PandaProbe Cloud
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What is PandaProbe Cloud?
PandaProbe Cloud is a production-grade, fully managed agent engineering platform that provides full-stack tracing, evals, and monitoring for AI agent applications. It enables teams to ship better AI agents without operating infrastructure, handling trace ingestion, storage, dashboards, evaluation LLM-as-judge, and embedding models automatically. The platform captures complete agent trajectories including every tool call, LLM hop, and decision branch as structured traces composed of spans, giving queryable records of what ran, in what order, with inputs and outputs.
Key features include one-line instrumentation for major agent frameworks (LangGraph, LangChain, Google ADK, Claude Agent SDK, OpenAI Agents SDK, CrewAI), zero-code LLM tracing wrappers for OpenAI, Gemini, Anthropic, and Mistral, SOTA evaluation metrics purpose-built for long-running agents that detect uncertainty over trajectories, LLM-as-judge scoring with structured feedback, session-level evaluation beyond isolated traces, and continuous monitoring with daily/hourly/custom cron eval schedules against production traffic. The platform also includes built-in eval schedulers, auto-scaling for traffic spikes, role-based access control with SSO, human annotation support, andalerts on metric regressions across agent versions.
PandaProbe Cloud is designed for AI developers, engineering teams building AI agents, DevOps teams focused on AI deployment, AI researchers, and scaling projects from small teams to large enterprises. It serves hobbyists getting started, developers and small teams, scaling projects, and large organizations needing enterprise security requirements. The platform works with any LLM provider out of the box, captures spans and metadata automatically, and includes a ready-made SKILL.md for coding agents to manage traces and evals through natural language plus a full CLI for terminal API access.
PandaProbe Cloud pricing
Pricing model: Freemium
Hobby: $0/forever - 100 base trace ingestion/month, 100 trace eval runs/month, 10 session eval runs/month, human annotation, 1 seat, community support via GitHub. Pro: $29/month - 5k base traces/month (then pay-as-you-go), 5K trace eval runs/month (then pay-as-you-go), 100 session eval runs/month (then pay-as-you-go), 2 seats, email support. Startup: $299/month - 50k base traces/month (then pay-as-you-go), 50K trace eval runs/month (then pay-as-you-go), 1K session eval runs/month (then pay-as-you-go), 10 seats, high rate limits, private Slack channel, data retention management. Enterprise: Custom pricing - alternative hosting options (hybrid & self-hosted), custom SSO, dedicated engineering team access, support SLA, team trainings & architectural guidance, unlimited seats, dedicated support. Open Source self-hosted: Free under Apache 2.0 license with all core platform features and APIs.
PandaProbe Cloud pros
- Fully managed with zero infrastructure overhead for your team
- No servers to provision, maintain, or scale
- Managed evaluation LLM-as-judge and embedding models included
- No external API keys required for eval LLM
- Auto-scaling handles traffic spikes and enterprise volumes
- Built-in eval scheduler for daily, hourly, or custom cron runs
- Role-based access control and SSO included
- Continuous monitoring catches regressions before users do
- One-line instrumentation for every major agent framework
- Works with any LLM provider out of the box
- Captures spans, tool calls, LLM hops, and metadata automatically
- SOTA metrics detect agent uncertainty over long trajectories
- Evaluate full sessions not just isolated traces
- Alerts on metric regressions across agent versions
- Setup time is minutes instead of days to weeks
- Dedicated support channel and SLA guarantees for enterprise
- Human annotation included even on Hobby plan
- Pay-as-you-go pricing after base trace limits
- SKILL.md ready-made skill for coding agents
- Full CLI access from terminal for scripting workflows
PandaProbe Cloud cons
- Hobby plan limited to only 100 traces per month
- Pro plan only includes 2 seats maximum
- Startup plan limited to 10 seats
- Pay-as-you-go fees apply after base limits
- No alternative hosting options on lower tiers
- Self-hosted core features free but Cloud requires payment
- Community support only on Hobby plan via GitHub
- Email support only on Pro plan, no dedicated channel
- Limited trace eval runs on Hobby (100 per month)
- Only 10 session eval runs per month on Hobby plan
- SSO and team permissions require paid plan
- Data retention management only on Startup tier
- Private Slack channel only on Startup plan
- Enterprise plan requires custom pricing discussion
- No unlimited seats below Enterprise tier
Frequently asked questions about PandaProbe Cloud
What is PandaProbe Cloud?
PandaProbe Cloud is a production-grade, fully managed agent engineering platform that gives your team full-stack tracing, evals, and monitoring with zero infrastructure to manage. It handles managed trace ingestion, storage, dashboards, evaluation LLM-as-judge, and embedding models so teams can focus on building agents instead of operating tooling.
How does PandaProbe tracing work?
PandaProbe captures the full execution flow of your LLM as traces composed of spans. Each trace represents a single request or operation, while spans represent individual steps within that trace. Together they provide a structured, queryable record of what ran, in what order, and with what inputs and outputs, capturing tool calls, LLM hops, token usage, and TTFT.
What agent frameworks does PandaProbe support?
PandaProbe works with LangGraph, LangChain, DeepAgents, CrewAI, Google ADK, Claude Agent SDK, and OpenAI Agents SDK through one-line instrumentation integrations that automatically trace the full lifecycle including LLM calls, tool invocations, sub-agent handoffs, and guardrails.
What LLM providers are supported?
PandaProbe works with OpenAI, Gemini, Anthropic, Mistral AI, AWS Bedrock, and more out of the box. Zero-code wrappers wrap LLM clients to automatically trace every API call without refactors beyond the wrap call, supporting Chat Completions API, Responses API, generate_content, and messages.create.
What evaluation metrics does PandaProbe provide?
PandaProbe provides SOTA (state-of-the-art) metrics purpose-built for long-running agents that detect agent uncertainty over long trajectories, score trajectories, and pinpoint exactly where agents drift across entire lifecycles. It includes LLM-as-judge scoring with structured, actionable feedback and evaluates full sessions not just isolated traces.
How does PandaProbe monitoring work?
PandaProbe monitoring schedules eval runs against production traffic on any cadence including daily, hourly, or custom cron schedules. It spots behavioral drift and performance regressions the moment they appear and triggers alerts on metric regressions across agent versions so teams catch issues before users do.
What is the difference between Cloud and Open Source?
Cloud is fully managed with minutes setup time, hosted trace ingestion, managed eval LLM and embedding, built-in eval scheduler, included SSO/teams, managed storage and retention, dedicated SLA and support, and no infrastructure to maintain. OSS requires days to weeks setup, self-hosting, bringing your own eval LLM, self-managed scheduler and SSO, self-managed storage, community support, and ongoing infrastructure maintenance.
What tracing layers does PandaProbe offer?
PandaProbe offers three layers: Layer 1 Wrappers for zero-code LLM tracing (wrap_openai, wrap_gemini, wrap_anthropic), Layer 2 Integrations for automatic agent framework tracing (LangGraph, Google ADK, Claude Agent SDK, CrewAI, OpenAI Agents SDK), and Layer 3 Manual instrumentation with @pandaprobe.trace and @pandaprobe.span decorators plus start_trace() and span() context managers for full control.
What support is included on each pricing tier?
Hobby includes community support via GitHub only. Pro includes email support. Startup includes high rate limits and a private Slack channel. Enterprise includes dedicated support channel, support SLA guarantees, access to dedicated engineering team, and team trainings with architectural guidance.
Can I self-host PandaProbe?
Yes, PandaProbe is open source under Apache 2.0 license and you can self-host all core platform features for free without limitations. The self-hosted version includes all core platform features and APIs, scalability of PandaProbe Cloud, deployment docs, community support, and customization options. Enterprise tier also offers alternative hosting options including hybrid and self-hosted deployments.