How Honeycomb Supercharges OpenTelemetry for AI

It has become common knowledge that the nature of software development has changed as AI-code generation and agent-based features gain adoption. In perhaps a more subtle shift, the fundamentals of software instrumentation are changing too. As OpenTelemetry becomes the standard instrumentation layer across enterprises, with thousands of developers (many from Honeycomb) actively contributing to it, the nature of the telemetry data captured itself is evolving to meet the growing demand for rich context.

AI rewards rich instrumentation

Understanding modern software demands more context and granularity per request than ever. With AI-assistance in play, rich instrumentation reveals purpose when there is no human contributor to ask. And with agentic systems, a plethora of new fields such as inference, prompt, token, and RAG context come into play. Some of the factors that are driving scale and that inevitably lead to complexity:

• Contextual detail growth: The integration of LLMs, new AI features, and accelerated feature releases will cause both the contextual data and the number of relevant fields across applications to grow indefinitely.
• Interteam proliferation: As OpenTelemetry is adopted as a cross-team standard, the proliferation of codependent services and infrastructure will multiply the factor of shared complexity across large enterprise teams.
• Competitive edge for developers: Frontline engineering teams need the freedom to conduct ad-hoc analysis on their own code. Any custom fields they add give them faster insight into the performance and reliability of the plethora of features they are launching. In the age of AI, this feedback loop is the competitive edge that sets their features apart.

Thankfully, OpenTelemetry is designed for the flexibility and unification these new fields demand. As enterprises move beyond initial OpenTelemetry adoption, they need an observability system that scales reasonably. But you can’t assume that your observability tooling will keep up.

The economic trap observability tools create

OpenTelemetry's value is flexibility. It gives teams the freedom to uniformly capture whatever context is relevant to their code, infrastructure, and business logic without getting locked into any particular observability backend. That's the entire point.

But most observability solutions charge extra for custom tags, ingested bytes, metric cardinality, or exploring your data. The more fields you add, the more your costs grow.

As your telemetry matures to capture more context, you face a difficult choice: control costs and limit your understanding of complex systems, or improve coverage but accept runaway bills. Both options undermine why you adopted OpenTelemetry in the first place.

Using Honeycomb for the most scalable OpenTelemetry implementations

Honeycomb was built to solve exactly this problem. It was designed to be OpenTelemetry-native and allow it to scale with high-cardinality data and unlimited unique values. For starters, Honeycomb integrates seamlessly with existing agents and Collectors, simplifying telemetry ingestion from current systems. It supports enterprises at any stage with clear migration guides and best practices, while also actively shaping the OpenTelemetry ecosystem as Maintainers, Approvers, and Ambassadors.

But on top of this, Honeycomb offers the most sustainable scaling path for OpenTelemetry maturity and complexity. Our observability architecture is designed to strategically support the context-enriched future state of telemetry in the following ways:

Reduced overhead concern: Honeycomb’s ability to track thousands of fields, large context windows, and infinite unique values gives enterprises the freedom to capture the custom context they need, today and tomorrow. Unlike other solutions that are driven and budgeted primarily by custom tags, byte volume, and metric analysis, Honeycomb doesn't require dropping potential context clues to control cost economics.

A logical telemetry system: Honeycomb's insight as an OpenTelemetry contributor allows us to partner with customers on OpenTelemetry maturation, delivering teams the most sustainable system to map growing code complexity into rich, useful telemetry that they own. Instead of continually wrestling with data context, teams can set reasonable thresholds and capture any details they need.

Empowering frontline builders: We supercharge developers by giving them the individual freedom to add fields and receive instant performance feedback in seconds, without concerns about overhead. Without the cognitive load barrier to fast feedback, Honeycomb users can proactively resolve performance or reliability issues before they occur.

If you’re limiting valuable context, you’re not unlocking OpenTelemetry

Netflix Compute’s Artem Tkachuk shared this at Kubecon: “The more (attribution) you can pour into every span of the data that is relevant, the better. Because someday, you will be able to group by (that) and surface some important issue.”

If your observability platform forces you to limit attribute capture or analysis, you're not getting the full value of OpenTelemetry. You're just shifting the constraint from the instrumentation layer to the platform.

Alternatively, Honeycomb's unique architecture captures the full value of your improving instrumentation. We built for this exact use case: high-cardinality, exploratory debugging at production scale that handles trillions of events without penalties. As your OpenTelemetry adoption matures and telemetry complexity grows, this advantage becomes more strategically relevant, not less.

The question for engineering leaders isn't whether to adopt OpenTelemetry. It's whether your observability platform can support it at scale without forcing economic tradeoffs that undermine the entire strategy.