CrewAI gets you to a multi-agent demo fast, and AMP can deploy it. With a framework you assemble the parts and own the result — availability, crash recovery, and the pager are yours. With a platform the system comes assembled and operated, under a contractual SLA.
CrewAI is a Python framework for building multi-agent systems — Crews of role-based agents and event-driven Flows. Crews and Flows are authoring components. Your engineers wire in crash recovery, high availability, and streaming around them. The Enterprise control plane, AMP, adds a visual editor, deployment, RBAC, SSO, tracing, task guardrails, and org-wide runtime policies on top, but the framework underneath is still an agent-authoring layer.
AMP deploys to CrewAI cloud, your VPC, or your own infrastructure. Resuming a crashed run is an explicit call in your code. There is no published application availability SLA, so the uptime of the running system is yours to guarantee.
Akka gives you the agents, durable state, streaming, APIs, and governance as one pre-integrated runtime. You describe the system you want, and the platform generates it, tests it, and runs it. Because everything runs in one runtime, a system that compiles is ready for production.
Akka keeps it running around the clock, with active-active replication and automatic failover, and stands behind a contractual 99.9999% availability SLA on the running system.
Akka Specify builds agentic systems from your specifications and releases only what meets your definition of done.
| Dimension | With CrewAI | With Akka |
|---|---|---|
| Availability | No numeric availability SLA is published, and no active-active HA/DR or RTO/RPO posture. Your team owns application uptime and recovery. | Akka owns 99.9999% on the running system — a ~31-second yearly budget — active-active across regions, sub-1-minute RTO, zero-byte RPO. |
| State & memory | CrewAI checkpoints crew, flow, and agent state to JSON or SQLite after each task, or after each LLM call if configured. Resuming after a crash is an explicit restore_from call, and automatic checkpoint writes are best-effort. |
Held in memory, active-active replicated — 4 ms reads, sub-10 ms writes — and replayable from the event journal. Recovery is automatic and runtime-handled. |
| Governance | Task guardrails, tracing, and audit logging are provided. The Agent Control Plane enforces org-wide policies, such as PII redaction and tool restrictions, on automations running on AMP only. Pre-deployment classification and a sealed audit artifact are not provided. | Akka Verify applies any control to any AI system and guarantees its enforcement, including HITL, HOTL, guardrails, and evaluations, and records every call in tamper-evident storage. |
| Spec-driven delivery | Crew Studio builds crews and flows from a plain-language description and exports the source code. You check the result by running it and reading its traces. | Akka Specify builds agentic systems from your specifications and releases only what meets your definition of done. |
| Model optimization | llm_overlay assigns a model to each agent role, and crewai eval --models compares cost and time per model. crewai train turns human feedback into prompt guidance and changes no model weights. |
Akka Optimize puts each request on the best model for the task, including smaller models trained on your data. |
| Runtime track record | CrewAI reports use across a large share of the Fortune 500 and hundreds of millions of agent executions a month, with the AMP managed control plane still recent. | since 2009, 100,000+ deployments — the runtime behind systems two billion people use daily. |
| Infrastructure cost | Billed by execution: the free tier includes 50 workflow executions a month, and Enterprise pricing is custom. The durable runtime and streaming are provisioned and paid for separately. | One shared-compute runtime carries the same volume on up to 90% less infrastructure, at a fixed annual fee. Akka Optimize keeps improving the models on your own production traffic, so cost per task keeps falling as the system runs. |
| Portability | A production deployment accretes dependencies on the AMP control plane, execution metering, and separately sourced state and streaming layers. | Any cloud, on-prem, or sovereign — portable specs, BSL licensing, no per-execution meter. |
The difference is who writes the policy and what it covers. With CrewAI, developers code task guardrails into each crew, and AMP administrators set policies for automations running on AMP.
Guardrails, policies & approvals
With CrewAI, developers code task guardrails into each crew, and the @human_feedback decorator pauses a flow for human review. The Agent Control Plane applies 13 built-in policies across AMP deployments, enforcing PII masking, model and tool restrictions, and execution windows, and can pause and resume runs.
The Agent Control Plane governs only automations running on AMP. CrewAI publishes no mapping of its controls to the EU AI Act or any other regulation, and offers no pre-deployment classification or sealed audit artifact. AMP logs each human review request, response, and decision with a timestamp.
Block & escalate
Risk teams govern every agentic system without waiting on its developers, and every control they apply is guaranteed to be enforced. Risk teams choose from 1,718 controls drawn from 241 regulations and frameworks. Human-in-the-loop approvals, human-on-the-loop halt switches, sanitizers, guardrails, evaluations, testing gates, and simulations all run before an action executes and block or escalate anything that does not pass.
Every call is traced automatically into petabyte-scale, tamper-evident storage, and that same trace data feeds continuous evaluations that keep running in production, right alongside your agents.
Reach for the framework when you are prototyping or standing up a multi-agent system quickly, when the system is internal or low-stakes enough that some downtime is fine, and when your team is ready to own the crash recovery, high availability, and streaming that come with running it yourself. Authoring by hand is also the right call when you want low-level control over each crew and flow.
Reach for the platform when the system has to stay up, survive failure, and recover on its own, and when it has to pass an audit and enforce governance while it runs. Akka fits when you want a single vendor accountable for availability under an SLA, and when you would rather ship the system than build and operate the plumbing underneath it.
The Akka Agentic AI Platform builds, runs, governs and optimizes agentic systems under a contractual SLA. Tell us what you are building, and we will show you how it runs in production.
