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Teams often presume that trying a new AI observability tool means re-instrumenting code, swapping agents, and arguing about which vendor gets to own the telemetry pipeline. But those presumptions aren’t true when it comes to using OpenTelemetry (aka OTel).
InsightFinder accepts OpenTelemetry Protocol (OTLP) data, so teams can emit telemetry to multiple observability backends—using the same instrumentation already deployed. Additionally, we accept traces from Arize (OpenInference), Temporal, and Datadog (ddtrace) SDKs.
If you’re using OpenTelemetry or generating traces from one of the vendors above, it’s never been easier to find out what InsightFinder can do for you.
OpenTelemetry reduces “rip and replace” risk
Most enterprises have already invested in logs, metrics, and tracing. They have SDKs in services, sampling policies, trace span enrichment, custom metrics, and semantic conventions that match how engineering works. A forced swap of instrumentation frameworks breaks more than code; it breaks operational muscle memory.
OpenTelemetry reduces the risks of “rip and replace” requirements when switching observability tools. It standardizes how system telemetry (logs, metrics, and traces) are produced and exported. In OpenTelemetry, SDKs create telemetry that describes what happened in a given operation, and exporters send that telemetry to a backend of choice. This is the core reason OTLP matters: it lets teams add multiple telemetry destinations without throwing away the first one.
In practical terms, side-by-side evaluation becomes a configuration change, instead of a platform migration. Your team keeps their current SDKs, collector pipelines, and internal telemetry conventions. InsightFinder simply becomes another consumer of the same OTLP data stream.
Why traces are the best lens for LLM and agent complexity
LLM applications behave like distributed systems, but with a non-deterministic twist. They execute multi-step workflows where the business logic is partly deterministic code and partly probabilistic model behavior. That duality creates failure modes that do not show up cleanly in metrics and logs alone.
A trace captures the end-to-end path of a single request across boundaries. Each span represents a unit of work with timing and attributes, and the trace connects those spans into a coherent execution story.
That story is exactly what AI and ML engineers need when debugging real production issues. Consider a typical retrieval augmented generation flow that starts clean in staging but degrades in production. Latency spikes appear only for a subset of tenants. Output quality drops only when a tool call returns a large payload. Costs jump because a fallback model triggers retries during partial outages.
Those aren’t problems that yield to averages. They require a per-request narrative that shows where time, tokens, and branching decisions occurred. Traces provide that narrative without requiring guesswork about which log line matters or which metric dimension to slice next. For non-deterministic systems, that makes traces the most reliable starting point for understanding what happened.
Vendor-neutral SDKs are the safest long-term bet
A vendor-neutral tracing strategy matters even more for AI systems than for traditional microservices. AI stacks evolve quickly. Teams swap model providers, introduce agent frameworks, change orchestration patterns, and add new safety and evaluation layers. Telemetry instrumentation should not need to change every time architecture changes.
OpenTelemetry exists to make instrumentation portable across backends. OTLP then makes export portable across destinations. This is why OpenTelemetry-compatible SDKs are a practical default: they let teams compare reliability platforms based on results, instead of blocking on migration effort.
InsightFinder’s tracing integration guidance reflects this reality. In addition to using OTLP, you can also port over tracing SDKs from Arize via OpenInference, Temporal, and Datadog’s ddtrace.
Existing custom trace attributes should not be thrown away
Many production-grade tracing deployments already include attributes that encode business context. That context is often the difference between a trace that is merely viewable and a trace that is operationally useful.
InsightFinder’s OTLP ingestion is designed to work with the attributes teams already set today, including application-defined fields used for correlation and troubleshooting. In the tested integrations, examples include session and user correlation attributes, as well as LLM-specific fields like prompt, response, token usage, and model metadata.
This matters in real reliability work. An SRE investigating an incident rarely asks, “Is latency high?” They ask, “Is latency high for this tenant, this route, this agent plan, and this tool call?” That question only becomes answerable when the trace preserves the attributes that express tenant, route, agent step, and tool identity. Keeping those attributes also enables better anomaly segmentation and faster diagnosis, because the platform can compare like-for-like requests rather than blending unrelated workloads.
InsightFinder uses Composite AI for better results.
A growing class of “AI” observability & reliability products focus on capturing prompts and responses, adding a trace view, then delegating detection and incident workflows to existing monitoring tools. That pattern can work for experimentation, but it tends to break down when teams need operational outcomes: early anomaly detection, credible root cause hypotheses, prevention, and response
Unlike many other solutions, InsightFinder is not a thin GenAI layer on top of someone else’s stack. InsightFinder is an end-to-end reliability platform spanning anomaly detection, root cause analysis, prediction, and remediation workflows across both traditional services and AI applications. InsightFinder works for both traditional and probabilistic applications.
AI systems don’t run in isolation. They depend on the same databases, queues, identity systems, and network paths that already generate incidents. Reliability requires one model of causality across the whole system, not one dashboard for AI and another for “everything else.”
InsightFinder’s approach is grounded in Composite AI, which combines multiple analytical techniques rather than relying on a single generative model to infer operational truths. Our patented methods for anomaly detection, holistic root cause analysis, and automated incident prevention are designed to use more than just generative AI: providing the right set of tools for the right job.
How to evaluate InsightFinder without rewriting instrumentation
The cleanest evaluation strategy is to keep current tracing SDKs and export OTLP to InsightFinder in parallel. That gives engineering leaders and SRE teams a fair comparison because both platforms see the same production traces, with the same sampling and the same custom attributes. OTLP is explicitly designed for delivery of telemetry between sources, intermediate nodes such as collectors, and backends.
From there, the evaluation should focus on outcomes that matter in production. How quickly does the platform surface anomalies that engineering would actually page on? How often does it provide a plausible root cause chain that matches what the team eventually finds? How well does it handle non-deterministic behavior where “normal” shifts by tenant, by prompt class, or by tool usage?
This is the power of OpenTelemetry and OTLP compatibility: it lets you send your telemetry data any place you like so that you can choose the best platform for your team based on reliability results.
Conclusion
Traces are the most effective way to understand the real complexity of LLM interactions because they capture end-to-end execution with the right granularity. OTLP and OpenTelemetry-compatible SDKs protect teams from vendor lock-in and make side-by-side platform evaluation realistic.
InsightFinder aims to go beyond a thin GenAI layer by delivering an end-to-end reliability platform grounded in Composite AI techniques, patented approaches to anomaly detection and root cause analysis. Engineering teams do not need another tool that only tells them what happened. They need a platform that helps them keep complex systems reliable, including the probabilistic parts.
To see how this works with your existing instrumentation, run OTLP traces to InsightFinder side-by-side with your current backend. Sign up for free, so that you can compare reliability outcomes.
