Enterprise AI Agent Platforms Compared Across Deployment, Controls and Pricing

Copilot Studio, Amazon Bedrock AgentCore, Gemini Enterprise Agent Platform and the OpenAI Agents SDK document different deployment models, identity controls and billing units. This reference maps what each vendor publishes so buyers can scope an identical comparison before committing.

Four starting points, two shapes

Microsoft positions Copilot Studio as a graphical, low-code studio for building and managing AI-powered agents and workflows and publishing them to Teams, Copilot, websites and other channels (Microsoft Learn). AWS frames Bedrock AgentCore as a production platform that works with any framework and model, naming the OpenAI Agents SDK and Strands SDK among starting points (AWS).

Google labels the family Gemini Enterprise Agent Platform and describes a fully managed Agent Runtime plus Sessions and Memory Bank (Google Cloud). OpenAI separates a managed Agents API, where OpenAI runs a Codex harness and saves progress, from an Agents SDK that runs inside the customer application (OpenAI). SDK versus managed service is therefore the first structural distinction.

Identity and access boundaries

Copilot Studio documents three authentication options: none, Microsoft Entra ID, and manual authentication with generic OAuth 2 providers (Microsoft Learn). The pairing of authentication type and required sign-in determines whether agent sharing can restrict who chats. Google lists IAM agent identity, Agent Gateway routing, sessions access via IAM Conditions and Private Service Connect (Google Cloud). OpenAI describes tools split between hosted execution on OpenAI servers and local runtime execution, plus approvals that still apply during program-owned calls (OpenAI).

Recovery, observability and evaluation

AgentCore says agents can be traced when something goes wrong, and variations tested against real traffic (AWS). Google documents Cloud Trace, Cloud Logging, an Evaluation Service, an Example Store, a feedback service and Memory Bank for long-term state (Google Cloud). OpenAI lists trace grading, then datasets and eval runs for repeatability, and its Agents API index includes errors and recovery (OpenAI; OpenAI). Microsoft cites evaluations with test sets before and after publishing, plus analytics and administration (Microsoft Learn). Our recommendation is to test one specific failure path rather than assume parity.

Pricing units differ before prices do

Microsoft lists a pre-purchase plan in Copilot Credit Commit Units and a pay-as-you-go plan, but the captured pricing page marks these plans unavailable for its selected market, with an Azure subscription required for agents (Microsoft). AgentCore bills per feature: per-second CPU and memory with a one-second minimum for Runtime microVMs, per query for Web Search, per API call for Gateway, per authorization request for Policy, plus standard EC2, EBS and S3 rates (AWS). Google's pricing page lists machine-type hourly rates, training and prediction charges in 30-second increments, and flexible savings plans (Google Cloud). OpenAI's pages describe hosted tools, sandboxes and session accounting (OpenAI).

Our checklist for an apples-to-apples comparison

Editorial assessment, not vendor guidance. Run one identical task across candidates, with the same data source, tool set and expected output. Fix one identity boundary and test whether the platform enforces it on tool calls and on chat access. Define one recovery requirement, such as resuming an interrupted session, and note whether state survives on the vendor side or in the application. Finally, map the invoice scope: credits, compute seconds, search queries, API calls and storage may all appear separately, so ask each vendor to show every line item the task would generate.

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