AI Observability by OpenObserve

Product Introduction

1. Definition: AI Observability by OpenObserve is a unified, OpenTelemetry-native observability platform specifically engineered for monitoring, tracing, and evaluating AI agents and Large Language Model (LLM) applications. It falls under the technical categories of AI/LLM observability, Application Performance Monitoring (APM), and distributed tracing.
2. Core Value Proposition: It exists to provide developers and AI engineers with granular, cost-attributed visibility into complex AI agent sessions. The platform solves the critical problem of AI observability silos by correlating LLM traces, tool calls, and model requests with traditional infrastructure logs, metrics, and traces in a single, cost-efficient platform priced per gigabyte instead of per span.

Main Features

1. Unified AI & Infrastructure Observability: OpenObserve ingests standard OpenTelemetry gen_ai spans and OpenInference conventions, placing AI telemetry (prompts, completions, tool calls) directly alongside logs, metrics, and traces from the underlying infrastructure (pods, databases, vector stores). This is powered by a correlation engine that allows federated SQL and PromQL queries across all data sources, eliminating context-switching between tools.
2. Agent Session Tracing & Debugging: The platform automatically constructs a visual Agent Graph for every request, mapping the full call tree across sub-agents, tools, and external services with health statuses based on error rates. The Session Debug view provides a chronological ribbon breakdown of each conversation turn, detailing token usage, latency, and cost per LLM call and tool invocation, enabling precise root-cause analysis.
3. Production Online Evaluations: Unlike offline batch evaluations, OpenObserve performs continuous, online LLM evaluation on live production traffic. Users can configure scorecards using LLM-as-a-judge (with their own model provider) or custom HTTP scorers to automatically assess spans, traces, or entire sessions for metrics like relevance, hallucination, or toxicity, with results visualized on a live Quality dashboard.
4. Flexible, Cost-Effective Deployment & Pricing: The platform offers four deployment models: Managed Cloud (SaaS), Self-Hosted (single Rust binary), Bring-Your-Own-Cloud, and Bring-Your-Own-Bucket. Its pricing model is based on gigabytes of data ingested and queried, not per span, seat, or unit. This prevents cost explosions from agentic applications that generate hundreds of spans per request and includes unlimited users.

Problems Solved

1. Pain Point: Lack of integrated visibility leads to high AI operational costs and debugging complexity. Engineers struggle to answer why an agent session was slow or expensive because LLM traces are isolated from backend and database telemetry, forcing manual correlation across multiple tools.
2. Target Audience: Primary personas include AI/ML Engineers building agentic applications, DevOps/SREs responsible for the performance and cost of production AI systems, and Engineering Leaders needing to manage AI spend and quality at scale.
3. Use Cases: Essential for debugging a costly or looping AI agent in production, continuously monitoring LLM output quality and safety in real-time, attributing cloud infrastructure costs to specific AI features or teams, and consolidating observability tooling to reduce vendor sprawl and cost.

Unique Advantages

1. Differentiation: Unlike specialized LLM observability tools (e.g., Langfuse) that create data silos, or traditional APM vendors (e.g., Datadog) that meter expensive per-span pricing, OpenObserve provides a unified, OpenTelemetry-first platform with predictable, usage-based (per-GB) pricing. It avoids vendor lock-in by supporting any deployment model.
2. Key Innovation: The core innovation is its radically efficient data engine built in Rust on columnar Parquet storage, which enables the cost-effective ingestion and querying of high-cardinality, high-volume AI telemetry. This technical foundation makes the unified observability and per-GB pricing model economically viable at scale.

Frequently Asked Questions (FAQ)

1. How does OpenObserve handle data privacy for sensitive AI prompts and completions? OpenObserve addresses data privacy through multiple deployment options that keep data within your security perimeter (self-hosted, bring-your-own-cloud). Additionally, it supports Vector Remap Language (VRL) pipelines to redact or mask sensitive fields in-flight before the data is stored, ensuring compliance.
2. Can I use OpenObserve with my existing OpenTelemetry instrumentation for AI frameworks? Yes, OpenObserve is fully compatible with standard OpenTelemetry gen_ai semantics. If you are already instrumenting frameworks like LangChain, LlamaIndex, or the OpenAI SDK with OTel, you can simply redirect your OTLP exporter to OpenObserve without code changes to gain immediate AI observability.
3. What is the difference between online evaluations and traditional LLM evaluation? Traditional LLM evaluation is typically an offline, batch process run on static datasets. OpenObserve's online evaluations score live production traffic as it happens, enabling continuous monitoring of model quality, immediate detection of regressions, and the ability to route scored traces directly into human review queues for dataset creation.
4. How does the pricing per GB compare to per-span pricing for agentic workloads? Agentic workflows can fan out a single user request into hundreds of LLM calls and tool spans. With per-span pricing, this causes unpredictable cost spikes. OpenObserve's per-GB pricing compresses this activity into a predictable storage metric, often resulting in significantly lower costs for complex AI applications, as the volume of spans does not directly dictate cost.
5. Does OpenObserve support evaluating custom, non-LLM scoring logic for AI agents? Absolutely. While it provides built-in LLM-as-judge scorers, OpenObserve's evaluation system fully supports remote HTTP scorers. You can deploy your own microservice containing custom evaluation logic (e.g., business rule validation, code quality checks) and configure OpenObserve to call it for scoring live traces, integrating proprietary quality metrics directly into the observability workflow.