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In modern microservice architectures, which are increasingly implemented in high-load, distributed environmentswith dynamic scalability, there is a growing interest in models of direct interaction between individual services without the useof traditional proxy solutions – so-called proxyless approaches. This interaction model reduces system response time, avoidsdelays caused by intermediary routing, optimizes infrastructure costs, decreases the number of potential points of failure, andensures greater architectural flexibility, scalability, control over data flows, and compliance with cloud-native principles. Inthis context, the implementation of data compression algorithms becomes particularly important as one of the key means ofoptimizing information exchange between services. Compression significantly reduces the volume of transmitted data, lowersnetwork load, minimizes request processing latency, and helps reduce resource consumption during intensive inter-servicetraffic.This paper provides a theoretical analysis of the impact of data compression on the quality, performance, andreliability of direct communication within a microservice architecture. The focus is on lossless algorithms such as GZIP andSnappy, which are among the most widely used in cloud-native environments supporting REST and gRPC. The analysisexplores the specifics of their integration, the dependence of efficiency on data format and structure, the type of API requests(single or batch), as well as network latency levels and computational overhead. The advantages of GZIP are highlighted forhigh-load scenarios that require deep compression, while Snappy is preferred in cases with strict latency constraints and apriority on speed. Potential limitations related to service compatibility, CPU overhead, and configuration flexibility whenmanaging compression parameters manually are also identified. The importance of configuration consistency on both ends ofservice interaction is emphasized, including proper header encoding and support for the required formats.The conclusion is drawn that an adaptive and context-aware approach to selecting a compression algorithm –considering the nature of the API, the structure of the load, the interaction topology, processing priorities, and networkcharacteristics–is critically important to ensure stable, reliable, and efficient microservice operation within proxylessarchitectures of modern cloud and hybrid platforms.