An owned, model-agnostic, deploy-anywhere AI platform — vs. AWS-native Bedrock + Amazon Q for building agents and enterprise assistants
AWS Bedrock gives enterprises an AWS-native way to access multiple foundation models (Anthropic, Meta, Cohere, Amazon, and others) through one API; Amazon Q layers an enterprise assistant on top. The pair runs on AWS, integrates with the broader AWS stack, and the model selection is curated by Amazon.
Self-hosted AI is a different shape: a model-agnostic platform you own, that runs on any cloud, on-premise, or fully air-gapped. You bring any LLM — including any of Bedrock's hosted models — and operate the platform under perpetual license.
Both are legitimate enterprise options. The decision is whether you want a deeply AWS-native multi-model service, or an owned platform that can run anywhere and route to any model.
by ibl.ai
Owned agentic AI platformby AWS
Hyperscaler AI platform (Bedrock foundation models + Q Business)| Criteria | Self-Hosted AI | AWS Bedrock + Amazon Q |
|---|---|---|
| Foundation Model Catalog | Run any open or commercial model — including Anthropic Claude and others Bedrock offers — through your own routing layer. | Strong multi-model API: Anthropic, Meta, Cohere, Amazon, and others, all managed inside Bedrock. |
| Agent & Workflow Builder | Full agentic OS — agents, workflows, learning, and content — owned end to end. | Bedrock Agents and Q's enterprise assistant capabilities, bounded to AWS's runtime. |
| Enterprise Search & RAG | Permissions-aware retrieval over your knowledge, with embeddings of your choice. | Strong RAG via Bedrock Knowledge Bases and the AWS data plane. |
| Any-LLM Routing | Route any LLM by cost, latency, and capability — switch per workload, on-prem and air-gapped included. | Routing across Bedrock's hosted models; non-Bedrock models require integration work. |
| Criteria | Self-Hosted AI | AWS Bedrock + Amazon Q |
|---|---|---|
| Multi-Cloud / On-Prem / Air-Gapped | Run on AWS, Azure, GCP, on-premise, or fully air-gapped — same platform across environments. | AWS-native; on-prem options (Outposts) are limited; not built for air-gap. |
| Data Sovereignty & Residency | Prompts, embeddings, and audit logs stay entirely in your environment. | Strong AWS region controls; data is processed in the AWS cloud under shared-responsibility. |
| Source-Code & Platform Ownership | Own the full platform code under perpetual license — fork, extend, exit. | Managed cloud service; you consume Bedrock and Q rather than owning the platform. |
| Cloud Vendor Lock-In | Cloud-agnostic — migration between clouds doesn't change the platform. | Tied to AWS account, Bedrock/Q services, and AWS pricing. |
| Criteria | Self-Hosted AI | AWS Bedrock + Amazon Q |
|---|---|---|
| Cost Model at Scale | Flat platform fee + usage on compute you own; cost grows with consumption, not seats or services. | AWS consumption + Bedrock/Q service fees; predictable but tied to AWS pricing. |
| Compliance Fit (HIPAA / FedRAMP / FERPA / NIST) | Compliance posture sits inside your perimeter; air-gap satisfies the strictest mandates. | Broad AWS compliance coverage, including GovCloud for federal workloads. |
| Time-to-Value | Requires infrastructure and setup, or a partner to deploy and operate it. | Turn on Bedrock and Q in your AWS account and ship quickly for AWS-standardized teams. |
| Support & Service Catalog | Forward-deployed engineering + enterprise support across the platform. | AWS enterprise support and the broader AWS service ecosystem. |
Self-hosted AI is the right shape when ownership, multi-cloud, and air-gap matter — when you want one platform across AWS, Azure, GCP, on-prem, and air-gapped.
Bedrock + Q are the right shape when AWS is the strategic cloud and a curated multi-model API across Bedrock's roster fits your needs.
Choose Bedrock + Q for AWS depth and a multi-vendor model API; choose self-hosted AI when you want one owned platform across clouds and deployment models.
A model-agnostic platform runs any LLM — including all the models Bedrock offers — and routes per workload as the frontier moves, including local and open models.
Bedrock offers strong third-party model breadth, but the catalog is curated and routing lives inside AWS.
If running open / local models alongside frontier ones matters, self-hosted AI fits; if Bedrock's catalog covers your needs, Bedrock + Q is sufficient.
Owning the platform code on infrastructure you choose means migrations between clouds don't replatform your AI — and air-gap is always an option.
Bedrock + Q assume AWS as the durable strategic cloud; leaving AWS means rebuilding the AI layer.
For organizations that want optionality on cloud and models simultaneously, the owned platform wins on lock-in posture.
If AWS is the strategic cloud and Bedrock's catalog fits your model needs, the native services deliver the fastest time-to-value.
Self-hosted AI runs the same platform across AWS, Azure, GCP, on-prem, and air-gapped — without rebuilding when cloud strategy shifts.
Full source-code ownership and air-gap deployment meet mandates a hyperscaler-managed cloud can't satisfy.
Routing across open and local models alongside frontier ones — on infrastructure you control — beats a curated catalog inside one cloud.
Timeline: A few weeks, depending on infrastructure and MLOps maturity
Timeline: Days to a couple of weeks
See how ibl.ai deploys AI agents you own and control—on your infrastructure, integrated with your systems.