vndr / systems & delivery

Cloud infrastructure with a reason

Choose the right mix of cloud services, dedicated infrastructure, and automation for the workload you actually have.

Choose your operating model

Cloud is a means, not a destination. We help you balance speed, control, economics, and compliance around the workload.

PUBLIC CLOUDPRIVATE CLOUDHYBRIDDEDICATED

Cloud is a means, not a strategy

We help you select and shape an operating model that balances speed, reliability, security, and spend—whether that means public cloud, a hybrid setup, or dedicated capacity.

Cloud foundations for AI workloads

AI implementation often introduces new requirements for data access, accelerated compute, privacy, and variable demand. We help you choose a proportionate design for the use case.

  • Secure access to models and business data
  • Retrieval and vector-search infrastructure
  • Autoscaling and rate-limit strategies
  • Usage monitoring and cost controls
  • Data retention and privacy boundaries
  • Fallback paths when models or providers fail

Cloud work that starts with the workload

We can help with a focused readiness review, migration plan, infrastructure implementation, or an operating-model reset. The output is a set of decisions your team can act on—not a recommendation to use more cloud services.

  • Workload and dependency assessment
  • Migration sequencing and risk plan
  • Network, identity, and security foundations
  • Backup, recovery, and resilience design
  • Resource and cost optimization review
  • Operational documentation and handover

Choose cloud with intent

Cloud can improve speed and flexibility, but only when the operating model matches the workload. We help you make that choice deliberately.

Cloud is a good fit when

  • Scalability is required (e.g., seasonal traffic, startups, SaaS applications).
  • Initial capital expenditure (CapEx) needs to be minimized.
  • Remote access and global reach are essential.
  • Fast deployment is a priority.
  • Automation and managed services reduce operational overhead.

Make the model work

  • Choose the right cloud model:
    • Public Cloud (AWS, Azure, GCP) for cost-efficiency and scalability.
    • Private Cloud self-hosted OpenStack, VMware) for security and control.
    • Hybrid Cloud (mix of both) for compliance and flexibility.
  • Select a pricing model:
    • Pay-as-you-go for dynamic workloads.
    • Reserved instances for long-term cost savings.
    • Spot instances for cost-efficient batch processing.
  • Use cloud-native services:
    • Compute: EC2, Lambda, Kubernetes.
    • Storage: S3, Azure Blob, Google Cloud Storage.
    • Databases: RDS, DynamoDB, Firestore.
    • Networking: Load Balancers, VPC, VPN.

AI-ready cloud foundations

  • Discovery: map data access, model providers, privacy boundaries, and expected usage before choosing infrastructure.
  • Monitoring: track latency, errors, token usage, model quality, and cost alongside normal service telemetry.
  • Security: isolate sensitive data, manage model credentials, and define fallback or human-review paths.

Know when dedicated wins

Dedicated capacity can provide predictable performance, control, and economics for the right workload.

Dedicated is worth considering when

  • Dedicated hardware is preferable when:
    • Performance is critical (e.g., AI training, high-frequency trading).
    • Regulatory compliance requires physical control over data.
    • Long-term cost predictability is necessary.
    • High network throughput is essential.
    • Bare-metal access is required (e.g., specialized GPUs, FPGA workloads).

Operate it deliberately

  • Choose the right hosting model:
    • On-premises data center for full control.
    • Colocation to save on facility costs.
    • Bare-metal cloud for short-term needs (e.g., IBM Bare Metal Servers).
  • Optimize infrastructure:
    • Use load balancers for redundancy.
    • Implement automation (Ansible, Puppet, Terraform).
    • Invest in monitoring tools (Nagios, Zabbix, Prometheus).

AI performance and control

  • Accelerated workloads: consider dedicated CPU, GPU, or other specialized capacity when latency and throughput justify it.
  • Predictable operations: combine infrastructure metrics with model and application signals to detect anomalies early.
  • Controlled spend: use quotas, rate limits, batching, and workload-aware scaling to keep experimentation sustainable.

Cloud and dedicated: choose by workload

Comparison of Cloud Services and Dedicated Hardware across various aspects
AspectCloud ServicesDedicated Hardware
CostPay-as-you-go, no upfront cost, but can become expensive at scale.High initial cost but cheaper in the long run for stable workloads.
ScalabilityInstant scaling up/down.Limited to purchased hardware, scaling is slow.
PerformanceGood for most workloads, but shared resources can cause inconsistencies.High and predictable performance with dedicated resources.
Security & ComplianceManaged security, but less control over data.Full control over security policies and data residency.
Management OverheadFully managed options reduce IT burden.Requires dedicated IT staff for maintenance and support.
CustomizationLimited to cloud provider configurations.Full customization for hardware and networking.
Disaster RecoveryBuilt-in backups, multi-region redundancy.Must implement own backup and recovery solutions.
Latency & NetworkCan be higher due to shared infrastructure.Lower latency, especially for local/private networks.
Make infrastructure intentional

Find the right home for your workload.

Discuss your platform