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There is no single best place to run AI agents. Choose the platform that fits your existing cloud, identity and data environment, then check framework support, session behavior, security controls, regional availability and the cost of your actual workload. For Microsoft-oriented teams, Microsoft Foundry Agent Service is a managed option; AWS teams can investigate Amazon Bedrock AgentCore Runtime; and Google Cloud teams can evaluate Google Cloud Agent Runtime. These are vendor-documented capabilities, not independent performance rankings.
Decide how much infrastructure you want to operate
Hosting is separate from the framework or protocol an agent uses. With managed hosting, the provider operates more of the runtime infrastructure. With self-hosting, your team controls more of the application environment but also takes on more operational work.
| # | Preview | Product | Price | |
|---|---|---|---|---|
| 1 |
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MINISFORUM MS-02 Ultra Workstation Mini PC, Intel Core Ultra 9 285HX (24C/24T, up to 5.5GHz), PCIe... | $1,659.00 | Buy on Amazon |
| 2 |
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GMKtec EVO-X2 AI Mini PC Ryzen Al Max+ 395 Superchip 128GB LPDDR5X 2TB SSD | $3,649.99 | Buy on Amazon |
Managed hosting
A managed service can handle runtime deployment, scaling and parts of session lifecycle or platform integration. That can reduce infrastructure work, but it does not transfer responsibility for agent behavior, permissions, application quality, safety measures or data-sharing decisions. Microsoft explicitly places those responsibilities on the application team.
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Self-hosting can suit teams that need control over routing or integration with existing infrastructure and are prepared to operate it. Microsoft’s hosting comparison says self-hosters are responsible for concerns such as routing, identity, authorization, request policy, storage, deployment, scaling and client libraries. The Responses protocol can be used with either hosting model.
#1 Best Overall
- High-Performance AI Processor：The MS-02 Ultra features an Intel Core Ultra 9 285HX (24C/24T, up to 5.5 GHz, 13 TOPS NPU), delivering fast and efficient performance for AI inference, algorithm development, and media workloads. A PCIe x16 expansion slot supports desktop-class GPU upgrades for advanced model training and accelerated computing tasks. It's ideal for creators, engineers, and teams handling intensive parallel workloads.
- 4 × M.2 PCIe 4.0 + 4 × DDR5 SODIMM slots:Four DDR5 SODIMM slots support up to 256 GB of memory, while ECC helps maintain data integrity in mission-critical environments. Four PCIe 4.0 M.2 slots support up to 24 TB of storage, supporting RAID 0/1/5/10, combining high-speed performance with data protection. It allows for the creation of independent scratch disks, media libraries, and project drives, providing high-throughput for production workflows.
- PCIe & USB 4.0 v2: Up to three PCIe slots can be equipped, including a dual-slot x16 GPU. The main slot supports PCIe 5.0, meeting the needs of high-bandwidth creative and computing workloads. USB 4.0 v2 (80Gbps) supports high-bandwidth external storage and displays.
- Ultra-fast Networking: Wi-Fi 7 further enhances wireless performance with next-generation speeds and low-latency stability. Intelligent bandwidth switching optimizes throughput in different network environments, ensuring optimal performance for enterprise or local networks. Dual 25GbE ports (providing up to approximately 3.125 GB/s bandwidth, about 25 times faster than traditional 1GbE), enabling seamless large-scale file transfers and parallel computing. 10GbE and 2.5GbE ports, with support for Intel vPro technology, ensure enterprise-grade remote management and deployment flexibility.
- Server-grade thermal architecture: Utilizing a dedicated CPU/GPU airflow design, equipped with a 6-pipe dual-fan cooler, it maintains stable performance even under sustained loads, delivering up to 140W Turbo power while maintaining a 100W TDP, and operating with noise levels as low as 36 dB. An integrated 350W power supply ensures stable and reliable output for demanding computing tasks and fully loaded extended configurations.
Compare the platforms that fit your cloud
| Platform | What the vendor documentation describes | Most relevant fit | What to verify |
|---|---|---|---|
| Microsoft Foundry Agent Service | Prompt agents, voice-based prompt agents and code-based hosted agents. Hosted agents can use several frameworks or custom code, with a managed endpoint, automatic scaling, Microsoft Entra identity, session-level state persistence and observability. | Teams using Azure, Microsoft identity or Foundry tools, or seeking managed code hosting with framework choice. | Regional availability, service limits, data flows and workload pricing. |
| Amazon Bedrock AgentCore Runtime | Managed runtime supporting frameworks including LangGraph, Strands and CrewAI, and models from multiple providers. Compute options include serverless microVM sessions and managed EC2 instances. | AWS teams seeking runtime controls while retaining framework and model choices. | Current platform version, service limits, regional availability and total cost. |
| Google Cloud Agent Runtime | A quickstart demonstrates deployment with LangGraph, LangChain, AG2 and LlamaIndex. The page marks its LlamaIndex integration Preview and subject to Pre-GA terms. | Google Cloud teams whose agent fits one of the documented framework paths. | Region support, feature stage, runtime limits and current costs. |
Vendor documentation establishes described features, not a cross-provider advantage in performance, price or capability. Check the current service terms and regional documentation for your intended deployment before committing.
Match the runtime to your agent’s workload
Conversational agents and agents that retain session state
Microsoft Foundry’s hosted-agent guide describes per-session VM-isolated sandboxes, persisted filesystem state and a durable key-value store. It distinguishes Responses, which manages conversation history for conversational flows, from Invocations, which gives callers a custom payload contract for webhooks or other structured, non-conversational work. Confirm lifecycle and persistence limits for your particular use case.
Webhooks and structured tasks
If the caller sends a defined payload rather than continuing a conversation, evaluate whether the runtime supports that interaction model cleanly. Foundry documents Invocations for arbitrary payloads and other protocols. For any provider, test request handling, retries and failure recovery with the payloads and downstream tools the production application will use.
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Framework or model flexibility
AgentCore explicitly documents support for multiple frameworks and model providers. Foundry’s hosted-agent overview lists Agent Framework, LangGraph, OpenAI Agents SDK, Anthropic Agent SDK, GitHub Copilot SDK and custom code. Google’s quickstart demonstrates LangGraph, LangChain and AG2, and lists LlamaIndex as Preview. Confirm that the specific framework version and model path you plan to deploy are supported; a general compatibility statement does not establish every combination.
Voice agents
Foundry’s overview includes a managed voice-based prompt-agent path. The reviewed material does not establish a comparable feature-by-feature voice assessment across all three services, so evaluate current documentation against your required interaction flow rather than assuming parity.
Rank #2
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- AI NPU with XDNA 2 ARCHITECTURE - Powered by 16 “Zen 5” CPU cores, 50+ peak AI TOPS XDNA 2 NPU and a truly massive integrated GPU driven by 40 AMD RDNA 3.5 CUs, the Ryzen AI MAX+ 395 is a transformative upgrade and delivers a significant performance boost over the competition. The Ryzen AI Max+ 395 excels in consumer AI workloads like the llama.cpp-powered application: LM Studio. Shaping up to be the must-have app for client LLM workloads, LM Studio allows users to locally run the latest language model without any technical knowledge required and unleash their creativity and productivity.
- AMD RADEON 8090S iGPU GAMING PC - The AMD Radeon RX 8060S offers all 40 CUs with up to 2.9 GHz graphics clock and uses the new RDNA 3.5 architecture. The powerful iGPU is positioned between an RTX 4060 and 4070 laptop GPU and therefore enables gaming in FHD at maximum details in most demanding games. The 8060S can also utilize the full 128GB pool, which is perfect for running LLMs such as Deepseek 70B Q8, which runs comfortably on this machine.
- EIGHT CHANNEL LPDDR5X - LPDDR5X is a new ground breaking memory small form factor installed on-board. With blazing speeds up to to 8000MT/s, it runs 1.5x faster than the DDR5 SODIMMs; 90% better performance over DDR5 SODIMMs in video conferencing and photo editing; 30% better performance in productivity apps; 12% better performance in digital content workloads.
- QUAD SCREEN 8K DISPLAY SUPPORT - EVO-X2 AI Mini PC support 4-screen 4K/8K output via HDMI 2.1 (8K@60Hz), DisplayPort 1.4 (4K@60Hz), and dual USB 4 40Gbps Transfer speed (supporting PD3.0/DP1.4/DATA). Ideal for gaming, video editing, and multitasking, it provides expansive and crisp multi-display support.
Assess security and operational ownership
Do not treat a managed runtime as a complete security design. Before choosing, map which identities can invoke the agent, what tools and data it can reach, where data flows, and how actions are audited. Review the service’s current controls against your organization’s compliance boundary and application safeguards.
- Identity and permissions: Check whether the runtime supports the identity model you use, and scope each agent’s access to the tools and data it needs.
- Network and data boundaries: Verify connectivity, data handling and regional support for the exact service configuration.
- Observability: Confirm that traces expose the events your team needs to diagnose failures. AgentCore describes tracing for reasoning steps, tool calls and model interactions; Foundry documents observability for hosted agents.
- Operational responsibility: Identify who owns deployment, scaling, storage, rollbacks, safety controls and incident response. Managed hosting changes the division of work; it does not eliminate it.
Estimate cost from your workload, not a platform label
The available product descriptions do not support a fair claim that one platform is cheapest. Foundry’s overview separates inference and tool-use charges for prompt agents from container compute for hosted agents. AgentCore documents both on-demand serverless sessions and managed instances, which have different capacity characteristics. A meaningful comparison needs a specific model, region, token and tool volume, session duration, concurrency, storage and network assumptions.
Build a practical estimate
- Write down the model or models the agent will call, including expected requests and tokens per session.
- Estimate tool calls, session length, daily or monthly workload, peak concurrency and any idle or persistent capacity.
- Include runtime compute, storage and network costs alongside model inference and tool costs.
- Use current regional pricing for each candidate and run a pilot with representative traffic to compare measured usage.
Run a pilot before committing
A short deployment test can expose mismatches that feature lists do not. Use the same representative tasks across shortlisted platforms and record the results under your own security and workload requirements.
- Test the actual framework, model, tools and request patterns you intend to use.
- Check tool permissions and whether an agent can reach only its approved data and services.
- Interrupt sessions, trigger tool failures and retry requests to see how recovery and state behave.
- Inspect traces and logs to confirm that operators can diagnose errors without exposing data they should not see.
- Measure cost using realistic session duration, concurrency, inference and tool usage.
- Confirm feature stage, service limits and supported regions for the exact configuration.
Which platform should you investigate first?
- Start with Foundry if your organization already relies on Microsoft identity and tools, or wants its documented managed hosted-agent path.
- Start with AgentCore if your workloads are already in AWS and you want to assess its documented framework, model and compute choices.
- Start with Google Cloud Agent Runtime if your team uses Google Cloud and the agent matches a documented framework path; treat the LlamaIndex route as Preview under the reviewed quickstart.
- Consider self-hosting when the application needs more direct infrastructure control and your team can own the associated operations.
These starting points reflect documented fit, not a universal ranking. The right choice is the one that passes your workload, security, operations and cost checks.
Quick Recap
Sources
- Microsoft Foundry Agent Service overview
- Microsoft hosted agents documentation
- AWS Bedrock AgentCore Runtime documentation
- Google Cloud Agent Runtime quickstart
- Microsoft Agent Framework hosting comparison
- Microsoft Foundry Agent Service product page
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
