Enterprise-grade multi-agent platform with visual workflow builder, managed deployment, SOC2 compliance, and team collaboration for production AI agent systems.
The commercial version of CrewAI — visual workflow builder, cloud deployment, and team collaboration for production AI agent teams.
CrewAI Enterprise is a paid enterprise agent platform (custom pricing, reportedly ~$120K/year) that extends the open-source CrewAI multi-agent framework with managed deployment, compliance certifications, and visual workflow tooling for production AI systems.
CrewAI Enterprise (CrewAI+) is the commercial platform built on top of the popular open-source CrewAI multi-agent framework (30,000+ GitHub stars). It extends the framework with enterprise features that bridge the gap between prototype and production: a visual workflow builder, managed deployment infrastructure, monitoring dashboards, and team collaboration tools.
The visual builder lets teams design multi-agent workflows without writing code. Users drag and drop agents, configure their roles and tools, define task dependencies, and test against live model calls — all in the browser. Workflows authored visually are fully interoperable with SDK-defined crews, so code-first and low-code approaches coexist.
Deployment is one-click: the platform containerizes workflows and runs them on managed Kubernetes clusters with autoscaling, load balancing, and high availability. Organizations that require data sovereignty can self-host on their own VPC infrastructure. SOC2 Type II certification is confirmed; the company also reports pursuing FedRAMP High authorization and SAM registration for government contracts.
The operational monitoring dashboard tracks every agent execution with per-step latency, token consumption, and dollar cost. Built-in PII detection and masking, RBAC, audit logging, and SSO via Microsoft Entra and Okta round out the governance layer. The platform supports multi-model routing — including OpenAI, Anthropic Claude, Google Gemini, AWS Bedrock, Azure OpenAI, and open-weights models like Llama and Mistral — configurable per agent within a single workflow.
Pricing is contract-based with unlimited seats and up to 30,000 included executions, reportedly reaching approximately $120,000/year at enterprise scale. Forward-deployed engineers and on-site training are included in enterprise contracts, with typical implementation timelines of 3–6 months for self-hosted deployments.
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Browser-based drag-and-drop canvas for designing crews of agents, configuring roles, tools, and task dependencies without writing code. Workflows authored visually are fully interoperable with SDK-defined crews, so engineers and non-engineers can collaborate on the same agent system. Includes in-browser testing so workflows can be validated against real model calls before deployment.
One-click deployment turns any workflow into a scalable API endpoint backed by managed Kubernetes clusters with autoscaling, load balancing, and high availability built in. Containerization and orchestration are handled by the platform, removing the DevOps burden that typically blocks multi-agent systems from reaching production. Supports both CrewAI-hosted cloud and customer VPC self-hosting.
Operational dashboard surfaces every agent execution: which tools are being called, latency per step, token consumption, and dollar cost per workflow run. Failures and bottlenecks are flagged in real time with full execution traces. This level of observability is critical for cost control on LLM-heavy workloads where a single misbehaving agent can spike spend by orders of magnitude.
SOC2 Type II certified, with the company reporting pursuit of FedRAMP High authorization and SAM registration for federal contracts. Built-in PII detection and masking prevents sensitive data from leaking into model context, while role-based access control, workflow versioning, approval gates, and audit logging give security teams the controls they need to sign off on autonomous agent deployments.
Native knowledge management lets teams attach proprietary documents, databases, or existing knowledge bases to agent workflows; relevant context is surfaced automatically during execution. Combined with the open-source framework's extensive tool ecosystem, agents can call internal APIs, query SQL databases, and integrate with SaaS platforms without bespoke glue code. Custom tools can be authored in Python and registered to any crew.
Custom (~$120,000/year reported)
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