AWS Bedrock vs Azure AI Agent Service vs Google Vertex vs GMI Cloud: Hosting AI Agents at Scale

Enterprise teams default to whichever cloud the master contract covers, then discover the agent service charges 5x per-token compared to a rental GPU. Picking by procurement convenience instead of concurrency ceiling locks teams into pricing that punishes scale, plus state stores engineers can't introspect. The cost shows up at the regional quota, the audit review, or the six-figure migration estimate.

Managed platforms still earn their keep for teams shipping in two weeks, so the point isn't to avoid them. It's to know what you're buying and where self-hosting pencils out. This guide compares AWS Bedrock AgentCore, Azure AI Agent Service, and Google Vertex Agent Builder head to head, then maps the self-host path on GMI Cloud.

TL;DR: The Three-Way Plus One

The three hyperscaler agent services compete on the same axis: managed runtime, built-in guardrails, native knowledge bases, tight binding to their parent cloud's identity stack. They differ on concurrency ceilings, state shape, and how painful the lock-in feels two years in.

GMI Cloud isn't a fourth managed agent platform. It's the compute layer underneath a self-built stack, at H100 $2.00/hr and H200 $2.60/hr, for teams that refuse to pay managed-service markups on every token.

Head-to-Head: The Three Managed Agent Platforms

The table below maps each service's public product surface. Concurrency figures cite each vendor's published quota docs as of early 2026 and shift on request.

Capability	AWS Bedrock AgentCore	Azure AI Agent Service	Google Vertex Agent Builder
Agent runtime	Managed, serverless	Managed, Foundry-bound	Managed, Gemini-native
Default concurrency	~100 concurrent sessions per region (quota-bumpable)	Bound to Foundry project TPM limits	Bound to Vertex regional quota
State persistence	DynamoDB-backed session memory	Cosmos DB / Azure AI Search	Firestore + Vertex Memory Bank
Native guardrails	Bedrock Guardrails (PII, denied topics)	Content Safety + Prompt Shields	Vertex Safety Filters
Knowledge bases	Bedrock Knowledge Bases (OpenSearch/Aurora)	Azure AI Search index	Vertex AI Search
Identity binding	IAM + AWS SSO	Entra ID + M365 graph	Workforce Identity Federation
Per-token premium vs raw inference	~3-5x	~3-4x	~3-5x

AWS Bedrock AgentCore

AgentCore wraps Claude, Llama, and other Bedrock-hosted models with managed orchestration, tool calling, and DynamoDB-backed session memory. It pairs with Knowledge Bases (RAG via OpenSearch or Aurora) and Bedrock Guardrails for PII and topic filtering. The catch is at scale: default concurrency caps near 100 sessions per region, bumpable but slow past a few hundred.

Azure AI Agent Service

Azure's agent layer lives inside AI Foundry and binds tightly to Entra ID, the right answer if your workforce already runs on M365 and Purview. Threads and run state persist in Cosmos DB or Azure AI Search, with Content Safety as the guardrail layer. Integration is the selling point, and so is the lock-in.

Google Vertex Agent Builder

Vertex Agent Builder leans on Gemini as the default brain, uses Vertex AI Search for grounding, and persists session state in Memory Bank. Workforce Identity Federation maps external IdPs without a parallel Google directory. Concurrency runs on per-project regional quotas, so your headroom is whatever Vertex has provisioned.

Where the Three Differ in Practice

Concurrency under burst load

Bedrock AgentCore's default quota is the lowest documented ceiling of the three. Azure and Vertex publish TPM and request quotas at the project level that you translate into agent capacity yourself. All three throttle differently under burst, so load tests against your real prompt shape matter more than spec sheets.

State persistence shape

Each platform picks a different store: Bedrock writes to DynamoDB, Azure to Cosmos DB, Vertex to Firestore plus Memory Bank. The shape matters for compliance review and migration. If auditors want point-in-time recovery on agent memory, the answer differs by platform.

Integration with the existing cloud estate

Bedrock plays best inside AWS accounts using IAM, VPC endpoints, and CloudWatch. Azure is the strongest fit when Entra ID owns identity. Vertex slots in when BigQuery and GCS hold your grounding data. Cross-cloud is possible everywhere and clean nowhere.

Engineering Reality: What Production Demands

Picking a managed platform doesn't remove the engineering work, it shifts where it shows up. Here's the short list of what your team still owns regardless of vendor.

Agent concurrency tuning. Default quotas won't survive a launch spike. Open the quota-increase ticket on day one, and load-test against your real prompt distribution because token bursts behave nothing like steady traffic.

State persistence patterns. DynamoDB, Cosmos DB, and Firestore each need TTL policies, partition-key design that avoids hot keys, and a backup strategy. Memory bloat from unbounded session history is the most common cause of agent latency regression in month three.

Audit logging hookups. CloudTrail, Azure Monitor, and Cloud Audit Logs capture control-plane events. Data-plane logging (actual prompts and tool calls) needs a separate sink. Pipe to S3, ADLS, or GCS with retention that matches your compliance window.

Multi-region failover. Session state is regional by default on all three. Cross-region failover means replicated state stores, idempotent tool calls so retries don't double-charge, and routing that handles regional brownouts cleanly.

Guardrails implementation. Native filters catch the obvious cases. You still need JSON-shape validation, PII redaction on tool outputs (not just model outputs), and an evaluator harness that catches regressions when you swap models.

The Self-Host Alternative: Build the Agent Layer on Rented GPUs

If the per-token premium looks unjustifiable at your volume, rent raw GPU capacity and build the agent layer yourself. GMI Cloud provides H100 SXM and H200 SXM on-demand at $2.00 and $2.60 per GPU-hour, on 8-GPU nodes with NVLink 4.0 (900 GB/s bidirectional aggregate per GPU on HGX) and 3.2 Tbps InfiniBand.

GB200 NVL72 is available for frontier-model deployments at an effective $8.00 per-GPU-hour. Check gmicloud.ai/pricing for current rates.

The stack ships pre-configured with CUDA 12.x, TensorRT-LLM, vLLM, and Triton, so you skip kernel tuning and focus on orchestration.

Layer	Managed Agent Service	Self-Host on GMI Cloud
Compute cost	Per-token premium (3-5x raw)	Per-GPU-hour flat
Agent runtime	Built in	You build (LangGraph / own)
State store	Managed	You operate (Redis / Postgres / Dynamo)
Guardrails	Native	You integrate (Guardrails AI / NeMo)
Vendor lock	High	Low (portable to any GPU cloud)
Time to first production agent	2-4 weeks	6-12 weeks

Honest Acknowledgment: What GMI Cloud Doesn't Replace

GMI Cloud is the compute layer, not a managed agent platform, and the comparison above isn't feature parity. Here's what you still own on self-host.

• No managed agent runtime. No GMI equivalent to AgentCore, Foundry agents, or Agent Builder. You bring LangGraph, Temporal, or your own orchestration.
• No native guardrails product. PII filtering, prompt-injection defense, and output validation are your integration, typically via Guardrails AI or NeMo Guardrails.
• No managed agent-memory service. Session state and long-term memory live in stores you operate (Redis, Postgres, Dynamo, or a vector DB).
• No built-in audit-logging for agent traces. You wire your own observability stack (OpenTelemetry, Langfuse, Arize) and retention policy.

If you lack the engineering bandwidth, a managed service is right even at the premium. If you have it, self-host math gets attractive once traffic crosses roughly 50 sustained sessions.

When Each Path Pencils Out

Your situation	Start here
First agent in production within a month	Bedrock / Azure / Vertex (match your cloud)
Workforce on M365 and Entra ID	Azure AI Agent Service
BigQuery is the grounding source	Vertex Agent Builder
AWS-native shop, IAM is identity	Bedrock AgentCore
Sustained 50+ concurrent agents, cost-sensitive	Self-host on rented GPUs
Full control over orchestration	Self-host on rented GPUs
Frontier-model agents (100B+, long context)	H200 or GB200 NVL72 self-host

FAQ

How do agent concurrency limits work across these platforms?

Bedrock publishes a soft cap near 100 concurrent sessions per region, bumpable via support but slow past a few hundred. Azure and Vertex govern by project-level TPM and request quotas you translate into agent capacity through load testing. Test against your real prompt distribution since burst behavior diverges from steady-state.

Can I migrate agent state between Bedrock, Azure, and Vertex?

Not cleanly. Each platform uses a different state store (DynamoDB, Cosmos DB, Firestore) with different schemas. Export-and-replay is doable, but it's a project, not a config change. Self-hosting on LangGraph plus Redis or Postgres avoids the lock-in.

Why self-host on GMI Cloud instead of using Bedrock AgentCore?

Cost and control. At sustained 50+ concurrent agents, managed per-token premiums typically exceed H100 $2.00/hr or H200 $2.60/hr GPU rental by several multiples. You also keep full ownership of orchestration, memory, and guardrails.

What does GMI Cloud not provide that Bedrock AgentCore does?

A managed agent runtime, native guardrails product, managed knowledge-base service, and packaged session memory. GMI Cloud provides GPU compute and a pre-configured inference stack. You build the agent layer with LangGraph, Guardrails AI, and your choice of state store.