Devotica is an AWS Advanced Tier Services Partner. We migrate workloads to Amazon Web Services, build data platforms on it, and run what we build — with AWS-certified engineers and the AWS Well-Architected Framework behind every design.
Advanced Tier is awarded by the AWS Partner Network to services partners with AWS-certified staff, proven customer launches and a sustained delivery record on AWS. You can verify our status on the AWS Partner Solutions Finder.
Each practice has its own page covering what we deliver, the AWS services we use and why, and the customer work behind it.
Move on-premises, VMware and legacy database workloads to AWS, then modernize them onto containers, serverless and managed databases.
Governed data lakes and lakehouses, real-time streaming pipelines and dashboards on AWS, from ingestion to insight.
CI/CD, infrastructure as code, observability and cost control on AWS, with 24/7 operations for the workloads we run.
Also on AWS: Generative AI on Amazon Bedrock and database engineering on Amazon Aurora, Amazon RDS and Amazon DynamoDB.
We pick managed AWS services first, so your team runs less undifferentiated infrastructure.
Managed Kubernetes and container orchestration for modernized applications, with no control plane to run.
Event-driven, pay-per-use compute for integrations, pipelines and APIs that don't need servers running around the clock.
Managed relational engines with Multi-AZ failover, automated backups and read replicas. Our default target for commercial database migrations.
Serverless key-value storage for high-throughput access patterns that need single-digit-millisecond latency at any scale.
An open-format data lake with a shared catalog and fine-grained access control, the foundation of every data platform we build.
Warehouse, serverless SQL and Spark. We match each engine to the workload, so analytics stay fast without paying for idle compute.
Access to leading foundation models behind AWS security controls, for RAG applications and agents grounded in your data.
Build, train and deploy ML models on the same governed data the analytics platform already serves.
Repeatable releases and infrastructure defined as code, reviewed like application code and identical from dev to production.
A governed multi-account landing zone, with the monitoring and cost visibility needed to run it well.
We follow the same four phases on every AWS engagement, with designs reviewed against the six pillars of the AWS Well-Architected Framework.
Discovery of your current estate, a Well-Architected review, and a business case with TCO on AWS.
Landing zone, security baseline, target architecture and a wave plan your team signs off.
Workloads moved in tested waves with rehearsed cutovers, then refactored where it pays off.
24/7 monitoring, incident response and continuous cost and performance tuning.
Each case study covers the problem, the AWS architecture we delivered and the results it produced.
An Amazon Redshift billing data mart with automated Amazon EMR and Amazon MWAA pipelines replaced manual monthly invoicing across 300+ lenders.
Re-architected ~900 Amazon Redshift queries to a hybrid lakehouse on Amazon EMR and Apache Iceberg on Amazon S3, with zero downtime at cutover.
Unified Postgres, MongoDB and third-party data into a GDPR-compliant AWS data lake with embedded Amazon QuickSight dashboards.
Replaced an overloaded database with a pay-per-use Amazon Data Firehose pipeline ingesting 3.3 billion records a month.
Tell us about the workload. We'll reply with a one-page target architecture on AWS and a 30-day plan.
Talk to an AWS architect →