestration primitives, LangSmith tracing and evaluation, and model-and-cloud portability — particularly complex multi-agent workflows with branching, checkpointing and human-in-the-loop. Our LangGraph multi-agent workflow deep-dive covers the production patterns.
CrewAI AMP Suite
What Is in the Box
The CrewAI README describes the AMP Suite as a commercial control plane around the open-source CrewAI framework, adding managed deployment, observability, governance, security and enterprise support, with tracing, a unified control plane, and on-premise or cloud deployment options. The core abstraction is the crew — role-based agents coordinating via sequential or hierarchical processes — alongside event-driven Flows. (The product was previously marketed as CrewAI Enterprise.)
Lock-In Risk
Our assessment: low. Crew definitions are open-source code, so moving between the AMP Suite and self-hosted CrewAI is straightforward, and CrewAI works with multiple model providers. The main coupling is to CrewAI's role-and-process abstraction, which is distinct from LangGraph's graph model; switching to LangGraph is a re-architecture, not a port.
Hidden Costs
Licence and infrastructure costs sit on top of model inference; at scale, model cost tends to dominate. Check the current AMP Suite pricing. In our experience, teams with strong evaluation requirements sometimes layer external observability on top.
Best Fit
Enterprises with role-based multi-agent workflows where the mental model of a specialist team matches the work, and bounded use cases that need a quick path from PoC to a governed deployment. Our CrewAI enterprise deployment guide covers the patterns that take it beyond PoC.
Platform Comparison at a Glance
| Platform | Strength | Lock-In (our assessment) | Best Fit |
|---|---|---|---|
| Gemini Enterprise Agent Platform (formerly Vertex AI Agent Builder) | Google data stack integration, Gemini-optimised, open-source ADK | Medium | Google Cloud / BigQuery shops |
| Microsoft Foundry Agent Service | Microsoft 365, Teams and Entra ID integration; any-framework hosted agents | High (within Microsoft) | Microsoft 365 / Azure shops |
| Amazon Bedrock AgentCore (successor to Bedrock Agents) | Any framework and model, modular services, consumption pricing | Medium | AWS-first enterprises |
| LangSmith Deployment (formerly LangGraph Platform) | Stateful orchestration, LangSmith tracing and evaluation | Low-medium | Complex workflows, multi-cloud |
| CrewAI AMP Suite (formerly CrewAI Enterprise) | Role-based clarity, governed deployment around open-source CrewAI | Low | Role-oriented multi-agent systems |
The Build-vs-Buy Rubric
Start from where your data lives
The single biggest integration cost in any enterprise agent is getting the agent to the data. In our view, if your data lives primarily in BigQuery, Google's platform starts ahead; if it lives primarily in SharePoint, Teams and Dynamics, Microsoft Foundry starts ahead; if it lives primarily in Redshift and S3, AgentCore starts ahead. Framework-native platforms (LangGraph, CrewAI) neutralise this advantage but require you to do the integration work yourself. The time and cost of that integration is the decisive factor for most enterprises.
Weight customisation needs realistically
If your agent logic is narrow and close to the vendor's reference patterns, a managed hyperscaler platform saves months. If your agent logic requires custom branching, explicit state machines, or non-standard evaluation, a framework-native platform is usually faster even accounting for the additional operational burden. Teams consistently underestimate how much their actual workflow deviates from the reference patterns, so err toward more flexibility when in doubt.
Evaluate observability and evaluation depth
The platform that provides the best debugging and evaluation experience is the platform that will hurt you least in month six. Each vendor now documents an observability story: LangSmith for LangGraph, Application Insights tracing and evaluations in Foundry, OpenTelemetry-compatible Observability and Evaluations in AgentCore, and Agent Observability in Google's platform. Compare them on your own debugging scenarios rather than on feature lists, and prioritise observability in the evaluation — the cost of bad observability compounds.
Model the total cost of ownership, not the sticker price
Agent platform TCO includes model inference, retrieval infrastructure, vector storage, durable execution, observability, identity integration, and operational burden. Sticker-price comparisons are misleading because managed platforms bundle many of these into a single line item while self-hosted approaches expose them. Build a realistic three-year TCO model across all layers before deciding. The answer frequently surprises teams that started the evaluation convinced a particular platform was obviously cheapest.
A common failure pattern is choosing an agent platform based on a successful PoC without validating the platform against the production workflow. PoCs exercise the happy path; production workflows exercise exceptions, edge cases, and integration boundaries the PoC never touched. Before committing to a platform, scope one production workflow with realistic data volumes, error conditions, and human-in-the-loop requirements, and pilot the platform against that workflow. The platform that wins on PoC and fails on production workflow pilot is a bullet dodged, not a platform rejected.
What We Recommend Across Inductivee Intelligence Engagements
Across financial services, healthcare, logistics, and manufacturing deployments, the pattern Inductivee Intelligence sees most often is a platform choice driven by the data stack and a framework choice driven by the workflow shape. Organisations already on Azure tend to pick Microsoft Foundry for the integration savings; organisations already on Google tend to pick Google's platform; organisations already on AWS tend to pick AgentCore — and, in our experience, teams increasingly author the agent logic itself in portable code (LangGraph, CrewAI or Microsoft Agent Framework) so that the deployment target can change.
For enterprises starting from zero — no strong cloud commitment yet — we generally recommend LangSmith Deployment plus whichever model provider best matches the workload, with observability via LangSmith. For enterprises already deep in a particular hyperscaler stack, use that hyperscaler's platform for deployment but keep the agent logic in portable code so that the authoring layer stays portable.
Our enterprise AI consulting practice helps enterprise architects run this evaluation rigorously. If you are mid-evaluation and want an engineering-honest second opinion on which platform fits your workload, that conversation is what our AI-readiness assessment is designed for.
Sources & Further Reading
Platform names, status and capabilities were checked against the vendor documentation below on 1 November 2026. Lock-in ratings and 'best fit' verdicts are our own assessment. Platforms change quickly; confirm before you commit.
- Google Cloud — Agent Builder documentation (now part of Gemini Enterprise Agent Platform)
- Google Cloud — Gemini Enterprise Agent Platform release notes
- Google Agent Development Kit — adk-python repository
- Microsoft Learn — What is Microsoft Foundry Agent Service?
- Microsoft Learn — Microsoft Copilot Studio overview
- AWS — Amazon Bedrock Agents Classic maintenance mode
- AWS — What is Amazon Bedrock AgentCore?
- LangGraph — GitHub repository (langchain-ai)
- CrewAI — GitHub repository (crewAIInc)
Frequently Asked Questions
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Written By
Inductivee Intelligence Team
AuthorAgentic AI Engineering Team
The Inductivee Intelligence engineering team — multi-agent orchestration, RAG pipeline architecture, and data liquidity engineering. Our writing is grounded in what we actually build, break, and operate in production.
Inductivee Intelligence is a remote-first agentic AI engineering studio. Our engineering content is written by active practitioners and technically reviewed before publication.
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