Past battle · 2026-07-25 UTC
Observability Showdown — July 25, 2026
From the Observability category. 21 marks placed across 9 fighters. Arize AI took the crown.
Final standings
The line-up
The fighters
Profiles of every tool that competed in this battle, ranked by their final score.
Arize AI
An AI observability and LLM evaluation platform that assists AI developers and data scientists in monitoring, troubleshooting, and enhancing the performance...
Arize AI is an AI observability and LLM evaluation platform. It assists AI developers and data scientists in monitoring, troubleshooting, and enhancing the performance of their AI models. The platform provides tools for identifying issues and proposing fixes, helping users improve their models' accuracy and reliability. Arize AI is designed for AI developers and data scientists who need to optimize their models' performance. The platform's capabilities include monitoring and troubleshooting, allowing users to quickly identify and address issues. Arize AI also provides features for evaluating and enhancing model performance, enabling users to refine their models over time. By using Arize AI, users can ensure their AI models are operating at peak performance, leading to better decision-making and outcomes. The platform's focus on observability and evaluation sets it apart from other AI development tools, making it a valuable resource for those working with large language models and other complex AI systems.
Criteria breakdown
- AI model monitoring
- Troubleshooting and issue identification
- Proposed fix generation
- Model performance evaluation
- LLM evaluation and optimization
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.
Criteria breakdown
- 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
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.
Criteria breakdown
- 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
AgentOps is a developer platform focused on the lifecycle of AI agents, providing tracing, monitoring, and debugging tools that surface what agents actually do at runtime. It captures LLM calls, tool usage, costs, and errors so teams can understand agent behavior across complex multi-step workflows. Beyond visibility, AgentOps offers session replay, performance analytics, and integrations with popular agent frameworks like LangChain, CrewAI, and AutoGen. This helps engineers move from prototype to production with measurable reliability instead of relying on guesswork or log scraping. It is aimed at developers and teams shipping agentic applications who need to track regressions, control spend, and prove that their agents behave correctly before and after deployment.
Criteria breakdown
- Agent session recording and replay
- LLM call and tool-use tracing
- Cost and token analytics
- Error and failure detection
- Framework SDKs for Python and JavaScript
- Dashboards for agent performance metrics
On the AI2AI project website, users can observe real-time conversations between two AI agents. To initiate an interaction, users must create two entities, assigning them a prompt, context, voice, and gender, and select a language model. The interaction can be started, saved, and shared with other users on the site. Example interactions are provided, showcasing different scenarios, such as seduction, anger, curiosity, and sales pitches. New users need to register and receive $1 in credit to explore the platform. Pricing is based on a per-minute rate, starting from 1 cent per minute.
Criteria breakdown
- Live AI-to-AI dialogue viewing
- Two distinct AI entities in conversation
- Observational, hands-off user experience
- Showcase of emergent AI behavior
- Accessible browser-based interface
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.
Criteria breakdown
- 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
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.
Criteria breakdown
- 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
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.
Criteria breakdown
- Agent building environment
- Evaluation and testing tools
- Performance monitoring
- Workflow automation support
- Iterative improvement loops
- Integration with business systems
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.
Criteria breakdown
- Agent tracing and metric collection
- Custom agent observability
- LLM tracing and evaluation
- OpenTelemetry span support
- Weights & Biases (W&B) integrations
- Python and TypeScript libraries