Comparison

AI Orchestrator vs AWS Bedrock AgentCore

Both products run MCP servers. They are not the same product. AgentCore is a managed runtime for the MCP servers AWS hosts. Magertron's AI Orchestrator is a Kubernetes-native governance plane for the MCP servers across your enterprise, wherever they run.

AI Orchestrator

The Kubernetes-native MCP control plane

Self-hosted in your cluster. Deploys MCP servers as pods, routes through Envoy with JWT authentication, and is being built to federate inventory and governance across managed, self-managed, and shadow MCP servers in your environment.

  • Runs in your Kubernetes cluster — any cloud, on-prem, air-gapped
  • OSS Free up to 20 servers, no signup, no credit card
  • Your data and credentials never leave your network
  • Fixed-capacity pricing, predictable at any scale
AWS Bedrock AgentCore

Managed runtime substrate for AI agents

A serverless AWS platform for hosting agents and MCP servers in per-session microVMs. Twelve modular components — Runtime, Gateway, Memory, Identity, Browser, Code Interpreter, Observability, and more — billed independently on consumption.

  • Fully managed by AWS, available in fourteen regions
  • Per-session microVM isolation, up to 8-hour sessions
  • Native integration with Bedrock models and the AWS ecosystem
  • Consumption-based pricing, idle wait time not charged
The framing

Hosting MCP servers and governing MCP servers are different problems.

When AgentCore launched in October 2025, the obvious question for anyone building MCP infrastructure was: is this the end of the category? It is a serious product from a serious company. AWS shipped microVM-per-session isolation, up to 8-hour session limits, consumption-based pricing that does not charge for I/O wait, and a Gateway service that centralizes MCP tool routing across MCP servers, REST APIs, and AWS Lambda functions. It is good work.

It is also not the answer to the enterprise MCP problem, because AgentCore solves hosting, and the enterprise problem is governance.

Side by side
▸ Show the full comparisonHide the full comparison 18 dimensions
AI Orchestrator AWS AgentCore
Deployment model Self-hosted on Kubernetes (any cloud, on-prem, air-gapped) AWS-managed PaaS, 14 regions
Where MCP servers run Kubernetes pods in your cluster microVMs in AWS infrastructure
Cloud lock-in None Runs anywhere k8s runs AWS-native Tightly coupled to Bedrock and AWS services
Pricing model Software licensing. OSS Free up to 20 servers. Tiered pricing above. Fixed capacity. Consumption-based per service. Runtime at $0.0895/vCPU-hr + $0.00945/GB-hr. Twelve components billed independently.
Predictable at scale Yes Cost scales with cluster capacity, not request volume Conditional Favorable at low/moderate volume; crossover point exists for high-traffic workloads
Data sovereignty Full Servers, credentials, and traffic stay in your network Regional Data resides in selected AWS regions, subject to AWS compliance posture
Air-gapped deployment Supported Not applicable AgentCore is a managed cloud service
Time to first MCP server Hours. Install k3s, deploy Helm chart, register server. Minutes. Push a container, deploy.
Session isolation Pod-level isolation, namespace policy microVM per session Strong tenant isolation
Session length limit No fixed limit — runs as long as the pod runs Up to 8 hours per session
Auto-scale to zero Not built-in HPA scales replicas, minimums are configurable Yes Native to AgentCore Runtime
Auth and identity Bring your own OAuth/OIDC provider. JWT validation at the Envoy data plane. AgentCore Identity (Cognito or external OAuth). Federated identity for tool targets.
MCP discovery and inventory Roadmap Federation across config files, endpoint inspection, egress logs, and source repos Out of scope AgentCore manages what customers register; does not discover shadow MCP
Observability Standard Kubernetes telemetry: Prometheus, OpenTelemetry, your existing stack AgentCore Observability via CloudWatch, OpenTelemetry exports, Datadog/Dynatrace integrations
Memory, code interpreter, browser as services Out of scope Included Managed Memory, Code Interpreter, Browser services
Multi-cloud or hybrid Yes Same control plane across AWS, GCP, Azure, on-prem AWS only
Maturity Pre-1.0, active development by a small team GA since October 2025, mature core with new features still shipping
Vendor Magertron (independent startup) Amazon Web Services

See where AI Orchestrator fits in your stack.

Talk through your environment, your existing infrastructure, and where MCP governance fits. Or grab the Helm chart and try it yourself.

Try it now View on GitHub