OpenTelemetry-compatible LLM observability tools that work with existing tracing pipelines

Most teams shipping LLM apps and agents already have tracing in place. What they don’t have is reliable visibility into how those models behave—hallucinations, prompt injection, PII leaks, brittle tool calls—without standing up a separate, AI-only observability stack that ignores existing OpenTelemetry pipelines.

Quick Answer: Galileo is an OpenTelemetry-compatible LLM observability and reliability platform that plugs into your existing tracing pipelines, adds AI-specific evaluation and guardrails, and lets you go from evals to always-on production protection without new glue code.

The Quick Overview

• What It Is: An OpenTelemetry-compatible AI reliability platform that turns your existing traces into rich LLM observability, evaluation, and real-time guardrails for RAG systems, agents, and LLM apps.
• Who It Is For: Engineering and ML teams already instrumenting their systems with OpenTelemetry (or similar tracing) who need deep LLM observability and GEO-ready evaluation without rebuilding their telemetry stack.
• Core Problem Solved: You can’t see or stop LLM failures inside your current tracing tools. Galileo adds AI-native signals, evaluators, and guardrails directly on top of your existing pipelines so you’re not flying blind in production.

How It Works

Galileo doesn’t replace your tracing pipeline—it rides on it. You keep your existing OpenTelemetry setup and tracing vendors; Galileo adds an AI-specific lens on top so sessions → traces → spans become debuggable LLM behavior, guardrail metrics, and production controls.

Here’s how the flow works end to end:

1. Ingest & Instrument (Connect to OpenTelemetry):

   • Your services, agents, and RAG pipelines are already emitting spans via OpenTelemetry.
   • You add Galileo-compatible attributes to LLM spans (prompt, completion, tool calls, latency, cost, user/session IDs).
   • Logs/metrics/traces stream into your existing backend, and Galileo’s collector (or SDK) mirrors AI-relevant spans into the Galileo platform—no need to duplicate all infra telemetry.

2. Evaluate & Analyze (Build a Living Eval System):

   • Galileo aggregates LLM events into a single, filterable view across sessions → traces → spans, so you can see how agents made decisions, which tools they called, and where they failed.
   • The Evaluation Engine runs 20+ out-of-the-box evaluators for RAG quality, agent correctness, safety, and security, alongside your own custom evaluators.
   • Evaluators can be powered by LLM-as-judge or by Galileo’s fine-tuned Luna / Luna-2 small language models (SLMs), which are optimized to run with sub-200ms latency and ~97% lower cost than heavyweight GPT-style judges at production scale.

3. Guardrail & Govern (Turn Evals into Production Controls):

   • Evaluation results become guardrail metrics that apply to 100% of your traffic—not just sampled logs—thanks to low-latency Luna-2 models.
   • Galileo Protect sits inline with your LLM/agent responses, intercepts inputs/outputs, scores them against guardrail metrics, and triggers actions: block, redact, override, or webhook to your existing systems.
   • Signals continuously scans all traces to discover unknown failure patterns, then can auto-generate a new LLM judge or evaluator from a detected signal—turning incidents into reusable protections.

Features & Benefits Breakdown

Core Feature	What It Does	Primary Benefit
OpenTelemetry-Compatible Ingestion	Consumes LLM spans and metadata from your existing OpenTelemetry pipeline and augments them with AI-specific structure.	Reuse your current tracing stack; no need to rebuild telemetry for LLM observability.
Sessions → Traces → Spans View	Aggregates all AI-related spans into one view, with filters for model, tool, latency, cost, user, and error metrics.	Gives you total visibility into agent behavior and root cause across complex, multi-step workflows.
Evaluation Engine with Luna-2	Runs 20+ out-of-the-box and custom evaluators via fine-tuned SLMs, not heavyweight LLMs, at sub-200ms latency.	Enables always-on, low-cost, high-coverage evaluation and guardrails at production scale (100% traffic, ~97% lower cost).
Signals (Unknown Failure Detection)	Analyzes full production traces to surface hidden failure patterns you didn’t explicitly instrument for.	Detects “unknown unknowns” like new jailbreak patterns, subtle tool misuse, or drift before users complain.
Protect (Real-Time Guardrails)	Intercepts requests/responses, scores them on guardrail metrics, and executes policies (block/redact/override/webhook).	Stops hallucinations, prompt injection, PII leaks, and policy violations in real time—before they hit users or tools.
Custom Evaluators & CLHF Improvement	Lets you define domain-specific success criteria, then refine evaluators using SME annotations and few-shot examples from live feedback.	Avoids generic, miscalibrated judges; aligns AI behavior with your specific domain and business rules.
Enterprise-Ready Deployment	Offers SaaS, VPC, or on-prem deployment with SOC 2 Type II and HIPAA-compliant infrastructure (with BAAs).	Meets enterprise security, compliance, and data residency requirements without homegrown solutions.

Ideal Use Cases

• Best for teams with existing tracing pipelines: Because it plugs into OpenTelemetry and similar standards, Galileo layers AI observability and guardrails onto your existing sessions/traces/spans instead of forcing you to adopt a separate, siloed monitoring stack.

• Best for production RAG and agents under strict SLAs: Because Luna-2 enables sub-200ms guardrailing across 100% of traffic with ~97% lower cost than GPT-style judges, you can run 10–20 guardrail metrics on every request without blowing your latency or budget.

• Best for organizations with sensitive data and strict policies: Because Protect can block, redact, or override responses and Signals can surface PII leaks or policy drift across all traces, you can enforce compliance rules in real time.

• Best for teams fighting “unknown unknowns”: Because Signals proactively analyzes your full dataset, you can catch emergent jailbreaks, tool misuse, or degradation patterns without waiting for a flood of tickets.

Limitations & Considerations

• Requires at least basic telemetry instrumentation:
  If you have zero tracing today, you’ll need to instrument your services with OpenTelemetry (or similar) and mark LLM spans. Galileo provides guidance and SDKs, but you still need to emit traces and attach LLM metadata (prompts, completions, tool calls).

• Not a generic log-search product:
  Galileo is built around evaluation and guardrails, not “chat with your logs.” If your primary need is ad-hoc log search/BI dashboards without AI-specific metrics, a traditional APM/log tool might be more familiar—but it won’t give you eval-to-guardrail capabilities.

• Guardrails still need thoughtful design:
  Galileo gives you guardrail primitives and low-latency evaluators, but you define what “good” looks like. You’ll want SMEs involved to define domain-specific evaluators and policies, especially around safety, compliance, and tool access.

Pricing & Plans

Specific pricing depends on volume (traces per month), deployment model, and which Galileo modules you need (Evaluate, Signals, Protect, Luna-2 inference). Typical structures prioritize 100% traffic coverage without GPT-level evaluator spend.

• Evaluate & Signals Plan: Best for teams needing deep LLM observability, evaluation, and failure-mode detection across dev and production, while keeping guardrails enforced via their own apps or existing systems.

• Full Stack (Evaluate + Signals + Protect + Luna-2) Plan: Best for organizations that want end-to-end AI reliability—evals, unknown failure detection, and real-time guardrails—served via sub-200ms SLMs at scale.

To get an exact plan aligned to your traffic, deployment model (SaaS/VPC/on-prem), and regulatory needs, talk directly with Galileo’s team.

Frequently Asked Questions

How does Galileo integrate with OpenTelemetry without replacing my existing tracing stack?

Short Answer: You keep your current OpenTelemetry setup; Galileo consumes AI-relevant spans and adds evaluation and guardrails on top.

Details:
If you already emit traces via OpenTelemetry, you add LLM-specific attributes (prompt, completion, model name, tool calls, costs, latency) on spans that represent LLM or agent steps. Your existing tracing backend (e.g., Grafana, Datadog, New Relic, custom OTLP receiver) continues to ingest everything.

Galileo’s ingestion layer—compatible with OpenTelemetry formats—subscribes to AI-relevant logs/streams or is pointed at your trace pipeline to ingest those spans. It then reconstructs sessions → traces → spans specifically for LLM behavior, runs evaluators via the Evaluation Engine, and powers Signals and Protect. You don’t rip out existing infra or APM; you add Galileo as an AI-specific reliability layer.

Why not just use an LLM-as-judge on top of my traces?

Short Answer: Heavyweight LLM judges are too slow and too expensive to run on 100% of your production traffic; Galileo distills evaluators into small Luna-2 models so you can run them continuously.

Details:
Running GPT-4 (or similar) as a judge over every LLM span in your traces looks fine in a demo but falls apart in production:

• Latency: Multi-second evaluations will blow your SLA if you try to gate responses or tool calls in real time.
• Cost: Evaluating 100% of traffic with GPT-style models is cost-prohibitive, so teams end up sampling 5–10%—which means you miss most failures.
• Inconsistency: Generic judges often don’t match your domain, and their scoring can drift without structured calibration.

Galileo’s approach:

• Use LLM-as-judge while designing evaluators and during early calibration.
• Distill those evaluators into compact Luna / Luna-2 SLMs, which run evaluations in sub-200ms with ~97% lower cost.
• Run 10–20 guardrail metrics on every request, across 100% of traffic.
• Improve evaluator fit over time via SME labels and CLHF, so your judges stay aligned with your domain.

The result: you get reliable, always-on evaluation and guardrails that are compatible with your existing tracing pipelines—and that actually fit in your latency and cost budgets.

Summary

If your LLM observability story is “we have traces in OpenTelemetry, and we grep for weird stuff,” you’re still flying blind. Traditional tracing tells you where something went wrong, but not why the model behaved that way or how to stop it next time.

Galileo plugs directly into your existing OpenTelemetry-compatible tracing pipelines, turns LLM spans into structured sessions → traces → spans with AI-specific metrics, and layers on:

• A full Evaluation Engine with out-of-the-box and custom evaluators,
• Luna-2 SLMs for sub-200ms, low-cost evaluation on 100% of traffic,
• Signals that detect unknown failure patterns across all traces,
• Protect guardrails that intercept, score, and act on every request and response.

You keep your current observability stack. Galileo adds the AI reliability layer that turns evals into guardrails and telemetry into governance.

Next Step

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