6 Best LLM Monitoring Solutions for Enterprise in 2026

Your production LLM applications are processing millions of requests across dozens of teams—with no centralized visibility. Enterprise LLM monitoring demands multi-team governance, compliance across regulated business units, and organization-wide cost allocation.

Without proper monitoring, you face ballooning costs, silent performance degradation, compliance blind spots, and no audit trail when regulators ask questions.

According to McKinsey's State of AI research, 88% of enterprises now use AI in at least one business function—yet scaling remains a significant challenge.

Enterprise-grade LLM monitoring platforms solve these challenges through centralized visibility, policy enforcement, cost tracking, and audit-ready logging. This analysis examines the top platforms with proven enterprise capabilities at scale.

TLDR:

• Enterprise LLM monitoring requires compliance certifications, multi-tenant architecture, and audit trails that developer tools lack

• Galileo processes 20M+ daily traces with deployment flexibility including on-premise and air-gapped options

• Enterprise LLM monitoring requires compliance certifications (SOC 2 Type II, ISO 27001, HIPAA, GDPR) to meet regulatory requirements across regulated industries

• Production-scale observability demands tracing millions of daily LLM interactions with sub-200ms latency for real-time quality evaluation

• Deployment flexibility—including SaaS, on-premise, and air-gapped options—addresses data sovereignty requirements for sensitive workloads

• Open-source LLM monitoring tools provide entry points for evaluation, but enterprise deployments require commercial support and SLAs

What is an enterprise LLM monitoring solution?

Enterprise LLM observability solutions are platforms that provide centralized, production-grade visibility and control over LLM applications across your organization. These aren't developer debugging tools—they're infrastructure designed for multi-team access controls, compliance certifications, SLA guarantees, and scalability to millions of daily traces.

What separates enterprise solutions from developer-stage tools? Gartner's LLM Observability Innovation Insight identifies the distinction: enterprise platforms provide threshold-based alerting, aggregated dashboards, and production runtime environments. Developer tools prioritize experimentation. Enterprise requirements include SOC 2 Type II or ISO 27001 certification, SSO integration, audit trails, and multi-tenant architecture.

For you as an AI leader, this translates to organization-wide visibility, cost governance that finance teams can work with, and compliance confidence for legal and risk management.

1. Galileo

Galileo operates as a purpose-built enterprise AI reliability platform unifying evaluation, monitoring, and runtime protection in a single system. The platform processes 20 million traces daily across 50,000 concurrent agents without complex capacity planning—scale that matters when supporting multiple production applications across business units.

What distinguishes Galileo from point solutions is the integration of core capabilities into one eval engineering workflow. Experiments enable systematic evaluation across prompts, models, and configurations using metrics in five categories: agentic performance, response quality, safety and compliance, expression and readability, and model confidence—plus custom evaluators. 

Logging and monitoring provide real-time visibility through traces, sessions, and spans. Runtime protection delivers production guardrails through a configurable rules engine powered by Luna-2 SLMs for blocking hallucinations, prompt injections, PII leakage, and toxic content.

Luna-2 models—fine-tuned Llama 3B and 8B variants—deliver sub-200ms latency at 97% lower cost than GPT-4-based evaluations. Continuous learning via human feedback (CLHF) improves metric accuracy over time, making continuous evaluation viable at scale.

Deployment flexibility addresses data residency requirements. Options span SaaS, virtual private cloud, on-premise, and fully air-gapped deployments on AWS EKS, Google GKE, or Azure AKS. For regulated industries where data cannot leave your environment, this flexibility is non-negotiable.

The company has raised $68 million in total funding, including a $45 million Series B led by Scale Venture Partners, providing the financial stability enterprise procurement teams evaluate.

Key features

• Galileo Signals: Analyzes 100% of production traces proactively, detecting security leaks, policy drift, and cascading failures

• Luna-2 evaluation models: Fine-tuned Llama 3B and 8B variants with sub-200ms latency and CLHF continuous improvement

• Runtime protection engine: Configurable rules, rulesets, and stages for PII detection, prompt injection prevention, and hallucination verification

• Multi-deployment architecture: SaaS, VPC, on-premise, and air-gapped configurations

• Framework-agnostic integration: Python and TypeScript SDKs with OpenTelemetry, plus OpenAI Agents SDK, LangChain, LangGraph, CrewAI, and Google ADK

Strengths and weaknesses

Strengths:

• Verified scale at 20M+ daily traces across production enterprise deployments

• Deployment flexibility including air-gapped environments

• Unified evaluation, observability, and runtime protection with configurable rules engine

• Luna-2 enables cost-effective continuous evaluation with CLHF improvement

Weaknesses:

• Public compliance certification documentation not available—enterprise buyers must request SOC 2 and ISO 27001 documentation directly

• Enterprise pricing requires sales engagement; see pricing page for tier details

Use cases

Fortune 500 companies including HP, Reddit, Comcast, Cisco, Twilio, and ServiceTitan deploy Galileo for production LLM monitoring. A Fortune 50 consumer products company achieved 75% reduction in evaluation time—the kind of efficiency gain that moves the needle at scale.

Multi-team governance scenarios benefit from centralized visibility across distributed AI initiatives. When dozens of teams build LLM applications independently, you need a single pane of glass showing cost allocation, quality metrics, and compliance status.

2. Arize AI

Arize AI brings enterprise ML observability heritage to LLM monitoring with $127 million in total funding across three rounds, most recently a $70 million Series C in February 2025. Phoenix, their open-source project, provides full-featured tracing on an OpenTelemetry foundation—critical for avoiding vendor lock-in. 

You can self-host Phoenix on Docker or Kubernetes for technical proof-of-concept, then migrate to AX Enterprise when you need compliance certifications and managed infrastructure.

Key features

• OpenTelemetry-based tracing: Standardized architecture capturing LLM calls, retrieval operations, and tool usage

• Phoenix open-source: Full self-hosting capability on Docker or Kubernetes before commercial commitment

• Multi-provider LLM support: Amazon Bedrock, Anthropic, Google Vertex AI, Groq, MistralAI, OpenAI, and OpenRouter

• Evaluation frameworks: LLM-as-a-Judge automated evaluation, human annotation queues, and integration with Ragas, Deepeval, and Cleanlab

Strengths and weaknesses

Strengths:

• Comprehensive compliance certifications: SOC 2 Type II, ISO 27001, HIPAA, GDPR documented on Arize Trust Center

• OpenTelemetry foundation reduces vendor lock-in risk

• Phoenix OSS enables risk-free technical validation

• SSO integration with Okta and Azure AD/Entra ID

Weaknesses:

• Enterprise pricing requires sales engagement—no public baseline for budget planning

• Series C funding completed in February 2025 means long-term platform stability at new scale cannot yet be assessed

Use cases

Organizations with strict data sovereignty requirements use Phoenix for self-hosted deployments where raw data never leaves their environment, then adopt AX Enterprise for compliance-required production monitoring. 

Teams transitioning from traditional ML monitoring to LLM observability find familiar concepts applied to new challenges. Multi-provider LLM strategies benefit from broad integration across cloud and model providers.

3. Weights & Biases

Weights & Biases brings a massive enterprise MLOps footprint to LLM monitoring through their Weave product. The platform has established itself in enterprise MLOps with a track record of handling cutting-edge LLM workloads at scale. CoreWeave completed their acquisition of W&B in May 2025, creating a vertically integrated infrastructure-to-MLOps offering that introduces both consolidated accountability and vendor concentration risk.

Key features

• Automatic call tracking: Functions decorated with @weave.op() capture inputs, outputs, and metadata without manual instrumentation

• Trace trees: Hierarchical visualization of nested function calls and LLM interactions

• Deployment flexibility: Cloud-hosted, self-hosted, and Advanced Enterprise self-hosted options

• ML CI/CD integration: Webhooks for automated pipeline integration

Strengths and weaknesses

Strengths:

• Verified compliance: SOC 2 Type 2, ISO/IEC 27001, ISO/IEC 27017, ISO/IEC 27018, HIPAA (with BAA), and GDPR documented

• Extreme-scale validation through enterprise deployments handling cutting-edge LLM workloads

• Unified system of record for all AI projects, models, and experiments

• Active bug bounty program and regular third-party security audits

Weaknesses:

• No public pricing information—budget planning impossible without sales contact

• Recent acquisition by CoreWeave (May 2025) creates integration uncertainty and potential vendor concentration risk

• No public SLA documentation for uptime guarantees

Use cases

Organizations with existing MLOps investments in W&B can extend their infrastructure to LLM monitoring without adopting new tooling. Teams building multimodal AI applications benefit from native support for text, images, and audio within unified traces. Healthcare and life sciences deployments leverage the comprehensive compliance certifications for regulated AI workloads.

4. LangSmith

LangSmith provides comprehensive, framework-agnostic tracing through Python and TypeScript SDKs plus OpenTelemetry support, with deeper integration for LangChain and LangGraph applications. Four deployment models address different requirements: cloud SaaS, hybrid deployment with split control and data planes, self-hosted for full infrastructure control, and standalone server. Self-hosted and hybrid options keep data within your VPC.

Key features

• End-to-end tracing: Captures every step of agent execution and reasoning process

• OpenTelemetry support: Standards-based tracing across any framework, not just LangChain

• Four deployment models: Cloud, hybrid, self-hosted, and standalone configurations

• Agent framework integrations: LangChain, LangGraph, AutoGen, CrewAI, and Claude Agent SDK

Strengths and weaknesses

Strengths:

• Verified compliance: SOC 2 Type 2 attestation and GDPR-compliant processing documented; HIPAA-eligible with BAA rather than HIPAA-certified

• Framework-agnostic with deep LangChain/LangGraph integration

• Four flexible deployment models including self-hosted and hybrid options

• OpenTelemetry support enables monitoring beyond LangChain ecosystem

Weaknesses:

• No verified adoption metrics in official sources—scale validation requires direct reference requests

• Agent Authorization feature currently in beta—GA timeline undocumented

• Enterprise pricing opacity prevents preliminary budget estimation

Use cases

Teams building with LangChain or LangGraph frameworks benefit from deep native integration, while OpenTelemetry support enables monitoring of non-LangChain components. Organizations requiring hybrid deployments where the control plane stays centralized but data planes remain in customer VPCs can utilize self-hosted options.

5. Datadog

Datadog extends their established enterprise infrastructure monitoring into LLM observability through their dedicated LLM Observability product. The platform provides end-to-end visibility for AI agents and LLM systems, correlating AI/ML workload anomalies with underlying infrastructure issues. This unified approach requires Datadog's dedicated LLM Observability module in addition to existing infrastructure monitoring.

Key features

• Unified observability platform: Correlates LLM metrics (latency, token usage, cost) and traces with infrastructure monitoring through unified dashboards

• Distributed tracing for LLM workflows: Captures inputs, outputs, latency, token usage, and errors at each step

• GPU monitoring for AI workloads: Detects hardware and network failures affecting AI applications

• Managed ML platform integrations: AWS SageMaker, Azure Machine Learning, Google Vertex AI

Strengths and weaknesses

Strengths:

• Verified compliance: SOC 2 Type 2, ISO/IEC 27001, ISO/IEC 27017, ISO/IEC 27018 (documented on Datadog Trust Center)

• Eliminates tool sprawl for organizations already using Datadog

• Granular RBAC with resource-level access control

• SAML 2.0 SSO support for enterprise authentication

Weaknesses:

• Usage-based pricing per LLM request requires careful cost modeling

• LLM observability features less specialized than purpose-built platforms

• Enterprise LLM-specific pricing details not publicly disclosed

Use cases

Organizations with existing Datadog investments can consolidate LLM monitoring through the dedicated LLM Observability product, though careful cost modeling is required given usage-based pricing. Infrastructure teams responsible for both traditional applications and AI workloads benefit from unified alerting and dashboards.

6. Honeycomb

Honeycomb brings deep observability heritage and a high-cardinality query engine to LLM monitoring. The platform is specifically engineered to handle high-cardinality and high-dimensionality data at scale, querying millions of unique attribute values without pre-aggregation—essential for debugging edge cases in production LLM systems.

Key features

• High-cardinality query engine: Group or filter on any attribute regardless of cardinality without pre-aggregation

• BubbleUp anomaly detection: ML-powered pattern surfacing in high-cardinality data

• Event-based data model: Preserves full context of each LLM interaction including complete prompt-response pairs

• Distributed tracing: Visibility from API gateway through RAG retrieval to LLM inference and post-processing

Strengths and weaknesses

Strengths:

• Comprehensive compliance: SOC 2 Type II, ISO 27001, HIPAA, GDPR, PCI DSS (documented on Honeycomb compliance page)

• High-cardinality performance critical for LLM debugging at scale

• Production-validated by Honeycomb's own Query Assistant feature

• Event-based model preserves debugging granularity that metrics aggregation loses

Weaknesses:

• Limited public framework integration documentation for popular LLM frameworks

• Custom enterprise pricing requires direct sales engagement with no published baseline

Use cases

Teams debugging edge cases in production LLM systems benefit from high-cardinality query capabilities that surface specific failure patterns without performance degradation. Organizations invested in distributed tracing methodologies find Honeycomb's event-based observability model maps naturally to LLM application architecture.

Building an enterprise LLM monitoring strategy

Enterprise LLM monitoring has shifted from optional tooling to critical infrastructure. The LLM observability platform market is projected to grow from $1.44 billion in 2024 to $6.8 billion by 2029, representing a compound annual growth rate exceeding 35%—reflecting the reality that organizations scaling AI beyond pilot projects cannot operate without centralized visibility.

Operating without proper monitoring exposes you to uncontrolled costs across teams, compliance gaps that create regulatory liability, and no visibility into model behavior when stakeholders ask questions. Specific operational failures documented at enterprise scale include hallucinations, latency degradation, context window exhaustion, and prompt drift.

Galileo provides enterprise-grade AI reliability with the deployment flexibility, scale, and integrated capabilities you need for production AI operations:

• 20M+ daily trace processing: Verified scale across 50,000 concurrent agents without complex capacity planning

• Signals: Analyzes 100% of production traces to surface security leaks, policy drift, and cascading failures

• Luna-2 cost efficiency: Sub-200ms evaluation latency at 97% lower cost than GPT-4-based approaches

• Runtime protection engine: Configurable rules for hallucination detection, PII protection, and prompt injection prevention

• Deployment flexibility: SaaS, VPC, on-premise, and air-gapped configurations for data sovereignty

• Fortune 500 validation: Proven deployments at HP, Reddit, Cisco, Comcast, Twilio, and ServiceTitan

Book a demo to see how Galileo enables enterprise AI reliability with centralized visibility across your AI operations.

Frequently asked questions

What separates enterprise LLM monitoring from developer-stage observability tools?

Enterprise LLM monitoring provides multi-team governance, audit trails, SLA guarantees, and scalability to millions of daily traces. Developer tools prioritize experimentation and semantic evaluation. Enterprise platforms focus on operational metrics, threshold-based alerting, and compliance capabilities required for regulatory compliance in production deployment across regulated organizations.

When should you implement centralized LLM monitoring versus team-level tools?

Implement centralized monitoring when managing multiple teams building LLM applications, addressing compliance requirements across business units, or needing consolidated cost tracking for finance. According to Gartner's analysis, LLMs require specialized observability approaches including prompt versioning, semantic quality monitoring, and human feedback integration—capabilities team-level developer tools typically don't address.

Should you use existing infrastructure monitoring or a dedicated LLM monitoring platform?

If you already use Datadog or Honeycomb, their LLM observability extensions provide unified visibility within your existing infrastructure monitoring. However, purpose-built platforms like Galileo or Arize AI offer deeper LLM-specific capabilities—semantic evaluation, hallucination detection, prompt versioning—that infrastructure monitoring tools add as features rather than core architecture.

How do you evaluate LLM monitoring vendors for compliance requirements?

Request SOC 2 Type II reports, ISO 27001 certificates, and BAA documentation directly from vendors. Validate deployment options match your data residency requirements. Check SSO integration with your identity provider and RBAC granularity for multi-team access control.