By 2026, federal program offices buying LLM capability have three serious accredited platforms to choose from: AWS Bedrock in AWS GovCloud, Azure OpenAI Service in Azure Government, and Google Vertex AI on Assured Workloads. Each is FedRAMP High accredited, each has a meaningful and growing model catalog, and each has a different IL trajectory for DoD work. Picking among them is the most consequential platform decision in any federal AI program — it drives model availability, fine-tuning options, integration cost, and steady-state operating cost for years.
This comparison is platform-neutral. Precision Federal delivers AI workloads on all three depending on the customer, the workload, and the existing agency cloud footprint. We have no preferred reseller relationship that biases the recommendation.
The three platforms in plain English
AWS Bedrock (in GovCloud)
AWS Bedrock is Amazon's fully-managed multi-vendor LLM service. In commercial AWS, Bedrock exposes models from Anthropic (Claude family), Meta (Llama), Mistral, Cohere, AI21, Stability, and Amazon's own Titan and Nova families through a single API. In AWS GovCloud (US) regions, Bedrock is FedRAMP High accredited with an expanding subset of these models — Anthropic Claude has been the headline addition for federal customers, alongside Meta Llama, Amazon Titan, Amazon Nova, Cohere, and others. The GovCloud Bedrock catalog lags commercial Bedrock by some weeks but moves quickly.
Bedrock's strength is vendor breadth: a federal program office can mix Claude for reasoning-heavy tasks, Llama for cost-sensitive bulk inference, Cohere for embedding, and Titan or Nova for AWS-native workflows — all under one IAM, one VPC, one accreditation boundary. Bedrock also exposes Knowledge Bases (managed RAG), Guardrails (content filtering and PII redaction), and Agents (managed orchestration), all inside the accredited region.
Azure OpenAI Service (in Azure Government)
Azure OpenAI Service is Microsoft's exclusive enterprise distribution of OpenAI models. In commercial Azure, this means GPT-4o, GPT-4.1, the o-series reasoning models, embeddings, DALL-E, and Whisper. In Azure Government, Azure OpenAI is FedRAMP High and DoD IL5 authorized for selected GPT models. This is the longest-standing and most mature IL5 LLM offering in the market and the default for DoD program offices that need GPT models in an IL5 environment.
Azure OpenAI's strength is OpenAI model access in the most accredited environment. For program offices that have standardized on the OpenAI model family in the rest of their organization, Azure OpenAI Government is the natural extension into the accredited side. Microsoft also brings deep integration with Microsoft 365 Government, Dynamics, Power Platform, and Sentinel — which matters when the LLM workload needs to plug into existing agency Microsoft tooling.
Google Vertex AI (on Assured Workloads)
Vertex AI is Google's unified ML and generative AI platform. Assured Workloads is the compliance posture that maps Vertex (and the rest of Google Cloud) to FedRAMP and other regulated environments. Vertex on Assured Workloads at the FedRAMP High posture exposes the Gemini family, Imagen, Codey, and select third-party models, with control plane and data plane constraints to keep the workload inside the accredited boundary.
Vertex's strength is integration with the Google data and analytics stack: BigQuery for warehouse, Vertex AI Workbench for notebooks, Vertex AI Feature Store, and the search and grounding capabilities that connect Gemini to enterprise data. For agencies running Google Workspace at scale or with significant Google Cloud footprints, Vertex on Assured Workloads is the natural choice.
Side-by-side comparison
| Dimension | AWS Bedrock (GovCloud) | Azure OpenAI (Government) | Google Vertex (Assured Workloads) |
|---|---|---|---|
| FedRAMP High | Yes | Yes | Yes |
| DoD IL4 | Yes | Yes | Expanding |
| DoD IL5 | Expanding model coverage | Yes — most mature IL5 LLM | Roadmap |
| Headline models | Anthropic Claude, Meta Llama, Amazon Nova/Titan, Mistral, Cohere, AI21 | OpenAI GPT-4o, GPT-4.1, o-series reasoning, embeddings | Google Gemini family, Imagen, Codey, select third-party |
| Model breadth | Highest — multi-vendor by design | Narrower — OpenAI family | Google-led plus selected partners |
| Fine-tuning | Supported on Titan, Nova, Llama, Cohere (varies by region) | Supported on selected GPT models | Supported on Gemini variants and adapter tuning |
| RAG / Knowledge | Bedrock Knowledge Bases (managed) | Azure AI Search + on Your Data | Vertex AI Search + grounding |
| Guardrails / safety | Bedrock Guardrails | Azure AI Content Safety | Vertex AI safety filters |
| Agent framework | Bedrock Agents | Azure AI Foundry Agents / Semantic Kernel | Vertex AI Agent Builder |
| Pricing pattern | Per-token by model provider; on-demand and provisioned throughput | Per-token; PTU (Provisioned Throughput Units) for steady-state | Per-character / per-token; provisioned throughput for steady-state |
| GovCloud premium vs commercial | ~25-50% on-demand | ~25-50% on-demand | ~25-50% on-demand |
| Data residency | US persons, US regions, no cross-region data movement | US persons, US regions, no model training on customer data | US persons, US regions, customer data isolated from model training |
| Identity integration | IAM, IAM Identity Center, federation to agency IDP | Entra ID Government, native federation | Cloud Identity, federation to agency IDP |
| Strongest fit when | Need Claude in GovCloud, multi-vendor model strategy, AWS-native estate | Need OpenAI models, IL5 today, Microsoft-heavy estate | Need Gemini, BigQuery integration, Google-heavy estate |
Model availability — the real differentiator
The model catalog is where these platforms differ most. Bedrock wins on breadth: it is a direct platform of the three with first-party access to Anthropic Claude in an accredited US-government environment, alongside Meta Llama, Mistral, Cohere, AI21, Amazon Titan, and Amazon Nova. For a federal program that wants to mix-and-match models by task — Claude for high-reasoning analyst workflows, Llama for high-volume bulk classification, Cohere for embedding — Bedrock is uniquely positioned.
Azure OpenAI Government is narrower but deeper on the OpenAI family. GPT-4o, GPT-4.1, the o-series reasoning models, embeddings, DALL-E, and Whisper — all in an IL5-authorized environment. For programs that have standardized on OpenAI in the rest of the agency or in commercial life, this is the cleanest extension. The IL5 authorization is the differentiator here: it is the most established LLM IL5 path in the market.
Vertex on Assured Workloads centers on Gemini. Gemini's strengths in long-context reasoning (multi-million token windows for some variants), code generation, and multimodal grounding make it the right pick for agencies with these specific workload patterns — and the BigQuery integration is unmatched if the agency data is already in BigQuery or being moved there.
IL levels — how this evolves through 2026
All three platforms hold FedRAMP High and IL4 (or equivalent) for the underlying compute. IL5 LLM coverage is where they diverge today:
- Azure OpenAI Government has the most mature IL5 LLM authorization, covering selected OpenAI GPT models. This is the default IL5 LLM platform for DoD programs in 2026.
- Bedrock at IL5 is expanding rapidly. The underlying GovCloud regions hold IL5 P-A; the work to extend specific Bedrock models into IL5 authorization is in progress and the catalog is growing through 2026.
- Vertex on Assured Workloads is moving toward broader IL coverage. Status changes month-to-month — always verify against the current Google Cloud compliance documentation and the DoD authorization registry before designing for IL5.
Pricing patterns
All three platforms charge a government region premium over the commercial equivalent — typically 25-50% higher per token for the same model in the accredited region. The pricing structures:
- Bedrock charges per-token, with rates set per model provider (Claude rates differ from Llama rates differ from Nova rates). Provisioned Throughput is available for steady-state workloads where reserved capacity meaningfully lowers effective rates.
- Azure OpenAI Government charges per-token on-demand, with Provisioned Throughput Units (PTUs) as the reserved-capacity option. PTUs are the way to make a steady-state production workload economic — at scale, the per-token effective rate under PTU is meaningfully below on-demand.
- Vertex uses per-character or per-token pricing depending on model, with provisioned throughput options for production. BigQuery ML integration can shift some inference cost into the warehouse layer for batch workloads.
For any production federal LLM workload with steady traffic, the right pricing posture is reserved or provisioned throughput, not on-demand. The on-demand premium in GovCloud regions makes uncommitted production deployment expensive fast.
Fine-tuning and data controls
All three platforms support fine-tuning in the accredited region with strict data residency: training data and the resulting model artifacts stay inside the boundary, US-persons-only privileged access, and no use of customer data for upstream model training. Specifics:
- Bedrock supports fine-tuning on Titan, Nova, Llama, and Cohere models in GovCloud (verify current model coverage before design).
- Azure OpenAI Government supports fine-tuning for selected GPT models in the government region.
- Vertex on Assured Workloads supports full fine-tuning and adapter (LoRA-style) tuning for Gemini variants.
For most federal AI use cases, retrieval-augmented generation (RAG) with a strong embedding strategy is a better starting point than fine-tuning. All three platforms have first-class managed RAG capabilities (Bedrock Knowledge Bases, Azure AI Search "On Your Data", Vertex AI Search) that get you 80% of the customization benefit with a fraction of the operational complexity.
How Precision Federal picks for a given workload
- Customer first. If the customer is DoD and needs IL5 today, Azure OpenAI Government is the default. If the customer is a civilian agency on AWS, Bedrock GovCloud is the default. If the customer is on Google Cloud or has a heavy BigQuery footprint, Vertex on Assured Workloads is the default.
- Model second. If the workload needs Claude (which we believe is currently the strongest model for many federal analyst-augmentation use cases), Bedrock is a direct accredited path. If it needs OpenAI GPT specifically, Azure is a direct path. If it needs Gemini, Vertex is a direct path.
- Footprint third. Don't ignore the existing agency cloud. The cost of running an LLM workload on a cloud the agency does not already use is high — identity integration, network paths, ConMon, billing, training all add up.
- Cost fourth. Reserved capacity changes the math substantially. Run the on-demand vs reserved comparison for the actual workload pattern before locking in the platform.
Internal links
Related: FedRAMP High vs IL5 for AI workloads, AWS GovCloud vs Azure Government for ML, Claude vs GPT-4 for federal use cases. Capabilities: agentic AI, applied ML, cloud architecture. Insights: FedRAMP LLM deployment 2026, agentic AI for federal compliance automation. Vehicles: OASIS+, Alliant 2, OTA consortia.
Get a vendor-neutral recommendation
If you are scoping a federal LLM deployment and want a 30-minute, vendor-neutral conversation about which platform fits your workload, customer, and accreditation posture, email the founder directly. We deliver on all three and have no incentive to push you to one over the others.