How do their growth rates compare?

AgentSphere has a month-over-month growth rate of 0%; Rivet.dev has -44.3%.

How does pricing compare?

AgentSphere: Contact for Pricing. No fixed public tiers. AgentSphere offers a free trial and positions itself around flexible, usage-based and enterprise plans (bursting compute, private deployment, SLAs); pricing is available on request. Rivet.dev: Freemium, Contact for Pricing. Rivet Cloud uses a plan-plus-nodes model. The control plane is Free ($0) for prototyping, Hobby at $10/month, Team at $120/month, and Enterprise at $900/month, each raising control-plane redundancy, Actor Node caps, and support. Actor Nodes are billed per month on top, from $4.20 (R-5) to $94.50 (R-160). Self-hosting is free and open source under Apache 2.0, and startups from Y Combinator or a16z Speedrun get 50% off the Team plan for 12 months.

What are alternatives to AgentSphere and Rivet.dev?

Other tools in related categories include RunPod, Modal, BLACKBOX.AI, Mastra, Browserbase.

AgentSphere vs Rivet.dev — Traffic (0 vs 21.36K), Pricing & Verdict

Open AI Tools

AgentSphere vs Rivet.dev — Traffic (0 vs 21.36K), Pricing & Verdict | Open AI Tools

Side-by-Side Traffic & Specification Matrix

Dimension / Metric	AgentSphere	Rivet.dev
Official Domain	agentsphere.run	rivet.dev
Pricing Model	Contact for Pricing	Freemium, Contact for Pricing
Monthly Visits	0 (0)	21.36K (21,359)
MoM Traffic Delta	0 (0%)	-16.97K (-44.3%)
Primary Market & Share	🌐 Global (—)	🇺🇸 United States (39.4%)
Direct Visits / mo	0	10.71K
User Rating	4.3	4.5
Supported Platforms	Website	Website

AgentSphere

Agent-native cloud sandboxes for secure AI code execution

MCP-integrated cloud sandboxes

Connect MCP-capable clients and agents directly to isolated cloud sandboxes to execute AI-generated code and handle files securely.

Fast, isolated execution

Cold-start latency advertised as low as roughly 100ms, with sandboxes deployed in-region and isolation backed by lightweight VMs such as Firecracker.

Stateful agents and multi-stage tasks

Support for long-running workflows with storage persistence, snapshot recovery, streaming output and event-triggered reactivation.

Security and compliance posture

SOC2 and GDPR compliance, access control, output tracing and full audit logging designed for running untrusted AI code in regulated environments.

Strengths & Pros

+Purpose-built for agentic workloads rather than retrofitted from a generic VM or container service
+MCP-first integration lowers the effort to wire execution into existing AI assistants and frameworks
+Strong isolation and compliance story (Firecracker-based VMs, SOC2/GDPR claims) suits regulated industries
+Model- and language-agnostic, with private deployment in your own cloud and support for stateful, long-running tasks