Best Observability (2026)
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A buyer's guide to the best Observability tools for monitoring logs, metrics, traces, and events across modern distributed systems and AI workloads.
Observability i siffror
Prismix
Best Observability (2026)
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KeywordsAIUnified developer platform for building, monitoring, and scaling LLM applications.5.0 (6) - 2
GuardianSecurity and governance platform for autonomous AI agents and intelligent systems.5.0 (5) - 3
Maxim AIEnd-to-end platform for evaluating, monitoring, and improving AI agents4.8 (6) - 4
WeaveA no-code AI workflow builder that enables businesses to automate operations by integrating multiple large language models (LLMs) and connecting prompts seam...4.8 (5) - 5
llm scoutMonitor how your brand appears across ChatGPT, Claude, Perplexity, and Google AI Overviews.4.8 (5) - 6
FoundryAIBuild, evaluate, and improve AI agents for business automation4.8 (4) - 7
Helicone AIAll-in-one observability platform to monitor, debug, and improve production LLM apps.4.7 (6) - 8
Fiddler AIAI observability and security platform for monitoring, explaining, and governing ML and LLM applications.4.7 (6) - 9
Edwin AIAI agent for IT operations that speeds up incident detection, triage, and resolution.4.7 (6) - 10
Confident AILLM evaluation platform built on DeepEval for testing, monitoring and improving AI applications.4.6 (5)
KeywordsAI
Unified developer platform for building, monitoring, and scaling LLM applications.
KeywordsAI is a developer-focused platform that consolidates the tools needed to ship production-grade LLM applications. It provides a single API gateway for accessing multiple model providers, along with built-in observability, logging, and evaluation features to help teams understand how their AI features perform in the real world. The platform is designed to reduce the operational overhead of running LLM-powered products. Developers can monitor latency and cost, debug prompts, run evaluations, and manage prompt versions without stitching together separate tools. This makes it easier for engineering teams to iterate on AI features and maintain reliability as usage scales.
- Unified LLM gateway across providers
- Request logging and tracing
- Cost and latency monitoring
- Prompt experimentation and version control
- Evaluation and testing workflows
- SDKs and API integrations
Guardian
Security and governance platform for autonomous AI agents and intelligent systems.
Guardian is a security-focused platform designed to protect organizations deploying autonomous AI agents and intelligent systems. It provides monitoring, policy enforcement, and risk controls aimed at preventing misuse, data leakage, and unintended agent behavior. The tool targets enterprises and developers building agentic workflows who need visibility into what their AI systems are doing and guardrails to keep them aligned with business and compliance requirements. Guardian sits between AI models, tools, and end users to apply real-time checks and audit trails. By combining behavioral analysis with configurable policies, Guardian helps teams scale AI adoption while reducing exposure to operational and security risks.
- Agent behavior monitoring
- Configurable security policies
- Threat detection for AI workflows
- Audit logging and reporting
- Guardrails for autonomous actions
- Integration with AI agent frameworks
Maxim AI is a developer platform built to help teams ship reliable AI agents and LLM applications. It brings together prompt engineering, evaluation, observability, and dataset management so teams can iterate quickly while keeping quality measurable. The platform supports automated and human evaluations across multiple models and prompts, letting engineers compare outputs, detect regressions, and trace failures in production. It is designed for cross-functional collaboration, with workflows that allow both technical and non-technical stakeholders to contribute to testing and review. Maxim is typically used by teams building chatbots, copilots, voice agents, and multi-step agentic workflows that need consistent performance across changing prompts, models, and user inputs.
- Prompt playground and versioning
- Automated agent and LLM evaluations
- Production observability and tracing
- Dataset curation and management
- Human review and annotation workflows
- Multi-model and multi-provider support
Weave
A no-code AI workflow builder that enables businesses to automate operations by integrating multiple large language models (LLMs) and connecting prompts seam...
W&B Weave is an observability and evaluation platform that helps track and improve large language model (LLM) applications. Weave provides tools to trace, collect metrics, and evaluate application responses using LLM judges and custom scorers. Key features include tracing sessions, LLM calls, and tool calls, as well as manual instrumentation of custom agents. The platform supports integrations with popular SDKs and harnesses, as well as custom agent observability. Weave provides Python and TypeScript libraries for installing and using the platform. It is hosted on Weights & Biases (W&B), requiring a W&B account and API key for authentication. Users can trace calls to LLMs, review inputs and outputs, and view agent metrics in the Weave UI. While Weave facilitates automation and evaluation of LLM applications, it is not a no-code AI workflow builder as suggested by its name.
- Agent tracing and metric collection
- Custom agent observability
- LLM tracing and evaluation
- OpenTelemetry span support
- Weights & Biases (W&B) integrations
- Python and TypeScript libraries
llm scout
Monitor how your brand appears across ChatGPT, Claude, Perplexity, and Google AI Overviews.
LLM Scout is a brand monitoring tool built for the era of generative search. It tracks how your company, products, and competitors are mentioned across major AI assistants and answer engines, giving marketing and SEO teams visibility into a channel that traditional analytics tools miss. The platform runs recurring prompts against systems like ChatGPT, Claude, Perplexity, and Google's AI Overviews, then reports on share of voice, sentiment, citation sources, and changes over time. Teams can use these insights to refine content strategy, identify gaps where competitors are being recommended instead, and measure the impact of optimization efforts aimed at large language models.
- Brand and competitor mention tracking
- Monitoring across ChatGPT, Claude, Perplexity, and AI Overviews
- Sentiment and share of voice analysis
- Citation and source visibility
- Custom prompt tracking
- Historical trend reporting
FoundryAI is a development platform focused on creating AI agents that handle real business workflows. It combines agent design, testing, and continuous improvement tools so teams can move from prototype to production without stitching together separate systems. The platform emphasizes evaluation, giving builders ways to measure agent performance against defined tasks and refine behavior over time. This makes it suited for organizations automating customer support, internal operations, or repetitive knowledge work where reliability matters. FoundryAI targets technical teams who need more control than no-code builders offer but want faster iteration than building agents entirely from scratch.
- Agent building environment
- Evaluation and testing tools
- Performance monitoring
- Workflow automation support
- Iterative improvement loops
- Integration with business systems
Helicone AI
All-in-one observability platform to monitor, debug, and improve production LLM apps.
Helicone AI is a developer-focused observability platform built specifically for applications powered by large language models. It captures requests, responses, costs, and latency across providers, giving engineering teams a unified view of how their LLM features behave in production. Beyond logging, Helicone offers tools for debugging prompts, tracing multi-step agent workflows, running evaluations, and tracking user-level usage. Teams can identify regressions, control spend, and iterate on prompts with data rather than guesswork. It integrates with popular model providers and frameworks through a lightweight proxy or async logging, making it straightforward to add to existing stacks without major code changes.
- Request and response logging
- Cost and token usage tracking
- Prompt management and versioning
- Agent and session tracing
- Custom evaluations and dashboards
- User and rate-limit analytics
Fiddler AI
AI observability and security platform for monitoring, explaining, and governing ML and LLM applications.
Fiddler AI is an enterprise platform that helps teams monitor, analyze, and secure machine learning models and generative AI applications in production. It provides visibility into model performance, data drift, bias, and quality issues, while also offering safeguards against risks specific to LLMs such as hallucinations, prompt injection, and unsafe outputs. Designed for ML engineers, data scientists, and risk and compliance teams, Fiddler combines explainability, real-time monitoring, and guardrails in a single workflow. It integrates with common ML pipelines and cloud environments, helping organizations operationalize responsible AI practices at scale.
- Model performance and drift monitoring
- LLM hallucination and safety detection
- Prompt injection and jailbreak protection
- Explainable AI and root cause analysis
- Bias and fairness assessments
- Dashboards and alerts for production AI
Edwin AI
AI agent for IT operations that speeds up incident detection, triage, and resolution.
Edwin AI is an AI agent for IT operations designed to speed up incident detection, triage, and resolution. It provides a centralized platform for IT teams to investigate incidents, understand their impact, find or generate fixes, and apply them across existing tools without needing to switch between systems. Edwin AI correlates alerts, identifies root causes, and initiates remediation automatically, starting from the first alert through to verified resolution. It uses historical patterns and observability data to predict and prevent outages. The tool integrates with over 3,000 tools across observability, APM, security, and CMDB, enabling real-time, actionable insights and eliminating silos. A Forrester study found that Edwin AI delivered a 313% ROI for a composite organization, with a payback period of 6 months or less.
- Alert correlation and noise reduction
- AI-driven root cause suggestions
- Natural-language incident summaries
- Integrations with ITSM and observability platforms
- Automated triage workflows
- Knowledge enrichment from past incidents
Confident AI
LLM evaluation platform built on DeepEval for testing, monitoring and improving AI applications.
Confident AI is an evaluation and observability platform for teams building large language model applications. Powered by the open-source DeepEval framework, it provides a unified workspace to run benchmarks, regression tests and quality checks across prompts, models and retrieval pipelines. The platform helps engineers catch hallucinations, prompt regressions and retrieval failures before shipping, while offering production monitoring to track real user interactions. Teams can centralize datasets, share test results and iterate on prompts with measurable feedback rather than guesswork. It is aimed at developers, ML engineers and QA teams who want a structured, metrics-driven approach to LLM quality assurance rather than ad-hoc manual review.
- DeepEval-powered evaluation metrics
- Regression testing for prompts and models
- RAG and retrieval evaluation
- Production tracing and monitoring
- Dataset and test case management
- Team collaboration on evaluation results
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