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Today, many organizations are pushing beyond existing limits for telemetry volume. Systems are ever-more distributed and generative AI workloads produce enormous amounts of data. As telemetry volumes grow, observability pipelines must become more efficient.
At scale, telemetry egress directly impacts observability spend. Cloud providers charge per gigabyte of data transferred across regions or providers, and those bytes add up quickly. The protocol used to encode telemetry determines how much data is sent over the network. Even modest improvements in encoding efficiency (i.e. the protocol) can translate into significant cost savings. However, the OpenTelemetry Protocol (OTLP) was not initially optimized for performance. Instead, it prioritized interoperability and easy adoption.
Today the OpenTelemetry community is exploring OTAP, a new stateful protocol for transmitting OpenTelemetry data based on Apache Arrow. By using columnar encoding and maintaining state throughout a stream, OTAP avoids repeatedly sending the same metadata, reducing payload size and network transfer. However, because OTAP relies on long-lived stateful streams rather than independent requests, there is additional architectural and operational complexity in its implementation. There are further challenges to larger adoption by the community; for example, Apache Arrow support varies significantly across languages.
Protocol design today is critical to efficiently scaling your systems. In this talk we will explore how protocol design affects telemetry egress and overall observability cost. We will go over some strategies for improving encoding efficiency, compare stateless and stateful approaches, and discuss the potential benefits and drawbacks of adopting a protocol like OTAP. Join us to learn more about how your protocol decisions can influence your costs over time.
