Noveum AI vs Langfuse: which LLM observability platform fits your team?
An honest, detailed comparison of the leading observability tools for AI engineers focused on evaluation, monitoring, and automated remediation.
Best for automated eval at scale and for teams that want to know not just what broke, but exactly how to fix it
Best for open-source flexibility and for teams that want full control over their own observability stack
We recently switched from Langfuse to Noveum AI for our DarGlobal team, and the experience has been very positive.
The Noveum team helped us integrate directly into our existing codebase, which made onboarding much smoother.
Compared to Langfuse, maintenance and deployment overhead dropped significantly, saving engineering effort and operational cost.
The biggest workflow improvement was AI-assisted debugging.
We used to manually dig through logs, copy traces into Claude Code, and iterate from there.
With Noveum, we analyze spans and traces inside the platform and identify issues in place, which has saved a lot of debugging time.
AI-based scorer recommendations also removed the guesswork of which scorers to build and configure.
When we needed CrewAI, the team implemented it quickly and helped us integrate it properly.
Shivam and the broader team have been responsive throughout, proactive on our feedback, and helpful in suggesting code changes when needed.
Overall, the platform has streamlined our AI observability and debugging while reducing operational overhead.”
Rehan Hussain Imam
Senior AI consultant, DarGlobal
Which tool is right for you?
Langfuse gives you the data. Noveum gives you the answer.
Langfuse excels when engineers want open-source building blocks and full hosting choice. Every trace, every score, every prompt version is visible and yours to act on. Noveum is built for teams that want the entire eval loop to run on its own. You connect your stack, it scores everything automatically, and when something breaks, NovaPilot identifies the issue and suggests fixes instantly without needing engineers to constantly monitor it.
The details that actually matter
Judges built in. You stay out of the loop..
Noveum ships with 100+ built-in scorers covering hallucination, faithfulness, RAG quality, safety, and more. LLM-based evaluation is built in where it matters, so you get judge-quality scores without setting up any infrastructure. With Langfuse, you get building blocks to assemble your own LLM-as-judge pipeline. That means writing prompts, picking models, and maintaining the system yourself before a single trace gets scored.
Evals that don't wait for your annotation team.
Noveum runs evals directly on your production traces with no expected answers needed. You get real signal from real data from day one. With Langfuse, your PM has to drive human annotations and define expected answers for every trace before meaningful evals can run. That is three sprints of labeling work before you see a single useful score.
Evals that get fixed, not just flagged.
Noveum surfaces not just what broke but exactly why, then hands you a NovaPilot recommendation report with actionable fixes for your prompts, tool calling, and pipelines. Other observability tools stop at flagging failures. You get a verified recommendation, not a list of failures to stare at.
We've used Noveum during the early stages of our retrieval pipeline at Wealthink, and what stood out most was how proactively the team helped us evaluate output quality.
They ran a custom eval on our setup and surfaced inaccuracies in our retrieval layer that we were later able to independently validate.
That genuinely helped us diagnose issues faster.
The founders themselves regularly jump on calls with our team to debug problems and discuss what should be built next.
That level of involvement isn't easy at this stage, and you can feel it reflected in the product.
The tracing capabilities and overall UI have improved noticeably over the months we've been using it.
If you're integrating AI into your tech stack and care about catching failures before your users do, it's definitely worth checking out.”
Umang Joshi
Founder, Wealthink
Pricing at a glance
Observation-based pricing
More eval power, less spend
With Noveum you pay for evals and remediations, not raw observation volume. Smart trace sampling puts you in control of what gets scored, so your bill stays predictable as traffic grows. 100+ built-in scorers plus enterprise custom scorers give you more eval depth without building pipelines from scratch. NovaPilot recommendation reports close the loop on agent failures, and teams using Noveum typically reclaim around a third of their engineering bandwidth on eval and remediation work. For any company building agents in production, that is a straightforward return.
Common questions
Our take
For teams shipping AI agents to real users, Noveum is the stronger choice. You get 100+ built-in scorers, root cause analysis, trace sampling you control, enterprise custom scorers, and NovaPilot recommendation reports all from day one. No setup loop. No maintaining pipelines. For growth-stage and enterprise companies, that is a no-brainer. Langfuse is a solid open-source platform for developers who want to self-host and build their own eval stack. The MIT license and active community are real strengths. But if you need a complete production-grade eval and autofix loop without the engineering overhead, Noveum is built for that.
Explore more comparisons
About Noveum.ai — Your 24/7 AI Eval Engineering Team
Noveum.ai is the only platform that doesn't just tell you what's broken — it tells you how to fix it. As your 24/7 AI Eval Engineering Team, Noveum monitors, evaluates, debugs, and sends fixes as PR to you, at scale. Built by ex-AWS SageMaker, ex-Sambanova, and ex-Playo engineers, Noveum is purpose-built for enterprise AI teams running chatbots, voice bots, and autonomous agents in production.
With a lightweight SDK integration (15 minutes or less), Noveum captures everything your AI Agent does — prompts, decisions, tool calls, token usage, context retrievals, and workflow traces. Processing over 6M traces and 60M spans per day, the platform provides production-grade observability at enterprise scale.
The platform includes 100+ specialized AI scorers across 18 categories, including dedicated Audio/TTS Quality and Voice Pipeline Latency scorers — making Noveum the only platform with built-in audio evaluation capabilities. From hallucination detection to mispronunciation scoring, from RAG quality to speaking-over-user detection, every dimension of AI quality is covered.
NovaPilot, Noveum's autonomous AI agent, analyzes evaluation data, identifies failure patterns, tests 136+ prompt variations across 4 evolutionary generations, and delivers verified fixes — all automatically. NovaPilot's 4 specialized agents (Prompt Analyzer, Tool Analyzer, Flow Analyzer, General Analyzer) provide comprehensive coverage of every failure mode. Teams can also create complex custom evaluations based on their Product Requirements.
Noveum supports on-prem deployment with BYO ClickHouse, offers Enterprise SLAs with SOC 2 Type II, HIPAA, and GDPR compliance. Backed by AWS, GCP, DigitalOcean, OVHCloud, Nvidia Inception Program, ElevenLabs, and Anthropic.
Why AI Reliability Matters for Modern Teams
AI Agents are becoming the backbone of digital operations — answering customer questions, triaging support tickets, qualifying leads, routing tasks, summarizing documents, analyzing data, and automating workflows. But unlike traditional software, AI is inherently probabilistic. Even well-designed agents will:
- hallucinate facts
- misunderstand instructions
- misuse tools
- return irrelevant answers
- generate inconsistent formats
- exceed token budgets
- produce partial responses
- violate compliance rules
- degrade over time without monitoring
Unreliable AI leads to increased support burden, lost revenue, compliance risks, poor customer experience, and rising operational costs. Noveum.ai provides the infrastructure needed to measure, monitor, and maintain AI Agent quality at scale.
Noveum Monitoring — Complete AI Observability
Noveum captures all agent behaviors in real time:
- Full prompt & response history
- Tool call inputs and outputs
- Token usage tracking
- Cost attribution per action
- System and user message flows
- Context retrieval logs
- Branching workflow behavior
- Intermediate reasoning
- Agent chain-of-thought traces
- Latency analysis
- Failure modes and exceptions
The Agent Visualizer produces a pre-order execution graph showing how the AI Agent reasons and acts across steps. This visual monitoring layer enables developers, product managers, and operations teams to understand the internal logic of their AI Agents and diagnose complex failure patterns.
Noveum Evaluation — Scoring AI Performance with 100+ Specialized Scorers
Noveum.ai includes the most comprehensive evaluation library available for enterprise AI Agents. Every scorer has been designed to evaluate a different dimension of AI output quality — from hallucination detection and factual grounding to conversation health, safety compliance, RAG retrieval quality, structured formatting, and system performance.
Each evaluation produces:
- numeric score
- natural-language reasoning
- step-by-step justification
- references to context used
- recommendations for improvement
Full Scorer Library With Explanations (100+ Scorers)
1. Accuracy, Matching & Semantic Quality Scorers
- ExactMatchScorer
Checks if the AI output perfectly matches the expected answer. Ideal for classification or deterministic tasks. - AccuracyScorer
Measures the proportion of correct responses across a dataset — essential for baseline LLM quality benchmarking. - MultiPatternAccuracyScorer
Supports multiple correct output forms. Useful for flexible workflows where several answers are acceptable. - F1Scorer
Balances precision and recall to measure how accurately the AI captures all important elements. - BLEUScorer
An n-gram matching metric for evaluating text similarity. Used broadly in summarization and generation tasks. - ROUGEScorer
Measures recall overlap with reference text, especially useful for long-form content evaluation. - LevenshteinSimilarityScorer
Calculates edit distance between expected and generated text, identifying near-matches efficiently. - SemanticSimilarityScorer
Uses embedding-based comparisons to assess meaning similarity, not just surface text similarity. - InformationDensityScorer
Scores how substantive and information-rich an answer is, identifying fluff or low-density output. - ClarityAndCoherenceScorer
Evaluates readability, logical structure, and flow — vital for customer-facing AI responses. - AnswerCompletenessScorer
Checks whether the AI addressed every part of the prompt completely. - QuestionAnswerAlignmentScorer
Ensures direct alignment between user question and generated answer. - TechnicalAccuracyScorer
Validates correctness for domain-specific answers in finance, engineering, legal, or medical tasks. - TerminologyConsistencyScorer
Ensures specialist terminology is used appropriately and consistently in AI responses.
2. Relevance, Faithfulness & Hallucination Scorers
- AnswerRelevancyScorer
Measures how relevant the AI's response is to user intent. - FaithfulnessScorer
Checks whether the AI stays grounded in provided context without adding unsupported claims. - FactualAccuracyScorer
Identifies factual inaccuracies by comparing output with known truths. - HallucinationDetectionScorer
Detects fabricated or invented information. One of the most important scorers for enterprise safety. - ConflictResolutionScorer
Identifies contradictions in the AI's reasoning. - ContextPrioritizationScorer
Ensures the AI selects the most important context pieces when generating a response. - ContextFaithfulnessScorerPP
Validates that the answer is grounded in provided documents. - ContextGroundednessScorer
Checks if every important claim is supported by context. - ContextCompletenessScorer
Measures whether the AI uses enough context to form a complete answer. - ContextConsistencyScorer
Ensures consistency with reference material across statements.
3. RAG (Retrieval-Augmented Generation) Scorers
- ContextualPrecisionScorer
Evaluates relevance of retrieved context snippets used in the final answer. - ContextualRecallScorer
Measures how much relevant content the AI included from the available context. - RAGASScorer
Industry-standard RAG quality scoring across groundedness, relevance, and answer quality. - ContextualPrecisionScorerPP
Enhanced precision evaluation for more complex retrieval chains. - ContextualRecallScorerPP
Advanced recall scoring that checks deeper semantic relevance. - RetrievalF1Scorer
Harmonic mean of precision and recall for retrieval effectiveness. - RetrievalRankingScorer
Evaluates document ranking quality for RAG pipelines. - RetrievalDiversityScorer
Ensures retrieval results include diverse, non-redundant knowledge sources. - AggregateRAGScorer
Provides a combined score summarizing RAG performance. - RAGAnswerQualityScorer
Evaluates whether the final answer is high-quality and well-grounded.
4. Conversation & Role Behavior Scorers
- ConversationRelevancyScorer
Checks turn-by-turn relevance in multi-turn dialogues. - ConversationCompletenessScorer
Ensures the entire user intent has been addressed in the conversation. - RoleAdherenceScorer
Ensures the agent sticks to its assigned role and persona. - ConversationalMetricsScorer
Composite conversational quality scorer measuring flow, engagement, and relevance.
5. Safety, Bias & Harm Prevention Scorers
- AnswerRefusalScorer
Detects whether AI refuses unsafe or disallowed tasks appropriately. - ContentModerationScorer
Flags potentially harmful, explicit, or policy-violating content. - ContentSafetyViolationScorer
Scores violations of safety, compliance, or governance guidelines. - ToxicityScorer
Evaluates presence of insulting, aggressive, or harmful language. - IsHarmfulAdviceScorer
Detects dangerous recommendations — crucial for sensitive domains. - NoGenderBiasScorer
Ensures fair, unbiased responses related to gender. - NoRacialBiasScorer
Evaluates outputs for racial bias or stereotyping. - NoAgeBiasScorer
Checks for age-related prejudice in AI responses. - CulturalSensitivityScorer
Ensures the AI respects cultural norms and avoids offensive phrasing.
6. Structured Output & Format Scorers
- IsJSONScorer
Validates whether responses follow correct JSON formatting. - IsEmailScorer
Checks for valid email generation structure. - ValidLinksScorer
Ensures URLs in the output are properly formatted and valid.
7. Multi-Judge & Deep Reasoning Scorers
- PanelOfJudgesScorer
Uses multiple LLM 'judges' to provide averaged evaluations for robust scoring. - SpecializedPanelScorer
Domain-specific multi-judge scorer for technical or expert evaluations. - GEvalScorer
A generalized evaluation approach measuring reasoning, structure, and coherence. - AgentScorers
Bundle of agent-specific evaluations optimized for multi-step AI workflows.
8. Pipeline & System-Level Scorers
- QueryProcessingEvaluator
Evaluates how accurately the system interprets and processes user queries. - RetrievalStageEvaluator
Scores the retrieval portion of a RAG pipeline. - RerankingEvaluator
Evaluates how well documents are reranked for relevance. - RAGPipelineEvaluator
Full end-to-end scoring for retrieval + ranking + generation. - PipelineCoordinationScorer
Evaluates coordination of pipeline components across stages.
9. Latency, Resource & Error Scorers
- LatencyAnalysisScorer
Measures latency and identifies bottlenecks across the AI execution path. - ResourceUtilizationScorer
Assesses token efficiency, compute usage, and cost impact. - ErrorPropagationScorer
Detects how errors propagate across multi-step agent workflows.
10. Knowledge & Memory Scorers
- KnowledgeRetentionScorer
Measures whether an AI preserves key information across turns or steps.
Noveum's Auto-Fixing Engine
After evaluating agents using all scorers above, Noveum automatically analyzes failures and runs improvement experiments such as:
- prompt optimization
- model selection for better cost-performance
- parameter tuning
- workflow adjustments
- tool-call corrections
The system then provides a full recommendation report or can automatically generate pull requests to update your agent logic.
AI Cost Optimization With Noveum
Noveum identifies token-heavy prompts, inefficient retrieval patterns, expensive models, and redundant workflow steps. It analyzes cost per generation step and suggests cheaper, higher-performing alternatives.
Companies typically see 20-50% reduction in AI operational cost after implementing Noveum's recommendations.
Noveum.ai — Your 24/7 AI Eval Engineering Team
As companies scale AI Agents to hundreds and thousands, Noveum provides the only platform that monitors, evaluates, and autonomously fixes broken agents at scale. Built for enterprise from day 1, with on-prem deployment, BYO ClickHouse, and SOC 2 Type II compliance. Trusted by agent builders, banks, and telecom providers.