AI Tool
AgentOps pricing, features, company info, and alternatives
A factual product page for AgentOps as an AI agent observability platform.
Last updated April 2026 · Pricing and features verified against official documentation
Pricing
Current public pricing tiers on file for AgentOps, last verified Apr 23, 2026.
Basic
$0 / month
Free plan with up to 5,000 events.
Pro
$40 / month
Pay-as-you-go plan with unlimited event limits, log retention, and export features.
Enterprise
Custom
Adds SLA, Slack Connect, custom SSO, on-premise deployment, custom retention, and self-hosting options.
What You Can Do With It
The main capabilities that shape how people use AgentOps today.
Automatically instruments agent and LLM calls in Python and TypeScript, then records traces and sessions in the dashboard.
Provides replay and drilldown views for session waterfalls, spans, errors, and cost data.
Exposes a read-only public API and an MCP server for querying trace and span data.
Supports self-hosting with Docker or native setup guides for teams that want to run the platform on their own infrastructure.
Best For
Who AgentOps is most clearly built for.
Developers who want lightweight observability for AI agents and LLM workflows.
Teams that need replay debugging, trace drilldown, and post-run analysis.
Organizations that want a self-hosted or API-driven observability stack for agent applications.
Company
Leadership and company context for STAF, INC.
Founders
Alex Reibman
Platforms
Where you can use AgentOps today.
Web
API
Python SDK
TypeScript SDK
Self-hosted
Integrations
Notable connected tools and ecosystem hooks for AgentOps.
OpenAI
CrewAI
AutoGen
LangChain
Privacy Notes
Publicly stated data-handling notes that matter when evaluating AgentOps.
The privacy policy says the service may collect email, sign-in data, phone number, payment details, browser metadata, and cookies.
The host-environment docs say AgentOps collects OS, Python version, anonymized hostname, SDK version, and process ID for debugging.
The host-environment docs say collected environment data is never sold or shared and can be opted out of with an environment variable.
Compliance
Public compliance or enterprise-governance signals we found for AgentOps.
SOC 2
HIPAA
NIST AI RMF
Access
How to integrate or build around AgentOps.
Public API
Yes
Docs
Available
Alternatives
Other tools worth considering alongside AgentOps.
Open-source LLM engineering platform for tracing, prompt management, evaluations, and analytics.
Framework-agnostic platform for observability, evaluation, and deployment of AI agents and LLM apps.
AI observability and evaluation platform for tracing, scoring, and improving production AI applications.
Product Snapshot
AgentOps is a developer platform for testing, debugging, and deploying AI agents and LLM apps. Its public surface includes a dashboard, Python and TypeScript SDKs, a read-only Public API, and self-hosting documentation.
What You Can Do With It
- Automatically instrument agent and LLM calls in Python or TypeScript and review the resulting traces in the dashboard.
- Drill into sessions, spans, errors, and cost data through replay-oriented views.
- Query trace and span data through the public API or the MCP server.
- Run the platform on your own infrastructure with the published self-hosting guides.
Why It Stands Out
It combines automatic instrumentation, replay debugging, a public API, and self-hosting options in one agent-observability stack.
Tradeoffs To Know
- The free Basic plan is capped at 5,000 events.
- Higher-tier controls such as SSO, on-premise deployment, and custom retention are reserved for Enterprise.
- The privacy and host-environment docs describe collection of browser metadata and machine details, with an opt-out flag for environment data.