Sold by: crewAI
Deployed on AWS
CrewAI Enterprise Platform is a fully managed SaaS solution listed in AWS Marketplace that enables rapid creation, deployment, and management of AI driven agents across any infrastructure. With built-in governance, auditability, and real time observability, organizations can accelerate automation adoption, meet compliance requirements, and demonstrate ROI by deploying AI agents in CrewAI.
4.4
Overview
CrewAI Enterprise Platform is a turnkey SaaS solution for designing, deploying, and managing AI agents that automate complex workflows across any environment, private data centers, or hybrid infrastructures. Teams can define agents in human readable YAML or use our visual, low code editor to assemble multi step processes that call REST APIs, invoke serverless functions, run Python code, or interface with popular SaaS systems like Salesforce, ServiceNow, and Slack. With one click GitOps pipelines and a unified CLI or UI, you can test locally, replay executions for debugging, and promote agents through dev, staging, and production in minutes.
Built for the enterprise, CrewAI enforces role based access controls, policy driven approvals, and immutable audit logs for every agent execution, ensuring compliance and security at scale. Real-time dashboards surface throughput, latency, error rates, and estimated costs so you can monitor SLAs, pinpoint bottlenecks, and demonstrate automation ROI. Out of the box connectors, extensible REST and webhook frameworks mean you can integrate CrewAI into existing systems without heavy engineering lift. From customer support and software engineering, CrewAI Enterprise Platform empowers your organization to innovate faster by abstracting away operational complexity.
Highlights
- Rich Integrations Ecosystem. Tap into databases, messaging systems, serverless functions, and SaaS apps with out of the box integrations or custom tools.
- Visual & YAML Based Agent Design. Build AI agents via drag and drop workflows or human readable YAML for maximum flexibility.
- Managed Support. Rely on CrewAI expert services team for onboarding, incident response, and ongoing operational guidance.
Details
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Delivery method
Deployed on AWS
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Pricing
Pricing is based on the duration and terms of your contract with the vendor. This entitles you to a specified quantity of use for the contract duration. If you choose not to renew or replace your contract before it ends, access to these entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/month |
|---|---|---|
Basic | Includes 100 executions/mo, 2 live crews, 5 seats, self-service. | $99.00 |
Dimensions summary
This listing offers one contract option: the Basic plan. You buy a single package that bundles set usage limits together. It includes 100 workflow executions per month, 2 live crews, and 5 user seats. You set up and manage the platform yourself through self-service. Pricing does not scale by instance size or usage add-ons here. Instead, you get a fixed bundle of capacity under one commitment. If your execution, crew, or seat needs exceed these limits, you would move beyond what this plan covers.
Top-of-mind questions for buyers
An execution is a single run of an agentic workflow. A live crew is a deployed group of AI agents active on the platform. A seat is one user account with access. The Basic plan includes 100 executions per month, 2 live crews, and 5 seats.
The Basic plan sets fixed limits: 100 executions per month, 2 live crews, and 5 seats. Needs beyond these caps fall outside what this plan covers. The platform does not add usage-based charges within this plan. Contact the vendor to discuss capacity beyond these limits.
Self-service means you create your account, build crews, and deploy them yourself. You use the no-code Crew Studio or code to build workflows, then deploy to managed infrastructure and access crews through API endpoints. You set up and manage the platform without guided onboarding.
docs.crewai.com
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Software as a Service (SaaS)
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Customer reviews
Abinash D.
Crewai Speeds Up Role-Based Multi-Agent Prototyping
Reviewed on Oct 20, 2026
Review provided by G2
What do you like best about the product?
The best feature of Crewai is how quickly I can start prototyping, run, and work on multiple agents and make them role-based with its tool.
What do you dislike about the product?
It feels like it lacks control and tracing over observability compared to graph frameworks for free users; it's more difficult in the observation part.
What problems is the product solving and how is that benefiting you?
It solves the hassle of spinning multiple collaborative agents for my ideas and prototyping.
Jithin G.
CrewAI Makes Multi-Agent Prototyping Fast and Easy
Reviewed on Oct 13, 2026
Review provided by G2
What do you like best about the product?
The best thing about CrewAI is how easy it is to set up AI agents that can work together like a real team. As a developer, I really appreciate how quickly we can build working prototypes and plug in different models.
What do you dislike about the product?
Sometimes the agents get stuck in endless loops while trying to use tools or when talking to each other, which quickly wastes time and drives up API costs. Also, it can be hard to customize when we need full control over exactly how tasks are passed around.
What problems is the product solving and how is that benefiting you?
Instead of writing one huge, messy prompt that ends up confusing the AI, CrewAI lets me build small, focused agents that work together like a real team. That means no more headaches from writing complex background code just to pass data from one AI task to the next. Ultimately, it saves me time.
Nikhil V.
Clean, Straightforward UI with Consistent AI Results and Fair Pricing
Reviewed on Oct 12, 2026
Review provided by G2
What do you like best about the product?
I really like the user interface. It’s clean and straightforward, which makes it easy for me to manage my tools, repositories, and automations all in one place. The pricing also feels fair for the range of features included, especially for software engineers like me. The AI works well across many of my projects and tends to deliver consistent results from one project to the next. On top of that, the signup process is smooth and hassle-free; with just my login credentials, I’m taken straight to the projects assigned by my enterprise.
What do you dislike about the product?
I dislike that it fails to handle multiple automation runs simultaneously, and that integration is a little difficult. I recommend improving its integrations with major cloud services so developers can set up easier workflows.
What problems is the product solving and how is that benefiting you?
It helps me automate my projects for testing and debugging, which saves me a lot of time. I can then use that time to focus on developing new projects.
Telecommunications
CrewAI Makes Multi-Agent Orchestration Fast, Powerful, and Developer-Friendly
Reviewed on Oct 11, 2026
Review provided by G2
What do you like best about the product?
CrewAI delivers exceptional value as a multi-agent AI orchestration framework by pairing an accessible setup featuring clean Python/YAML-based configurations and intuitive CLI onboarding with a developer-centric UI that makes building, debugging, and observing complex agentic workflows seamless. Its intelligent core orchestrates autonomous role-playing agents that collaborate, delegate tasks, and execute sequential or hierarchical processes with remarkable efficiency. Supported by native integrations with LangChain, LlamaIndex, local LLMs (Ollama), and major vector databases, CrewAI maintains high performance and low latency across custom enterprise pipelines. The platform yields an immediate return on investment by automating multi-step business research and technical workflows, supported by active community channels, enterprise-grade documentation, and dedicated customer support.
What do you dislike about the product?
While CrewAI provides a flexible multi-agent framework, its primary drawbacks stem from an over-abstracted architecture that can make non-deterministic agent loops and circular delegation difficult to debug, along with memory and context decay in multi-step workflows. Non-technical users face onboarding friction due to its reliance on raw Python code or strict YAML configurations, while UI/UX options are split, as the full visual studio interface remains paywalled behind enterprise platform tiers. High-density runs with multiple agents carry latency and cost risks from continuous API calls, and native SaaS integrations are limited compared to custom developer setups. Additionally, smaller RIAs or SMBs can find the enterprise pricing tiers restrictive, while open-source support relies mainly on asynchronous community channels like GitHub or Discord rather than real-time live assistance.
What problems is the product solving and how is that benefiting you?
CrewAI solves the critical challenges of LLM context overload, single-prompt limitations, and complex multi-step process fragmentation by organizing autonomous AI agents into collaborative, specialized teams. Paired with a zero-cost open-source core and flexible cloud/AWS pricing models, it delivers low-latency orchestration and robust UI developer controls via code or visual flows. Native integrations with LangChain, Ollama, and vector databases allow agents to delegate tasks, pass structured memory, and query tools seamlessly. Backed by guided onboarding documentation and active enterprise/community support, this directly benefits daily workflows by automating complex research, software development, and analytical pipelines with measurable accuracy and immediate operational ROI.
Jeet S.
CrewAI Brings Clear Structure and Control to Multi-Agent Chatbot Workflows
Reviewed on Oct 07, 2026
Review provided by G2
What do you like best about the product?
What I liked about CrewAI is that it provides a clear structure for building AI workflows, instead of putting everything into one big prompt. I used the CrewAI framework in our custom chatbot, where we had different responsibilities such as identifying the intent of the question, applying guardrails, understanding or translating requests, and converting relative date questions (like “last 7 days”) into actual dates.
The agent- and task-based approach made it easier to keep these responsibilities separate and to change one part without disturbing the whole chatbot flow. I also liked that I could define more specific instructions and behavior for different steps, rather than relying only on the main LLM prompt.
For me, the biggest advantage was the control over the workflow. I could decide where the LLM should be used and where we needed more deterministic logic. This mattered because not every chatbot request should be handled as a free-form AI response.
For responses, CrewAI helped me create a skill (using its skills feature) so the bot would reply only in a professional, store-owner-friendly tone. It was easy to set up the workflow, and it saved a lot of time and effort during implementation. Performance was also solid: CrewAI felt fast even with multi-agent flows.
The agent- and task-based approach made it easier to keep these responsibilities separate and to change one part without disturbing the whole chatbot flow. I also liked that I could define more specific instructions and behavior for different steps, rather than relying only on the main LLM prompt.
For me, the biggest advantage was the control over the workflow. I could decide where the LLM should be used and where we needed more deterministic logic. This mattered because not every chatbot request should be handled as a free-form AI response.
For responses, CrewAI helped me create a skill (using its skills feature) so the bot would reply only in a professional, store-owner-friendly tone. It was easy to set up the workflow, and it saved a lot of time and effort during implementation. Performance was also solid: CrewAI felt fast even with multi-agent flows.
What do you dislike about the product?
The main thing I’ve found is that once the workflow becomes more complex, there’s a learning curve in deciding what should be an agent, what should be a task, and what should remain normal application logic. It was easy to start with a simple multi-agent flow, but production use needs more thought around prompts, tool calls, guardrails, state management, and error handling. Debugging can also take time when the final answer is affected by multiple steps.
I also feel that CrewAI can be more than what’s needed for a very simple chatbot. If the application only needs one LLM call with a straightforward prompt, using a full agent workflow can add unnecessary complexity and pain.
I also feel that CrewAI can be more than what’s needed for a very simple chatbot. If the application only needs one LLM call with a straightforward prompt, using a full agent workflow can add unnecessary complexity and pain.
What problems is the product solving and how is that benefiting you?
For me, CrewAI helped by breaking a complicated chatbot process into smaller AI responsibilities. Instead of relying on one prompt to handle everything, we separated pieces such as question intent, guardrails, the nature of the question, the time frame, and so on. This separation made the chatbot easier to maintain and test. For example, if we changed the intent logic, we didn’t need to rewrite the translation or date-handling parts. We also integrated multiple LLM models, like Gemini and Cerebras models, within one flow, which showed that integration was straightforward. Overall, that’s how CrewAI was useful for our chatbot setup.