We help enterprises evaluate, deploy, and govern foundation models — from Amazon Bedrock's multi-model catalog to Azure OpenAI & Copilot and Google Gemini — and engineer the migration path to the platform that actually fits your security, cost, and compliance requirements.
Amazon Bedrock is AWS's fully managed generative AI service. It gives you one API in front of foundation models from Anthropic (Claude), Meta (Llama), Mistral AI, AI21 Labs, Cohere, Stability AI, and Amazon's own Nova family — plus managed Knowledge Bases for retrieval-augmented generation (RAG), Agents for multi-step task automation, and Guardrails for content safety. Unlike Microsoft Copilot (a per-seat productivity product) or a single-vendor API, Bedrock is infrastructure: you pay per token, keep data inside your own AWS account, and switch models without rewriting your application.
Filter by platform to compare context windows, ideal use cases, and what makes each model family distinct.
Complex reasoning, coding agents, long-document and contract analysis with citations.
Prompt caching and extended-thinking modes reduce cost on repeated context.
High-volume, cost-sensitive workloads — classification, summarization, chat at scale.
Amazon's own model family, typically the lowest cost-per-token in the Bedrock catalog.
Fine-tuning and customization where you want more control over the underlying weights.
Open-weight architecture available directly through the managed Bedrock API.
Multilingual support and native tool/function calling for agentic workflows.
Strong price/performance for European-language customer support use cases.
Long-context retrieval over large document sets at lower inference cost.
Hybrid transformer/state-space architecture built for context-heavy workloads.
Enterprise semantic search and the embedding layer behind RAG pipelines.
Purpose-built for retrieval quality rather than open-ended generation.
General-purpose reasoning and vision tasks tightly coupled to the Microsoft stack.
Priced per-token via Azure AI Foundry; identity and networking run through Entra ID and VNets.
Embedded AI assistance inside Word, Excel, Teams, and Outlook.
Packaged on top of Azure OpenAI + Microsoft Graph and licensed per named user, per month.
In-IDE code completion and developer productivity.
Also billed per-seat; usage is not metered by token the way Bedrock or Azure OpenAI API calls are.
Low-latency, on-device, or cost-constrained inference.
Compact open-weight models tuned for efficiency over raw capability.
Massive-context reasoning and native multimodal input — video, audio, image, and text together.
The Flash tier is typically the cheapest frontier-class option at high volume.
Enterprise-grade image generation for marketing and creative workflows.
Accessed through Vertex AI alongside Gemini in the same governed environment.
Grounding Gemini responses in your own data with Google-scale retrieval infrastructure.
Comparable in role to Bedrock Knowledge Bases, built for the Google Cloud ecosystem.
Bedrock is a fully managed, serverless layer over foundation models — not another model to learn, but a single API in front of all of them.
Swap Claude for Nova, or Llama for Mistral, by changing a model ID — not your application code or infrastructure.
Point Bedrock at your documents, databases, or APIs and it handles chunking, embeddings, vector storage, and retrieval — no infrastructure to run.
Build multi-step, tool-using assistants that call your APIs, query data, and complete tasks — configured, not hand-coded.
Content filtering, denied-topic controls, PII redaction, and Automated Reasoning checks for hallucination reduction ship as configuration, not custom code.
Prompts and completions are never shared with model providers or used to train base models — encrypted with your own KMS keys, inside your VPC.
Pay per token on-demand, or reserve Provisioned Throughput for predictable high-volume workloads — no capacity planning for GPUs.
Model quality gaps between the major platforms are narrow. The decision usually comes down to governance, pricing model, and how deeply it fits your existing cloud.
| Dimension | Amazon Bedrock | Azure OpenAI & Copilot | Google Vertex & Gemini |
|---|---|---|---|
| Model catalog breadth | 18+ providers, 110+ models (Anthropic, Meta, Mistral, Amazon, AI21, Cohere, Stability) behind one API | OpenAI GPT family plus Microsoft Phi models, deepest on day-one access to new OpenAI releases | Gemini family plus Imagen and open models via Model Garden |
| Pricing model | Consumption-based per-token, plus optional provisioned throughput for predictable high volume | Consumption-based per-token via Azure OpenAI; Copilot itself is a flat per-seat license (~$30/user/mo) | Consumption-based per-token; Flash tier priced for high-volume, cost-sensitive workloads |
| Data usage for training | Prompts and outputs are not shared with model providers and are not used to train base models | Enterprise data is excluded from OpenAI model training under the Azure OpenAI terms | Enterprise data is excluded from model training under Vertex AI enterprise terms |
| Compliance posture | HIPAA eligible, GDPR-aligned, SOC 2, ISO 27001, FedRAMP High in AWS GovCloud | HIPAA, GDPR, SOC 2, ISO, FedRAMP High — deep fit for Microsoft-regulated environments | HIPAA, GDPR, SOC 2, ISO; FedRAMP High coverage is narrower than AWS and Azure |
| Native RAG & agent tooling | Managed Knowledge Bases (RAG), Agents, Guardrails, and Automated Reasoning checks built in | Azure AI Foundry Agent Service, Azure AI Search for retrieval, Content Safety guardrails | Vertex AI Search and Agent Builder, grounding, and safety filters |
| Best fit when... | you want model flexibility, usage-based cost, and deep AWS infrastructure integration | you are Microsoft-first — Entra ID, M365, and want the newest OpenAI models fastest | you run on BigQuery/GCP and need very large context windows at the lowest cost per token |
Compare flat per-seat AI licensing against consumption-based Bedrock pricing for your organization.
Illustrative estimate for planning purposes, not a quote. Assumes 30 USD/user/month flat licensing versus Bedrock consumption pricing at a blended ~$1 per 1M tokens across an average 700-token query, plus a $350/month baseline for managed retrieval infrastructure. Actual cost depends on model mix, prompt length, and caching strategy — talk to us for a workload-specific assessment.
A staged migration that runs in parallel with your existing tools — no big-bang cutover.
We audit current AI spend — Copilot seats, ChatGPT Enterprise licenses, ad hoc API usage — and map each use case to the right foundation model on cost, latency, accuracy, and compliance.
Usage audit, per-seat vs per-token cost modeling, data sensitivity classification
We design the Bedrock environment — VPC endpoints, least-privilege IAM, KMS encryption, and Guardrails policy — before any workload moves.
Multi-account governance, private connectivity, encryption key management
We rebuild your RAG pipelines on Bedrock Knowledge Bases, migrating embeddings, vector stores, and prompt templates to the target models.
Chunking strategy, embedding model selection, retrieval evaluation
Bedrock runs side-by-side with your existing tools while we benchmark accuracy, latency, and cost, and get sign-off from stakeholders before cutover.
A/B evaluation harness, accuracy scoring, latency and cost benchmarking
We cut traffic over, then apply FinOps discipline — provisioned throughput vs on-demand, batch inference, model routing — with continuous monitoring in place.
Cost allocation tagging, model routing rules, usage dashboards
Mix and match models behind one API — Claude for reasoning, Nova for high-volume tasks, Llama for fine-tuning — without re-architecting your application.
Pay for tokens processed, not idle seats. Provisioned throughput adds predictable pricing once volume justifies it.
Prompts and completions stay inside your AWS account and VPC. Nothing is shared with model providers or used to train base models.
Guardrails, PII redaction, denied-topic controls, and Automated Reasoning checks ship natively — no bolt-on compliance layer required.
Managed Knowledge Bases and Agents remove the undifferentiated infrastructure work of building RAG and tool-use from scratch.
HIPAA eligibility, GDPR alignment, SOC 2, ISO 27001, and FedRAMP High in GovCloud — audit-ready from day one.
B2B SaaS company with about 1,200 employees running ChatGPT Enterprise seats and several unmanaged OpenAI API integrations
Per-seat AI licensing scaled with headcount regardless of actual usage, there was no centralized governance across AI tools, and sensitive customer data was flowing through consumer-facing AI products without an audit trail.
Amazon Bedrock · Claude · Amazon Nova · Knowledge Bases · Guardrails · CloudWatch
Get a workload-specific assessment of which models and which platform — Bedrock, Azure OpenAI, or Gemini — fit your cost, compliance, and performance needs.