PandaProbe Cloud

Key Features

• •Managed Trace Ingestion: PandaProbe Cloud automatically ingests and stores trace data from your agents, eliminating the need for self-hosted storage infrastructure. With auto-scaling, it handles traffic spikes and provides built-in dashboards for real-time visualization of agent behavior.
• •Managed Eval LLM: The platform runs evaluation LLMs and embedding models for you, so no external API keys are required. Combined with the built-in eval scheduler, you can automate continuous evaluations against production traffic to catch regressions early.
• •Auto-Scaling: PandaProbe Cloud automatically scales to handle traffic spikes, growing teams, and enterprise volumes without requiring manual capacity planning. This ensures consistent performance as your agent usage grows.
• •SSO & Permissions: Role-based access control and single sign-on (SSO) are included for growing engineering teams and enterprise security requirements. Manage who can view trace data and configure evaluations.
• •Continuous Monitoring with Eval Scheduler: The built-in eval scheduler allows you to run evaluations daily, hourly, or on custom cron schedules against your production traffic. This enables continuous monitoring of agent quality without manual intervention.
• •SLA & Support: PandaProbe Cloud offers dedicated support channels and SLA guarantees so your team can ship with confidence. Enterprise plans include access to a dedicated engineering team.
• •No Infrastructure Management: With PandaProbe Cloud, you have zero servers to provision, maintain, or scale. The fully managed service handles trace ingestion, storage, and dashboards.

Use Cases

• •Hobbyist Agent Experimentation: A hobbyist uses the free Hobby plan to start experimenting with agent tracing and evaluations. They can send up to 100 traces per month, run 100 trace evals, and use human annotation to understand agent behavior. Outcome: Quick learning and validation of agent concepts without upfront cost.
• •Small Team Agent Development: A small development team adopts the Pro plan ($29/month) to collaboratively build and evaluate agents. With 5,000 base traces and 5,000 trace eval runs, they can continuously monitor agent performance during development sprints. Email support ensures they get help when needed. Outcome: Faster identification of issues and improved agent reliability.
• •Scaling Startup Agent Quality Assurance: A growing startup uses the Startup plan ($299/month) to ensure consistent agent quality across a larger user base. With 50,000 traces and 50,000 eval runs, plus high rate limits, they can run extensive evaluations. The private Slack channel facilitates direct communication with the PandaProbe team. Outcome: Confident deployment of agents at scale with automated monitoring.
• •Enterprise Agent Observability: A large enterprise with strict security requirements uses the Enterprise plan with custom SSO, hybrid hosting options, and unlimited seats. They leverage the dedicated engineering team for architectural guidance and team trainings. Outcome: Full control over agent observability while meeting compliance and scalability needs.
• •Continuous Production Monitoring: An engineering team sets up the eval scheduler to run daily evaluations on production trace data. They catch regressions automatically and get alerts when agent performance degrades. Outcome: Proactive maintenance of agent quality without manual checks.

Who is this for?

PandaProbe Cloud is designed for engineers and engineering teams building AI agents. It caters to hobbyists and individuals starting with agent development, small teams collaborating on agent projects, scaling startups needing robust evaluation pipelines, and large enterprises requiring security, compliance, and dedicated support. The platform is ideal for organizations that want to eliminate infrastructure overhead and accelerate agent engineering. Specific roles include AI engineers, machine learning engineers, backend developers integrating agents, DevOps teams tired of managing monitoring stacks, and product managers overseeing agent quality. Companies of any size that rely on AI agents can benefit from fully managed observability and evaluations.