Amazon Web Services
Bedrock, S3, Lambda, VPC, KMS, and PrivateLink-style patterns for agents and pipelines in your AWS account.
Learn more →Partners
Your cloud. Our front layer. No lock-in.
Customers choose AWS, Azure, or GCP for compute, storage, and models. DynasAI makes agents, data processing, and governance easy on top — with residency and cost recommendations, not a forced hyperscaler.
Why enterprises keep three clouds in play
Agent workloads follow data gravity, identity, and sovereignty — not a single vendor logo. We architect for the mix you already have.
- Primary backends we design for: AWS, Azure, and Google Cloud
- 3
- Bring your own VPC, tenant, or data lake — or start from a managed template
- BYO
- Region and residency follow your GDPR or US privacy requirements
- EU / US
- We recommend the cheaper fit for each workload — models, storage, and pipelines
- Cost
Simple for users. Serious in the backend.
Business and engineering teams work in DynasAI: visual workflows, data evaluation, templates, and audit trails. They should not have to assemble Bedrock, Vertex, and Azure OpenAI by hand.
Underneath, we wire the hyperscaler you select. Identity can follow Entra ID, IAM, or Cloud Identity. Data can stay in S3, ADLS, or GCS / BigQuery. Models stay behind your private endpoints. That is how we avoid lock-in and keep customer data control.
Hyperscaler backends
We suggest the cost-effective service mix for each use case — you approve the account and region.
Microsoft Azure
Azure OpenAI, Entra ID, Fabric / ADLS, and enterprise app connectors for teams already on Microsoft 365.
Learn more →
Google Cloud
Vertex AI, BigQuery, Cloud Storage, and document AI-style processing for analytics-heavy estates.
Learn more →Databricks
Lakehouse context and model ops alongside the DynasAI front layer — not a replacement for your lake.
Snowflake
Secure data sharing and warehouse retrieval so agents read governed tables instead of shadow copies.
Enterprise SaaS
Salesforce, ServiceNow, SAP, and custom APIs — connected with identity-aware tool access.
Learn more →How we pick a cost-effective stack
Advisory first, then implementation on the same platform you will operate.
Map data gravity
Where the raw sources already live usually wins. Moving petabytes to a new cloud is rarely the cheapest AI plan.
Match identity & compliance
Entra-centric enterprises often stay on Azure. GDPR residency may pin EU data to EU regions on any of the three.
Right-size models & pipelines
Batch processing, RAG indexes, and agent calls have different cost curves. We recommend managed vs self-hosted per workload.
Keep an exit ramp
Open connectors, your VPC, and no training on your corpus. Switching a model provider should not mean rewriting the business layer.
Engagement models
Customer-owned cloud
Agents and data processing run in your AWS, Azure, or GCP project. DynasAI is the control plane you log into.
Managed template
We provision a reference architecture in a dedicated tenant. You still choose region and can migrate to BYO later.
Hybrid
Sensitive stores stay on-prem or private cloud; the front layer and selected models run in public cloud with private networking.
SI & ISV partners
Systems integrators and software vendors can co-deliver on DynasAI. Talk to us about a partner motion.
Get a cloud and residency recommendation
Bring your current AWS, Azure, or GCP footprint. We will map a cost-aware architecture and a governed DynasAI front layer.
