Run APIs, indexers, databases, caches, analytics, and workers on isolated dedicated servers with role-based capacity, direct OS control, traceable runtime behavior, and regional placement.
Place application endpoints and data services in regions that match user demand and operating requirements.
Assign API and RPC-facing workloads to clear capacity boundaries so teams can trace use and runtime contention.
Allocate CPU, RAM, storage I/O, and bandwidth around indexer, database, cache, analytics, and worker demand.
Use direct OS access to set service configuration, observability, access controls, and release workflows.
A production dApp rarely has one uniform infrastructure profile. User-facing APIs depend on predictable response time, while indexers and analytics consume CPU and storage I/O, and databases and caches need memory discipline. A single capacity pool can hide which service is driving contention and where capacity should change first.
Our team provides isolated hardware, configurable CPU, RAM, storage, bandwidth choices, direct operating-system access, and regional deployment options. Infrastructure teams can tune each service to its bottleneck, trace runtime behavior against a clear hardware boundary, and adjust capacity while retaining control of the deployment environment.
Assign API, indexing, database, cache, analytics, and worker roles according to load, then separate or consolidate them as demand changes. Melbicom supports the infrastructure layer and hardware, while your team retains control of application code, smart contracts, keys, node or RPC software, application databases, monitoring, deployments, and upgrades.
Trace API latency under changing demand by assigning compute and bandwidth to customer request paths.
Keep RPC software isolated while your team controls clients, keys, monitoring, configurations, and upgrades.
Allocate CPU and storage I/O to event ingestion, transformation, and queries so teams can trace contention.
Size RAM and storage for application databases by write load, query demand, indexes, and recovery plans.
Group or separate services by workload, failure scope, and region so capacity decisions remain traceable.
Run analytics jobs on assigned cores, memory, and storage throughput; preserve user-facing service capacity.
Assign background workers their own capacity so queue and scheduler bursts stay distinct from API load.
Allocate RAM to hot data and session state so cache pressure stays visible during application demand peaks.
