Comprehensive LLM Observability Platform
End-to-end LLM observability with tracing, evaluation, and autofix. Monitor performance, costs, and quality in production with 73+ evaluation scorers.
What is LLM Observability?
LLM Observability provides deep visibility into your AI applications—understanding not just what happened, but why and how to improve.
Definition
LLM Observability is the practice of monitoring, tracing, and analyzing Large Language Model applications to ensure quality, performance, and cost-effectiveness in production.
Why It Matters
Raw observability data is useless without evaluation. Agents generate 1000+ traces daily - impossible to review manually. You need AI-powered eval to pinpoint exact errors with reasoning.
AI-Specific Metrics
Track metrics that matter for AI: token usage, model costs, response quality, semantic accuracy, hallucination rates, and latency patterns.
Beyond Traditional APM
Traditional monitoring tracks requests. LLM observability tracks meaning, quality, and the reasoning behind every AI decision.
Traditional APM vs LLM Observability
| Metric | Traditional APM | LLM Observability |
|---|---|---|
| Token Usage Tracking | ||
| Cost Attribution | ||
| Response Quality Scoring | ||
| Latency Monitoring | ||
| Error & Failure Analysis | AI Evals |
Purpose-Built for the AI Era
Our three-pillar approach provides complete observability for any LLM application—from simple chatbots to complex multi-agent systems.
Tracing
Capture every LLM call, RAG retrieval, and agent decision with hierarchical traces that show the complete picture.
- Full request/response capture
- Hierarchical span visualization
- Token and cost attribution
Evaluation
Automatically score every AI interaction with 73+ evaluation metrics including accuracy, safety, and custom business rules.
- 73+ built-in scorers
- Custom evaluation rules
- Real-time quality monitoring
AutoFix
NovaPilot analyzes your data and automatically generates fix recommendations—prompt improvements, model changes, and optimizations.
- AI-powered recommendations
- Prompt optimization suggestions
- Model upgrade analysis
Project-Centric Architecture
Organize your AI applications with a clear hierarchy that scales from development to production.
Project Organization
Group related AI workloads under projects with isolated metrics and access controls.
Environment Management
Separate dev, staging, and production with environment-aware dashboards and alerts.
Real-time Processing
Sub-second trace ingestion with immediate availability for monitoring and analysis.
Everything You Need for LLM Observability
From tracing to evaluation to automated fixes, Noveum provides the complete toolkit for AI operations.
Complete Tracing
Capture every interaction in your LLM pipeline with hierarchical traces that reveal the full story of each request.
- LLM calls with full prompts and responses
- RAG pipeline steps with retrieved documents
- Agent workflows and tool executions
- Custom spans for any operation
Dashboard Analysis
Real-time dashboards with AI-specific metrics that give you instant insights into your production LLM applications.
- Request volume and latency trends
- Model distribution and usage patterns
- Error rates and failure analysis
- Custom metric visualization
Cost Attribution
Understand exactly where your LLM budget goes with granular cost tracking across projects, models, and requests.
- Per-request cost calculation
- Model-level cost comparison
- Project and team cost allocation
- Budget alerts and forecasting
Quality Scoring with NovaEval
Automatically evaluate every LLM response with our comprehensive scorer library—no manual review required.
- 73+ evaluation scorers
- Accuracy and relevance metrics
- Safety and bias detection
- Custom business rule evaluation
AutoFix with NovaPilot
Our AI engineer analyzes your data and automatically generates actionable fix recommendations.
- Prompt optimization suggestions
- Model upgrade recommendations
- Configuration improvements
- Automated fix validation
Works With Your AI Stack
Native integrations with all major frameworks—plus OpenTelemetry for custom implementations.
LangChain
First-class LangChain integration with automatic chain and agent tracing.
LlamaIndex
Complete LlamaIndex support for RAG pipelines and document processing.
CrewAI
Multi-agent workflow monitoring with crew and task-level visibility.
AutoGen
Microsoft AutoGen integration for conversational agent systems.
LangGraph
Graph-based agent workflows with state and transition tracking.
OpenTelemetry
Standard OTEL support for any custom AI implementation.
All Major AI Providers Supported
Automatic cost and token tracking for every provider.
Built for Enterprise Scale
Security, compliance, and collaboration features that meet enterprise requirements.
Multi-Tenant Architecture
Complete data isolation between organizations with granular access controls.
- Organization-level isolation
- Role-based access control
Multi-Region Deployment
Deploy in any region to meet data residency requirements and reduce latency.
- Global edge ingestion
- Regional data storage
Advanced Security
Enterprise-grade security with encryption, SSO, and audit capabilities.
- End-to-end encryption
- SSO and SAML support
Compliance & Audit
Comprehensive audit logs and compliance features for regulated industries.
- Complete audit trails
- Data retention policies
Team Collaboration
Built-in collaboration features for AI teams of any size.
- Shared dashboards
- Annotation and commenting
Custom Metrics
Define and track business-specific metrics tailored to your use case.
- Custom scorer creation
- Business KPI tracking
Get LLM Observability in Minutes
Add comprehensive observability to your LLM applications with just a few lines of code.
$ pip install noveum-traceimport os
import noveum_trace
from noveum_trace import NoveumTraceCallbackHandler
# Initialize Noveum Trace
noveum_trace.init(
api_key=os.getenv("NOVEUM_API_KEY"),
project="my-llm-app",
environment="development"
)
# Create callback handler
callback_handler = NoveumTraceCallbackHandler()from langchain_openai import ChatOpenAI
# Add callback to your LLM
llm = ChatOpenAI(
model="gpt-4",
callbacks=[callback_handler]
)
# All LLM calls are now automatically traced!
response = llm.invoke("What is the capital of France?")Frequently Asked Questions
Everything you need to know about LLM Observability with Noveum.ai.
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