LLM Observability

Control AI Application Costs and Performance

Start with Flow telemetry pipeline to collect, optimize, and route AI telemetry at scale, then gain full visibility into LLM performance, costs, and compliance with Apica Observe.

The Problem

AI Applications Generate 10-100x More Telemetry

LLM applications don’t just add features, they fundamentally change your telemetry volume and cost structure.

The Telemetry Challenge:

A single AI can generate more telemetry in an hour than an entire application stack generated in a day
Traditional observability platforms charge for every byte ingested, making AI workloads prohibitively expensive
Teams forced to choose between complete visibility and manageable costs

Key Stats:

10-100x – Telemetry volume increase from AI workloads
36% of enterprises spend $1M+ annually on observability (Gartner)
40% – Average cost reduction with Apica’s telemetry pipeline approach

Our Solution

Telemetry Pipeline First, Observability Second

The Apica approach to LLM observability begins with controlling telemetry before expensive platform ingestion.

The Architecture:

Step 1: Flow Telemetry Pipeline Collect, process, transform, and route AI telemetry intelligently:

Never Block, Never Drop – Zero data loss with InstaStore™ infinite reservoir
Intelligent Processing – Transform and enrich telemetry before expensive platform ingestion
Smart Routing – Send high-value data to expensive platforms, archive the rest cost-effectively
Open Standards

Step 2: Observe Platform Once telemetry is optimized, gain comprehensive AI observability:

Monitor token usage, request latency, and system performance in real-time
Track dependencies across LLMs, RAG pipelines, and orchestration layers
Enforce approved LLM usage, eliminate Shadow AI and detect proprietary data leakage
Ensure compliance with full audit trails

How It Works

Telemetry-First Architecture for AI Workloads

Observe: AI & LLM Monitoring
Flow: AI Telemetry Pipeline
Seamless Integration

Comprehensive observability across your entire AI stack—from inference APIs to vector databases to retrieval pipelines.

LLM Performance Tracking

Monitor inference latency, throughput, and availability for OpenAI, Anthropic, Bedrock, SageMaker, and custom models
Track prompt token count, completion tokens, and total tokens per request
Measure time-to-first-token and streaming performance for user-facing applications
Identify slow model calls impacting user experience

Cost Monitoring & Attribution

Real-time tracking of token usage and associated costs across all LLM providers
Cost attribution by feature, customer segment, or user cohort
Budget alerts when spending exceeds thresholds
Identify expensive prompts or inefficient retrieval patterns driving costs

Quality & Accuracy Monitoring

Capture prompts and responses for quality analysis
Track model output quality metrics and accuracy scores
Monitor for hallucinations, inconsistent responses, or degraded performance
A/B testing support for prompt engineering and model selection

Agentic Workflow Visibility

End-to-end distributed tracing across multi-step agent chains
Visualize LLM calls, tool usage, RAG retrievals, and external API interactions
Understand which steps in agentic workflows contribute to latency or cost
Debug failed agent executions with complete context

LLM-Specific Dashboards

Pre-built dashboards for token usage, cost analysis, latency tracking

Route, enrich, and optimize AI telemetry data across your observability and analytics platforms.

Intelligent Data Routing

Send LLM telemetry to specialized AI observability platforms, cost analytics tools, and compliance systems
Route sensitive prompt/response data to secure, compliant storage
Filter and sample high-volume AI telemetry to control costs

Enrichment for Context

Add user context, session IDs, and feature flags to AI telemetry
Enrich with business metadata (customer tier, use case, geography)
Correlate AI performance with user satisfaction and business outcomes

Compliance & Privacy

Redact personally identifiable information (PII) from prompts before storage
Filter sensitive data to ensure compliance with data residency requirements
Maintain audit trails for AI usage and outputs

Built for AI-Era Telemetry Volumes

Handle 10-100x data volumes from LLM applications
Native support for OpenTelemetry AI telemetry collection
Intelligent routing for token usage, inference metrics, and trace data

Store and analyze AI telemetry data with InstaStore™ for long-term trend analysis and compliance.

Complete Prompt/Response History

Infinite retention of prompts and responses for debugging and analysis
Instantly search months of historical AI interactions
Reproduce issues by replaying exact prompts and model configurations

Long-Term Cost & Performance Trends

Analyze AI spending patterns over weeks and months
Identify seasonal usage patterns and forecast future costs
Track model performance degradation or improvement over time

Compliance & Audit Support

Maintain complete audit trails of AI usage for regulatory compliance
Search historical outputs for bias detection and fairness analysis
Support investigations with instant access to any past AI interaction

Seamless Integration

Built for Your AI Stack

Apica integrates with the entire AI ecosystem:

LLM Providers: OpenAI, Anthropic, Cohere, Mistral, HuggingFace

Cloud AI Platforms: Azure OpenAI, Google AI Studio, Amazon Bedrock, Vertex AI

On-Premises & Open Source: Ollama, GPT4All, custom models

AI Frameworks: LangChain, LlamaIndex, Semantic Kernel, AutoGen

The Result

AI Applications You Can Trust

Cost Optimization

Predict and prevent cost spikes before they impact budgets
Analyze token usage patterns across models and applications
Right-size model selection based on actual performance data

Performance Monitoring

Trace complete request flows from user input to LLM response
Identify bottlenecks in multi-step AI workflows
Monitor inference latency, error rates, and throughput

Security & Compliance

Detect PII leakage and sensitive data exposure
Monitor for prompt injection and adversarial attacks
Maintain full audit trails for regulatory compliance
Track model behavior changes and bias indicators

AI Quality Assurance

Validate response relevance and accuracy
Track model performance degradation over time

Why Apica For AI & LLM Observability

Purpose-Built for AI Workloads

Native support for LLM-specific metrics, agentic workflows, and AI application patterns. We understand that AI observability requires more than traditional APM.

Complete Data Control

Your prompts and responses stay yours. Route AI telemetry to compliant storage, apply PII redaction at the pipeline, and maintain complete control over sensitive data.

Unified Platform

Monitor AI applications alongside traditional infrastructure and services in one platform. Correlate AI performance with user experience and business outcomes.

OpenTelemetry & Standards-Based

Built on open standards for maximum flexibility. No vendor lock-in for your AI observability stack.

Cost Optimization Built-In

Intelligent sampling, filtering, and routing ensure you capture critical AI telemetry without drowning in costs.

Get Started

See AI Observability in Action

Schedule a demo showing real-time LLM monitoring, cost attribution, and agentic workflow tracing.

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Try AI Monitoring Free

Start monitoring your AI applications today with full LLM observability capabilities.

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AI Observability Documentation

Technical guides for instrumenting LLM applications and setting up cost tracking.

View Documentation

Download AI Observability Guide

Best practices for monitoring GenAI applications in production.

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