Observability

Highflame’s observability layer is designed to give teams a clear, end-to-end understanding of how AI traffic behaves in production. As AI systems grow more complex, spanning agents, tools, models, and guardrails, it becomes increasingly difficult to understand why a request behaved a certain way, where latency was introduced, or why a security decision was made. Highflame observability is built to answer those questions directly.

Rather than treating AI requests as opaque black boxes, Highflame captures rich execution data for every interaction. This allows developers, security teams, and platform owners to reason about performance, cost, and security decisions using the same shared source of truth.

Traces

Highflame observability is built around tracing. A trace represents the complete lifecycle of a single request as it moves through your application, the Highflame Gateway, configured guardrails, and downstream model providers. Every call your application makes generates a trace, allowing you to reconstruct exactly what happened, in what order, and why.

Inspecting Traces

By inspecting a trace, developers can understand where time was spent and how decisions were made at each stage of execution. Latency breakdowns show how long a request spends inside your application, within the Highflame platform, and with the underlying model provider, making it easier to diagnose performance regressions or unexpected slowdowns. Token metrics provide a precise view of token usage across prompts and responses, helping teams anticipate costs and identify inefficient interactions.

Traces also serve as a detailed security audit. When a request is modified, blocked, or flagged by a guardrail, the trace records which guardrail fired, what it detected, and what action was taken. This makes it possible to quickly understand why enforcement occurred without guesswork or log spelunking. In cases of unexpected behavior, traces allow teams to inspect request and response payloads at each stage, providing the context needed to reproduce and resolve issues.

Trace Metadata

Rich metadata attached to traces enables powerful filtering and search. Teams can slice traffic by application, route, agent, environment, or custom tags to investigate incidents, identify trends, or analyze usage patterns over time. With this level of visibility, issues that once took hours to diagnose can often be resolved in minutes.

Highflame observability is built on tracing. A trace is the lifecycle of a single request as it moves through the platform. For every call your application makes, Highflame creates a trace so you can see step by step what happened, when, and why. If you explore a trace further, you can find lots of useful information, like:

• Latency: Spot performance issues by understanding how long a request takes in your application, the Highflame Gateway, and the LLM provider.

• Token Metrics: Anticipate and manage costs by getting an accurate account of token usage.

• Guardrail Usage: Diagnose why a request was modified or blocked by referencing its detailed security audit.

• Payloads: Identify the data that led to unexpected behavior by examining request and response payloads at any stage of the process.

• Metadata: Filter, search, and gather data to investigate incidents and study trends by taking advantage of context-rich tags.

With this level of detail at your fingertips, your developers can resolve issues in minutes rather than hours.

Analytics and Dashboards

While traces provide deep insight into individual requests, Highflame’s analytics dashboards offer a high-level view of system-wide behavior and security posture. These dashboards aggregate trace data across time, applications, and environments, enabling teams to understand how their AI systems perform as a whole.

Dashboards can be filtered by time range, application, route, or other metadata, making it easy to zoom in on specific workloads or incidents. Highflame also provides prebuilt dashboards that surface key metrics, including usage and cost trends, performance characteristics, and the frequency and types of detected threats. This enables proactive monitoring of AI security health, rather than reacting only after an incident occurs.

Model Playground

Observability isn’t limited to production traffic—it also includes the ability to test and experiment safely. Highflame’s Model Playground provides an interactive environment where developers can send requests through their configured routes and guardrails and immediately observe the results.

The Playground makes it easy to understand how changes to prompts, policies, or guardrail configurations affect behavior before deploying them. By exposing the same tracing and enforcement logic used in production, it allows teams to experiment with confidence and iterate quickly without introducing risk.

OpenTelemetry Integration

Highflame's tracing features comply with OpenTelemetry, the industry standard for observability. This integration lets you connect AI performance and security data from all your agents, giving your teams a single, clear view. You can also easily export your data from Highflame into various enterprise observability tools.