Cloud & InfrastructureAWS
The cloud platform that runs the infrastructure behind the majority of the internet.
Amazon Web Services (AWS) is the world's largest cloud computing platform, offering over 200 services spanning compute, storage, databases, networking, machine learning, security, and developer tooling. It is used by companies at every stage, from solo founders running a single EC2 instance to enterprises with multi-region architectures processing billions of transactions daily. For software teams specifically, AWS solves the problem of owning and operating physical infrastructure: teams can provision servers, managed databases, queues, CDN, and object storage within minutes and pay only for what they use, which fundamentally changes the economics of building and scaling software products. Key differentiators include the depth and maturity of services like RDS, Lambda, S3, CloudFront, and SageMaker, the global reach of availability zones and regions, and the extensive IAM permission system that makes compliance and least-privilege access practical at scale. AWS is where most serious production workloads eventually land, either by design or by default.
Engineering teams at software companies of any size who need reliable, scalable cloud infrastructure without managing physical hardware, particularly those building production applications that require databases, compute, and storage in a managed environment. The right moment to move workloads to AWS is as soon as the product needs reliable uptime, automated backups, or the ability to scale beyond what a single server can handle.
Up to $100,000 in credits or 20-50% off your monthly spend through our partnership.
Subject to partner eligibility criteria. Savings estimates reflect maximum potential value.
AWSin depth.
Elastic Compute (EC2 and Lambda)
Run virtual machines with granular instance type selection or deploy serverless functions that scale to zero when idle. Engineering teams use EC2 for stateful workloads and Lambda for event-driven processing, background jobs, and APIs with bursty traffic patterns.
Managed Databases (RDS, Aurora, DynamoDB)
RDS and Aurora provide fully managed relational databases (Postgres, MySQL, and Aurora-native engines) with automated backups, read replicas, and Multi-AZ failover. DynamoDB offers a serverless NoSQL option that scales horizontally without manual sharding or capacity planning.
S3 Object Storage
Durable, infinitely scalable object storage for static assets, backups, data lake inputs, and user-uploaded content, accessible from any AWS service or the public internet. S3 is foundational to almost every AWS architecture.
IAM and Security Controls
Fine-grained identity and access management lets teams implement least-privilege policies across every AWS resource, which is essential for SOC 2 and ISO 27001 compliance. IAM roles, policies, and service control policies make it possible to model complex organizational permission structures without writing custom auth logic.
CloudFormation and CDK (Infrastructure as Code)
Define and provision AWS infrastructure in code using CloudFormation templates or the higher-level AWS CDK in TypeScript, Python, or Java. This makes infrastructure reproducible, version-controlled, and deployable across environments without manual console configuration.
AWS integrates natively with GitHub Actions, CircleCI, Terraform, Datadog, Splunk, Snowflake, Databricks, and thousands of other tools through its partner ecosystem and open APIs. In virtually every modern software stack, AWS functions as the foundational infrastructure layer underneath application code, databases, and the services that run on top of them.
Commonuse cases.
Deploying and scaling a production SaaS backend
Engineering teams use EC2 or ECS for application servers, RDS for the database, ElastiCache for caching, and CloudFront for CDN, composing a production-grade architecture from managed pieces without running physical servers. This setup can scale from hundreds to millions of users by adjusting instance sizes and replica counts rather than re-architecting.
Running ML model training and inference at scale
Data and ML teams use SageMaker for managed training pipelines, GPU instances for compute-intensive workloads, and S3 as the data layer, keeping the entire ML workflow within a single platform with native IAM integration. This avoids the operational overhead of managing GPU clusters and ML infrastructure independently.
Building a compliant, multi-region architecture for enterprise customers
Teams pursuing SOC 2 or HIPAA compliance use AWS's compliance-ready services, audit logging via CloudTrail, and multi-region deployments to meet enterprise customer security and data residency requirements. Having the audit trail and access controls already instrumented significantly accelerates the compliance certification process.
Three stepsto activate.
Check eligibility
Each partner maintains independent qualification criteria. We assess your profile and determine which offers you qualify for.
Schedule a briefing
Book a call with our partnerships team to discuss your stack requirements and walk through the activation process.
Activate credits
Once approved by the partner, credits are deployed to your account. Timelines vary by partner.
BearPlex maintains partnerships with leading technology providers to facilitate access to exclusive programs for our clients. All offers are subject to each partner's independent eligibility requirements, approval processes, and terms of service. Savings figures represent maximum potential value and may vary based on qualification, usage, and partner-specific criteria. BearPlex acts as a facilitation partner and does not guarantee approval or specific credit amounts. Offer availability and terms may change at the partner's discretion.
