Cloud modernization
Containerization, Kubernetes (EKS, AKS, GKE), managed services, CI/CD and infrastructure as code for mature SaaS platforms that still run on older deployment models.
- Kubernetes
- EKS
- AKS
- GKE
- Terraform
- CI/CD
Cloud and AI modernization
Cloud & AI Modernization for Mature SaaS
We build, modernize and migrate production SaaS and AI workloads across AWS, Azure and Google Cloud.
Where we deploy
AWS
- Amazon Bedrock
- AWS Strands
- EKS
- Lambda
Microsoft Azure
- Azure AI Foundry
- Azure OpenAI
- AKS
- Cosmos DB
Google Cloud
- Vertex AI
- Gemini
- GKE
- Cloud Run
Where cloud fits
Cloud work in service of an AI-native product
We are not a generic cloud consulting firm. Cloud modernization, migration and deployment engineering are the supporting capabilities that let a mature vertical B2B SaaS product run agents, copilots and AI workflows in production, safely and at a defensible cost.
Datics AI — AI product engineering for established vertical B2B SaaS. Most of the platforms we work on already carry customers, contracts, integrations and compliance obligations. Cloud modernization and cloud-to-cloud migration only happen where they unlock the AI capability, reduce infrastructure cost or satisfy a security review.
Capabilities
Cloud modernization, migration and production AI
Cloud-to-cloud migration
AWS to Azure, Azure to AWS, on-premise to cloud and multi-cloud consolidation, planned around tenancy, data residency and customer contracts rather than a lift-and-shift script.
- AWS to Azure
- On-premise to cloud
- Multi-cloud
- Data residency
AI workload migration
Moving models, retrieval pipelines and agent workloads between providers and regions: Amazon Bedrock, Azure AI Foundry, Google Vertex AI, self-hosted and local models.
- Amazon Bedrock
- Azure AI
- Vertex AI
- Self-hosted models
Production AI deployment
Inference endpoints, vector stores, evaluation harnesses, observability and rollout controls, so an AI capability can be supported by the same team that supports the product.
- Inference
- Vector search
- Evaluations
- Observability
Data platform migration
Warehouse and lakehouse migration, governed access and pipeline modernization on Databricks, Snowflake, BigQuery and Redshift so AI features read trustworthy data.
- Databricks
- Unity Catalog
- BigQuery
- Snowflake
- Redshift
Infrastructure and inference economics
Cloud cost optimization and AI inference economics: model routing, caching, batching, right-sizing and committed-use planning, measured as cost per successful task.
- Cost optimization
- Model routing
- Caching
- Right-sizing
Security and deployment boundaries
Tenant isolation, private networking, key management, audit trails and customer-controlled deployment, including VPC, self-hosted and air-gapped environments.
- Tenant isolation
- VPC
- KMS
- Audit trails
- Air-gapped
Platforms
AWS, Microsoft Azure and Google Cloud
We run production workloads on all three, plus customer-controlled deployment inside the buyer's own cloud account where enterprise contracts require it.
AWS
AWS Select Tier partner. ECS and EKS, Lambda, RDS and Aurora, S3, Amazon Bedrock and AWS Strands for agent workloads.
- Amazon Bedrock
- AWS Strands
- EKS
- Lambda
- Aurora
- SageMaker
Microsoft Azure
Azure AI Foundry and Azure OpenAI, AKS, Azure Functions, Azure SQL and Cosmos DB, with Entra ID identity and private endpoints for enterprise buyers.
- Azure AI Foundry
- Azure OpenAI
- AKS
- Cosmos DB
- Entra ID
Google Cloud
Vertex AI and Gemini models, GKE, Cloud Run, BigQuery and customer-controlled deployment inside a client's own Google Cloud project.
- Vertex AI
- Gemini
- GKE
- Cloud Run
- BigQuery
Migration approach
How a cloud-to-cloud or AI workload migration runs
Incremental, reversible and scheduled around your customers rather than a cutover weekend.
- 01
Assess
Inventory workloads, data flows, tenancy model, integrations and contractual constraints. Identify what moves, what stays and what must be modernized first.
- 02
Design
Target architecture, landing zone, security boundaries, deployment topology and an inference plan with a cost model attached to it.
- 03
Migrate
Incremental migration behind feature flags and dual-run periods, so customers keep working while workloads move.
- 04
Operate
Observability, evaluations, cost monitoring and runbooks handed to your team, not retained as a dependency on us.
Your customers already expect AI from your product.
Let us make it real without rebuilding what already works.