19 AgentOps tools for monitoring AI activity, issues, and costs
Pricing: Small free tier; Pro plans start at $50 per month with usage-based limits and costs
Standout feature: Real-time guardrails for deployed agents
Best for: Security-conscious installations that need to defend against hallucination and data leakage
Grafana Labs
Long the go-to source for open source telemetry, Grafana Labs now tracks performance of AI models in constellations of services. Grafana tracks the evolution of answers across the agentic network to recognize how small changes or hallucinations can spin out of control. It bills its system as “actually useful AI” and has even trademarked it. Its cloud assistant can configure and reconfigure the Grafana dash to offer the right level of observability. Its system includes AI-level analysis that can flag models that are responding quickly but offering bad answers because of problems such as model drift or context degradation.
Pricing: Basic free tier; Pro plan begins at $19 per month, includes better retention and some usage-based fees
Standout feature: Full-stack tool with fully integrated LLM tools
Best for: Large, enterprise-scale system adding AI
Helicone
Sometimes shoehorning in another tool into the chain can be tricky. Helicone is designed as a smart network proxy that will route all model requests while keeping solid debugging records from the data as it goes by. The data it captures can be turned into nice charts that make it easy to spot latency issues or model failures. Naturally, tracking AI spend is also a feature in much demand as bills continue to climb.
Pricing: Small free tier; Pro plan starts at $79 per month, includes features such as team collaboration and improved querying
Standout feature: Proxy-based integration
Best for: Development teams who want to add better monitoring features quickly
Laminar
Tracking agents in development and production means building strong storehouses of data enumerating what happened. Laminar works closely with OpenTelemetry to follow agents operating in production so that flaws and failure modes can be understood from log files stored efficiently with their own compression scheme. Developers can search through traces with an SQL-ish language and Laminar’s transcript view illuminates what happened. When necessary, the traces can enable developers to scroll back in time and replay the same inputs for debugging. The goal is to offer deep insights with high-level visibility of how well the agents are meeting business objectives.
Pricing: Small free tier; “Hobby” tier that adds more features at $30; Pro level starts at $150 per month
Standout feature: Open-source license makes self-hosting a viable option
Best for: Teams fully able to leverage open-source responsibilities
LangChain LangSmith
Real-time data from agents is essential for managing any mutli-agent system in production. LangSmith from LangChain traces costs, tools, and progress toward solutions for a wide collection of agents using SDKs for Python, TypeScript, Go, and Java. The OpenTelemetry-based solution watches for anomalies, issuing warnings and alerts through dashboards and communication channels such as PagerDuty. Deeper analysis can reveal issues such as topic clustering or odd patterns of failure. Coordination with agent deployment platforms such as LangGraph and deepagents ensures greater focus on successful resolution of assignments.
Pricing: Free for solo developers; Pro teams start at $39 per person per month
Standout feature: Systematic approach to regression testing of prompts
Best for: Teams relying on LangChain and LangGraph frameworks for supporting complex agentic behavior
Lunary
Watching the user experience is essential for building AI applications such as chatbots and assistants. Lunary offers a proxy that traces all interactions and then builds analytical dashboards for measuring metrics such as user satisfaction or model costs. One common usage is finding frequent topics and looking at the responses to ensure they deliver. When prompts aren’t perfect, Lunary lets teams iterate on the prompt text until the right answers are coming out. Its proxy structure and common API format enables Lunary to promise to work with “any LLM, any framework.”
Pricing: Free tier; Pro plan starts at $20 per month
Standout feature: Deep integration with humans for reviewing and optimizing results
Best for: Startups focused on rapid prompt innovation
NewRelic
The platform that began tracking performance of some web applications is now powerful enough to track the flows of data through complex agentic ecologies. NewRelic’s AI-driven monitoring watches for golden signals that can indicate misbehavior or worse throughout the entire lifecycle. It tracks every detail of the interactions through protocols such as MCP and then makes this available to the AI engineers responsible for performance. The dashboard provides the insights necessary to watch for toxic behavior, overt bias, drift, and overblown hallucinations. Predicting and maybe even controlling the cost is also a growing role as tokenomics becomes as important as response time.
Pricing: Free tier; Pro plan fees available through website
Standout feature: Full-stack support with hundreds of integrations with other tools
Best for: Established enterprise teams mixing in AI
Nova AI Ops
The goal of Nova AI Ops is to deliver a team of agents that watch over a cloud and make it, at least partially, self-healing. Each agent uses a mixture of predictive AI and machine learning to watch cloud telemetry reports for anomalies. Then they calculate the “blast radius” and decide whether this is a problem that can be fixed automatically “while you sleep” or saved for the human supervisors. These tools are aimed not just on LLM operations but on the stack as a whole.
Pricing: Small free tier; Standard pricing begins at $40 per user per month with usage billing
Standout feature: Focus on software reliability engineering helps teams deliver stable stacks
Best for: Teams that want to integrate LLMs into incident response and stability management
Splunk
The platform that began delivering smart logging is now fully AI capable, offering solutions that can watch over agents with much the same way that it continues to track microservices. Splunk now includes a fairly large amount of predictive AI for learning from the information in the logs and then turning this learning into fast solutions. This AI assistant can track deployed AI models connected by protocols such as MCP and watch over behavior while delivering the ability for users to drill down and explore what’s working and what’s failing. Their AI Canvas is meant to offer a central hub where the AI scientists can track both the local behavior of the models as well as their role in a larger data ecosystem.
Pricing: Activity-based pricing tracks usage of LLM backends and storage
Standout feature: Ready to scale to large enterprise stacks
Best for: Teams with legacy systems that are folding in agentic options
SuperPenguin
One of the most important parts of an AI service is the bill. SuperPenguin is a product designed to track consumption and make predictions so that the CFO won’t be surprised. The goal is to provide solid estimates about the total cost of each product by allocating costs to customers, features, and teams. If there’s a sudden shift, a “spike detector” will raise an alarm so that dev teams can ensure that the AI spend is worth it.
Pricing: Small free tier for experimentation; Growth tier for teams, starting at $30 per month; Pro tier offers deeper options starting at $200 per month
Standout feature: Strong accounting with invoice reconciliation and PR-level usage tracking
Best for: Teams that need precise cost accounting
Vellum
Prompt engineers spend time fussing over the details of tweaking, improving, and enhancing the words that guide the LLM. Vellum started as a company that would provide the pipeline so that you could manage and improve the prompts that ran again and again. Now the system is growing more powerful, offering a higher level of automation that lets you meta-manage the prompt chain. They’ve also begun marketing it as a form of personal assistant with pre-built connections to many of the major services such as Gmail. Its llm-cost-optimizer can juggle multiple options while finding a cheaper way to execute a prompt, a process the company suggests can save 60% or more.
Pricing: Open-source free tier; Pro plan starts at $35 per month
Standout feature: Focus on multi-model pipelines for true agentic solutions
Best for: Product teams with complex prompt engineering workflows
