Comprehensive LLM Observability Platform
Connect instrumented tracing, configured evaluation, and NovaPilot recommendations. Monitor captured performance, estimated costs, and quality with selected compatible checks from 112 published scorers.
Understanding LLM Observability
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
Trace volume can outgrow manual review. Configured evaluation narrows attention to selected items and scorer decisions, while source-trace links preserve the evidence needed for investigation.
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 and infrastructure. LLM observability adds instrumented model, retrieval, tool, token, cost, and evaluation context; it does not expose hidden model reasoning.
Traditional APM vs LLM Observability
The Noveum Approach
Purpose-Built for the AI Era
Our three-pillar approach connects tracing, configured evaluation, and NovaPilot reports for instrumented LLM applications.
- 1
Tracing
- 2
Evaluation
- 3
NovaPilot Analysis
Tracing
Capture instrumented LLM calls, RAG retrievals, tool operations, and agent steps as hierarchical traces.
- Request/response capture for instrumented operations
- Hierarchical span visualization
- Token and cost attribution
Evaluation
Evaluate selected AI interactions with published scorers for accuracy, safety, and custom business requirements.
- 112 published scorers
- Custom evaluation rules
- Configured evaluation results
NovaPilot Analysis
NovaPilot analyzes evaluation results for dataset items and produces reports with recommended next steps for your team to review.
- Evaluation-result summaries
- Recurring issue patterns
- Recommended next steps
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 development, staging, and production with environment-aware dashboards and filters.
Trace Ingestion
Ingest instrumented traces and inspect them in Explorer after they become available.
Platform Capabilities
Connected Tools for LLM Observability
From tracing and evaluation to NovaPilot analysis reports, Noveum provides an integrated toolkit for AI operations.
Hierarchical Tracing
Capture the interactions you instrument as hierarchical traces that connect each request's recorded operations.
- LLM calls with the prompt and response fields your capture settings retain
- RAG pipeline steps with retrieved documents
- Agent workflows and tool executions
- Custom spans for any operation
Framework Support
Works With Your AI Stack
Supported integrations include LangChain, LangGraph, CrewAI, LiveKit, and Pipecat, with custom operations available through the Python SDK.
LangChain
A callback handler traces the LangChain invocations to which you attach it, including supported chain and agent events.
LangGraph
Graph-based agent workflows with state and transition tracking.
CrewAI
Multi-agent workflow monitoring with crew and task-level visibility.
OpenTelemetry
Standard OTEL support for any custom AI implementation.
Provider Metadata Support
Record provider, model, token, and reported-cost metadata when the integration supplies it; estimates depend on recognized inputs.
Enterprise Features
Built for Enterprise Scale
Deployment, access-control, auditability, and collaboration options to review against enterprise requirements.
Multi-Tenant Architecture
Organization-scoped data access with granular controls; review the selected deployment's isolation boundaries during security assessment.
- Organization-level isolation
- Role-based access control
Multi-Region Deployment
Discuss region placement and data-residency requirements when scoping an enterprise deployment.
- Deployment-specific ingestion topology
- Contract-defined storage location
Advanced Security
Scope encryption, SSO, and audit capabilities for the selected plan and deployment.
- Encryption for supported data paths
- SSO and SAML options for eligible agreements
Compliance & Audit
Use available records and deployment controls as inputs to your organization's compliance program.
- Captured activity and trace records
- Contract- or deployment-specific retention requirements
Team Collaboration
Share organization and project access to captured traces and evaluation results.
- Organization and project access
- Shared evaluation evidence
Custom Metrics
Define and track business-specific metrics tailored to your use case.
- Custom scorer creation
- Business KPI tracking
Quick Start
Instrument a Representative Operation
Install and initialize the Python SDK, trace one operation, and verify the resulting spans before expanding coverage.
- 1
Install the SDK
- 2
Initialize the Client
- 3
Add Tracing to Your Code
1st trace
Initial Verification
Python
First-Party SDK
Measure
Workload-Specific Overhead
FAQ
Frequently Asked Questions
Everything you need to know about LLM Observability with Noveum.ai.
What is LLM Observability?
LLM Observability is the practice of monitoring, tracing, and analyzing Large Language Model applications in production. It goes beyond traditional APM by tracking AI-specific metrics like token usage, model costs, response quality, and semantic accuracy.
How does it differ from traditional APM?
Traditional APM tracks HTTP requests, errors, and system metrics. LLM observability adds hierarchical AI traces, model and token context, semantic evaluation with 112 published scorers, and NovaPilot analysis reports.
What is NovaEval?
NovaEval applies selected scorers to structured dataset items. Its 112 published scorers cover accuracy, relevance, coherence, safety, bias, and voice and audio evaluation. Separate rubric-based custom scorers can represent product requirements; compatible fields and execution mode determine what runs.
What is NovaPilot?
NovaPilot analyzes results for evaluated dataset items and produces reports with issues and recommended next steps. Engineers review the report, make any code, prompt, model, or configuration changes, and validate those changes separately.
Can I self-host Noveum.ai?
Self-hosted and in-VPC deployment options can be scoped for qualifying enterprise engagements. Architecture, managed dependencies, available features, data flows, and operational responsibilities depend on the agreement.
How long is data retained?
Retention depends on the deployment and applicable contract or privacy terms. The current application does not evidence plan-aware enforcement of the plan-specific day counts previously shown here. Confirm the policy for your workspace with Noveum.
What does Noveum.ai cost?
Noveum.ai offers a free tier for getting started. Pro plans feature usage-based pricing with volume discounts. Enterprise plans include custom pricing with dedicated support.
What support is available?
Documentation is available publicly. Additional support channels, coverage hours, response targets, implementation help, and named contacts depend on the active plan or enterprise agreement.
Start Your LLM Observability Journey
Connect tracing, configured evaluation, and NovaPilot analysis in one workspace.
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