LLM Observability Explained: 9 Tools for Metrics, Tracing, and Cost Monitoring in AI Apps (2026)

LLM applications run multiple steps per user request: retrieval, prompt construction, model calls, tool execution, and response generation. A problem in any one step can degrade the final output even if the request completes successfully and no system errors occur. Traditional monitoring shows uptime and latency but cannot reveal whether the model’s response was grounded, relevant, or appropriate for the user. According to the 2024 CNCF Annual Survey, 87% of organizations use logs and 57% use traces, but LLM observability requires capturing domain specific data like prompts, outputs, token counts, and evaluation scores that standard telemetry systems were not designed to handle.

This guide compares 9 LLM observability platforms across tracing depth, evaluation loops, cost attribution, OpenTelemetry support, and deployment model. Each tool is assessed on how well it captures the full execution path of an LLM request, measures output quality in production, and surfaces the cost drivers that make AI workloads unpredictable.

Quick Comparison: 9 LLM Observability Platforms at a Glance

Tool	Best For	Pricing	OpenTelemetry Native	On Prem Deployment
CubeAPM	Teams running AI workloads inside existing Kubernetes observability stacks	$0.15/GB ingestion, unlimited retention	✓ Native	✓ First class
Langfuse	Agent observability with open source flexibility and per trace cost attribution	Free open source, Cloud starts $59/mo	✓ Compatible	✓ Self hostable
Grafana Cloud AI Observability	OpenTelemetry native teams already using Grafana Loki Tempo	Usage based, free tier available	✓ Native	✓ Via self hosted Grafana
Datadog LLM Observability	Full stack teams needing broad provider coverage and compliance retention	Starts $31/host/month plus usage fees	Partial via OTLP	✗ SaaS only
Dynatrace AI Observability	Enterprise teams requiring audit trails and 10 year prompt retention	Custom enterprise pricing	Partial via OTLP	✓ Managed SaaS or private
Spanora	OTLP native backend for LLM and agent workloads with span level cost attribution	Usage based pricing	✓ Native OTLP backend	✗ SaaS only
GetMaxim	Distributed tracing and evaluation with human in the loop workflows	Free tier, paid plans from $99/mo	Partial via integrations	✗ SaaS only
Elastic Observability	Teams with existing ELK stack adding LLM monitoring via API logs and OTLP APM	Free self hosted, Cloud from $95/mo	✓ Via APM agent	✓ Self hosted Elastic
NVIDIA NIM Observability	NVIDIA infrastructure teams monitoring GPU inference metrics with Prometheus	Included with NIM deployment	Via Prometheus exporter	✓ On premises with NIM

Pricing figures reflect publicly available information as of April 2026. Enterprise discounts, custom contracts, and negotiated rates are not reflected here. Verify current pricing with each vendor directly.

What Is LLM Observability and Why Traditional APM Falls Short

LLM observability is the practice of capturing and analyzing inference level data from AI applications to monitor behavior, performance, output quality, and cost in production. It extends traditional application performance monitoring by adding domain specific signals: prompts, model outputs, token usage, retrieval context, tool call sequences, and evaluation scores.

Traditional APM tools track uptime, error rates, and latency. These metrics confirm that the system executed without failure but do not indicate whether the model’s response was factually correct, relevant to the user’s question, or safe to display. An LLM application can return a successful HTTP 200 response while producing a hallucinated answer, a policy violating output, or a response that fails to use the retrieved context. Without observability instrumentation that captures the full execution path and evaluates output quality, these failures remain invisible until users report them.

The Three Pillars of LLM Observability

Tracing records how a single request runs through an LLM application. Each step is captured as a span: the initial user input, embedding generation, vector search, retrieved documents, prompt construction, model call with parameters, tool invocations, and the final response. Spans are linked in a parent child structure that shows execution order and timing. Tracing makes it possible to see which step caused a failure or introduced latency.

Evaluation measures output quality using automated scorers or human review. Offline evals run against test datasets before deployment to catch regressions in prompt changes or model updates. Online evals run against live production traffic to surface failure patterns that do not appear in controlled tests. Common evaluation criteria include factual accuracy, relevance to the user’s question, groundedness in retrieved context, safety, and policy compliance.

Cost monitoring tracks token usage, model API costs, and infrastructure spend at the request level and aggregates it by user, feature, or business transaction. LLM costs scale with prompt length, output length, model choice, and call frequency. Without per request cost attribution, teams cannot identify which parts of the system are driving spend or measure the ROI of model changes.

1. CubeAPM

CubeAPM provides full stack observability for teams running AI workloads inside Kubernetes clusters alongside traditional microservices. It captures LLM traces, logs, and infrastructure metrics in one platform and correlates them with application performance data. Teams instrument their LLM applications using OpenTelemetry and route traces to CubeAPM’s self hosted backend, which runs inside their own cloud or data center. This keeps prompt and output data within the team’s infrastructure and eliminates public cloud egress costs.

Key Features:

• OpenTelemetry native ingestion for LLM traces with GenAI semantic conventions support
• Correlates LLM traces with APM data, logs, and infrastructure metrics in unified dashboards
• Self hosted deployment inside Kubernetes clusters for data residency and compliance
• Unlimited retention at $0.15/GB with no separate indexing or query fees
• Sub second search across high cardinality trace data including prompts and model outputs

Pricing: $0.15/GB for all ingested data with unlimited retention and no per seat fees. Self hosted deployment requires customer managed infrastructure.

Pros:

• Unified observability for AI and non AI workloads in one platform
• Full data control with on premises deployment
• Predictable flat rate pricing with no surprise overages
• Fast trace search with high cardinality support for prompt and output fields

Cons:

• Requires Kubernetes infrastructure and team capacity to manage self hosted deployment
• Evaluation and prompt experimentation features less mature than specialized LLM tools
• Smaller ecosystem of pre built LLM specific dashboards compared to SaaS platforms

Best for: Platform and DevOps teams running AI workloads in Kubernetes who need unified observability across LLM applications and infrastructure while keeping telemetry data inside their own cloud for compliance or cost control.

2. Langfuse

Langfuse is an open source LLM observability platform focused on agent workflows, evaluation, and per trace cost attribution. It supports OpenTelemetry and integrates with agent frameworks including LangGraph, CrewAI, and Pydantic AI. Teams can self host Langfuse or use the managed cloud service. The platform captures full execution traces for multi step agent workflows and attributes token costs to individual spans, making it possible to see which retrieval step, tool call, or model invocation drove spend for a given request.

Key Features:

• Native support for agent frameworks with trajectory level tracing and evaluation
• OpenTelemetry compatible ingestion with GenAI semantic conventions
• Per trace cost attribution showing token usage and API costs at span level
• Open source with self hosting option for full data control
• Evaluation strategies for single step, trajectory, and final response scoring

Pricing: Free open source self hosted. Langfuse Cloud starts at $59/month for small teams with usage based pricing for higher volumes. Verify current rates at langfuse.com/pricing.

Pros:

• Open source with active community and transparent development
• Strong agent workflow support with trajectory tracing
• Self hostable for teams with data residency requirements
• Detailed cost attribution at span level

Cons:

• Smaller ecosystem of integrations compared to enterprise platforms
• Some enterprise features like SSO and RBAC still maturing
• Cloud service pricing can scale quickly with high trace volume

Best for: Engineering teams building agentic AI applications who want open source flexibility, strong evaluation workflows, and the option to self host while maintaining detailed cost visibility per trace.

3. Grafana Cloud AI Observability

Grafana Cloud AI Observability extends Grafana’s existing observability stack to support LLM applications, vector databases, GPU monitoring, and MCP servers. It ingests traces via OpenTelemetry and correlates them with logs in Loki and metrics in Tempo. Teams already using Grafana for infrastructure and application monitoring can add LLM observability without introducing a separate platform. The service includes pre built dashboards for token analytics, cost tracking, and GenAI evaluation signals.

Key Features:

• OpenTelemetry native with GenAI semantic conventions support
• Pre built dashboards for LLM performance, token usage, and cost analytics
• Integrates with existing Grafana Loki Tempo workflows for unified observability
• Supports GPU monitoring and vector database instrumentation
• GenAI evaluation workflows with dashboards for evaluation outputs

Pricing: Usage based pricing with free tier available. Cost scales with trace ingestion volume and retention period. Verify current rates at grafana.com/pricing.

Pros:

• Fits into existing Grafana ecosystems without tool sprawl
• Strong cost and performance visibility with token level tracking
• OpenTelemetry standards based for vendor neutral instrumentation
• Active development with frequent feature additions

Cons:

• Full value requires familiarity with Grafana ecosystem and configuration
• Visualization tied to Grafana UI which has a learning curve
• Some advanced AI features require manual dashboard configuration

Best for: Teams already using Grafana, Loki, and Tempo for infrastructure monitoring who want to add LLM observability without migrating to a new platform or changing their OpenTelemetry instrumentation.

4. Datadog LLM Observability

Datadog LLM Observability provides full stack monitoring for LLM applications with support for major providers including OpenAI, Bedrock, Azure OpenAI, and Vertex AI. It captures traces, logs, and metrics from LLM inference and correlates them with application and infrastructure telemetry in Datadog’s unified platform. The service includes pre built dashboards for latency, token usage, error tracking, and prompt response pairs. Datadog states it can store prompts for up to 10 years depending on configuration, which supports compliance and audit requirements.

Key Features:

• Broad provider coverage for OpenAI, Bedrock, Azure OpenAI, Vertex AI
• Full stack correlation between LLM performance and infrastructure metrics
• Pre built dashboards for latency, tokens, errors, and prompt response logging
• Long term prompt retention up to 10 years for compliance
• Integration with Datadog’s existing APM, logs, and infrastructure monitoring

Pricing: Datadog APM starts at $31/host/month with additional usage based charges for logs, traces, and custom metrics. LLM Observability features are included in APM but total cost scales with host count and data volume. Verify current rates at datadoghq.com/pricing.

This estimate models a production ready setup with high availability. A smaller or simpler deployment may cost significantly less.

Pros:

• Comprehensive provider support with minimal instrumentation work
• Unified platform for LLM, application, and infrastructure monitoring
• Strong compliance story with long term prompt retention
• Mature enterprise features including RBAC, SSO, and audit logs

Cons:

• Pricing can grow quickly with increased host count and data ingestion
• SaaS only deployment means prompts and outputs leave your infrastructure
• Per host billing model less predictable for containerized workloads

Best for: Enterprises already using Datadog for full stack monitoring who want to add LLM observability without introducing a separate tool and need long term prompt storage for compliance or audit requirements.

5. Dynatrace AI Observability

Dynatrace AI Observability offers full stack monitoring for LLM applications, agents, RAG pipelines, and AI infrastructure. It integrates with Bedrock, Azure AI, LangChain, NVIDIA NIM, OpenAI, and Vertex AI. The platform captures application performance, agent execution traces, model metrics, and infrastructure signals in one view. Dynatrace emphasizes compliance with configurable prompt retention up to 10 years and strong audit trail capabilities for regulated industries.

Key Features:

• Full stack observability across LLM apps, agents, RAG pipelines, and infrastructure
• Broad integration support for major cloud AI providers and frameworks
• Long term prompt retention up to 10 years for compliance and audit
• AI assisted root cause analysis using Dynatrace Davis AI
• Enterprise grade security, RBAC, and audit logging

Pricing: Custom enterprise pricing based on infrastructure scale and feature set. Verify current rates at dynatrace.com/pricing.

Pros:

• Comprehensive full stack visibility with strong AI provider coverage
• Mature compliance and audit features for regulated industries
• AI assisted troubleshooting reduces mean time to resolution
• Flexible deployment including managed SaaS and private cloud options

Cons:

• Enterprise focused pricing can be high for smaller teams
• Complexity of feature set requires investment in onboarding and training
• Some advanced features locked behind higher tier plans

Best for: Large enterprises running mission critical AI applications in regulated industries who need comprehensive audit trails, long term prompt retention, and AI assisted troubleshooting with flexible deployment options.

6. Spanora

Spanora is an OpenTelemetry native backend designed for LLM and agent workloads. It ingests traces via OTLP and supports OpenTelemetry GenAI semantic conventions without requiring proprietary SDKs. The platform emphasizes span level cost attribution and provides a production readiness checklist that includes creating spans per LLM call and tool invocation, adding token and model attributes, and ensuring trace data is flushed before application exit.

Key Features:

• OTLP native ingestion with no required vendor SDK
• OpenTelemetry GenAI semantic conventions support
• Span level cost attribution for token usage and API costs
• Production readiness checklist for instrumentation best practices
• Vendor neutral approach supports multi provider LLM deployments

Pricing: Usage based pricing tied to trace ingestion volume. Verify current rates at spanora.io/pricing.

Pros:

• Fully vendor neutral with standard OTLP ingestion
• No SDK lock in allows flexible instrumentation
• Clear guidance on production ready tracing practices
• Cost attribution at span level for detailed spend analysis

Cons:

• Smaller ecosystem with fewer pre built integrations than larger platforms
• Less name recognition and community support compared to established vendors
• Feature set focused on tracing may require additional tools for evaluation or experimentation

Best for: Teams committed to OpenTelemetry standards who want vendor neutral LLM observability without proprietary SDKs and need detailed span level cost attribution for production workloads.

7. GetMaxim

GetMaxim focuses on distributed tracing and evaluation for LLM applications with session, trace, span, generation, retrieval, and tool call concepts. The platform supports both automated and human evaluation workflows and provides real time alerts with data warehouse export for long term analysis. GetMaxim emphasizes early monitoring and capturing full request response pairs to support debugging and quality assessment.

Key Features:

• Distributed tracing with session, trace, span, generation, retrieval, and tool call tracking
• Automated and human evaluation workflows with real time scoring
• Real time alerts for latency, errors, and quality degradation
• Data warehouse export for long term storage and custom analysis
• Full request response capture for debugging and audit

Pricing: Free tier available with paid plans starting at $99/month. Pricing scales with trace volume and evaluation usage. Verify current rates at getmaxim.ai/pricing.

Pros:

• Strong tracing model with clear semantic concepts for LLM workloads
• Human in the loop evaluation supports quality validation
• Real time alerting catches production issues quickly
• Data export enables custom analysis and long term storage

Cons:

• Newer platform with smaller user base and ecosystem
• Less breadth of provider integrations compared to full stack vendors
• Evaluation features may require configuration to match specific quality criteria

Best for: Teams building complex LLM applications who need distributed tracing with strong evaluation workflows and want to combine automated scoring with human review for quality validation.

8. Elastic Observability

Elastic Observability provides end to end LLM monitoring via API logs, metrics, and OTLP APM integration. It includes pre built dashboards for OpenAI, Bedrock, Azure OpenAI, and Vertex AI that surface latency, token counts, errors, and prompt response pairs. The platform leverages Elastic’s search capabilities for fast querying across large trace datasets and supports both self hosted and managed cloud deployments.

Key Features:

• API logs and OTLP APM integration for LLM trace ingestion
• Pre built dashboards for major cloud AI providers
• Fast search across prompts, outputs, and trace metadata using Elastic’s search engine
• Tracing and guardrails support for troubleshooting and compliance
• Flexible deployment with self hosted and Elastic Cloud options

Pricing: Free for self hosted open source Elastic Stack. Elastic Cloud starts at $95/month for standard plan with usage based scaling. Verify current rates at elastic.co/pricing.

Pros:

• Strong search and log analysis capabilities from Elastic’s core engine
• Flexible deployment model supports self hosting or managed cloud
• OpenTelemetry path enables vendor neutral instrumentation
• Pre built dashboards reduce time to first insight

Cons:

• Full value requires familiarity with Elastic Stack and configuration
• AI specific features still evolving compared to specialized LLM platforms
• Self hosted deployment adds operational overhead

Best for: Teams already using the Elastic Stack for logs and application monitoring who want to add LLM observability without migrating to a new platform and prefer flexible deployment options.

9. NVIDIA NIM Observability

NVIDIA NIM Observability provides Prometheus metrics for LLM inference on NVIDIA infrastructure. It tracks GPU cache utilization, request counts, token counts, time to first token, time per output token, end to end latency, and success failure rates. The metrics integrate with standard Prometheus and Grafana workflows for visualization and alerting.

Key Features:

• Prometheus metrics for GPU cache, requests, tokens, latency, and success rates
• Native integration with Prometheus and Grafana monitoring stacks
• Tracks time to first token and time per output token for latency analysis
• Success and failure rate metrics for reliability monitoring
• Included with NVIDIA NIM deployment at no additional cost

Pricing: Included with NVIDIA NIM deployment. No separate observability licensing fees.

Pros:

• Native GPU and inference metrics purpose built for NVIDIA hardware
• Standard Prometheus integration fits existing monitoring workflows
• No additional cost beyond NVIDIA NIM infrastructure
• Low latency metrics useful for performance tuning

Cons:

• Tied to NVIDIA infrastructure limits portability
• No built in agent or application level tracing
• Requires separate tools for prompt output capture and evaluation
• Limited to infrastructure and model performance metrics

Best for: Teams running LLM inference on NVIDIA NIM infrastructure who need GPU and inference performance metrics integrated with existing Prometheus and Grafana monitoring stacks.

How to Choose the Right LLM Observability Tool

Choosing an LLM observability platform depends on five factors that reflect how your team operates, what compliance requirements you face, and what failure modes you need to detect.

Deployment model and data residency requirements: Teams in regulated industries or with strict data residency rules need platforms that support self hosting or on premises deployment. Prompts and model outputs often contain sensitive user data, PII, or proprietary business information. SaaS only platforms route this data through external infrastructure, which may violate compliance policies. CubeAPM, Langfuse, Grafana Cloud AI Observability self hosted, and Elastic Observability self hosted support keeping telemetry data inside your own cloud or data center.

OpenTelemetry compatibility and vendor lock in: Platforms built on OpenTelemetry standards allow you to instrument your application once and change backends later without rewriting instrumentation code. Vendor proprietary SDKs create lock in that makes migration expensive. Langfuse, Spanora, Grafana Cloud AI Observability, and CubeAPM offer native OpenTelemetry support. Datadog and Dynatrace support OTLP ingestion but recommend their own SDKs for full feature access.

Tracing depth and execution path visibility: Production LLM failures often occur mid workflow: a retrieval step returns irrelevant documents, a tool call times out, or a prompt construction step omits critical context. Observability tools that capture every span in the execution path make these failures visible. Langfuse, GetMaxim, and Spanora emphasize detailed tracing with per span attributes for tokens, latency, and errors. Platforms with coarser tracing granularity may miss the exact step that caused a failure.

Evaluation and quality measurement capabilities: Offline evals catch regressions before deployment. Online evals measure quality in production under real user inputs. Teams building customer facing AI applications need both. Langfuse and GetMaxim provide evaluation frameworks with human in the loop workflows. Grafana Cloud AI Observability integrates evaluation signals into dashboards. CubeAPM and NVIDIA NIM focus on performance and infrastructure metrics but leave quality evaluation to external tools.

Cost attribution and budget control: LLM costs scale unpredictably with prompt length, output length, model choice, and retry logic. Without per request cost tracking, teams cannot identify which features or users are driving spend. Langfuse and Spanora provide span level cost attribution that links token usage to individual requests. Grafana Cloud AI Observability includes token analytics dashboards. Platforms without cost tracking require manual correlation between billing data and trace logs to understand spend drivers.

Disclaimer: The information in this article reflects the latest details available at the time of publication and may change as technologies and products evolve. Features, pricing, and plan limits can change over time. Always verify the latest information directly with the vendor before making purchasing or deployment decisions.

Frequently Asked Questions