Most managed AI tools charge per agent. You end up paying for several separate subscriptions to run specialized agents, such as a researcher, an operator and a support triage bot.
Hivra takes a different approach: paid plans give you a compute pool, not per-seat pricing. Each agent gets its own computer, memory, tools, and role, and they share your plan's compute: $9.99/mo for 2 vCPU and 4 GB RAM, or $19.99/mo for 4 vCPU and 8 GB RAM.
Why multiple agents outperform one generalist
A single agent that handles everything, from research and coding to customer support and content writing, has to context-switch constantly. Its system prompt grows bloated trying to cover every role. The memory store accumulates unrelated history that competes for context space on every task.
Specialized agents are more focused. A research agent configured for competitive intelligence has a system prompt, tool set, and memory structure tuned for that job. A coding agent has different tool access and different working memory. Each one is sharper at its task than a generalist would be.
Many larger AI teams are being built this way in 2026, as a portfolio of focused agents with defined handoff points between them.
Isolated memory, one compute pool
Each agent profile on Hivra has a completely separate memory store. The research agent does not see the coding agent's task history and vice versa. This prevents the interference that happens when a generalist agent tries to apply patterns from one domain to an unrelated task.
Each agent still runs on its own computer. At the plan level, those computers draw from one compute pool: you split your plan's vCPU and RAM across them instead of paying for each one separately.
The agents run side by side, not as a team. Hivra does not coordinate work between them today; built-in orchestration is planned, not shipped. When one agent's output should feed another, you set up that handoff yourself.
Practical configurations
A common setup on the $9.99/mo size (2 vCPU, 4 GB RAM): a research agent and a coding agent on different schedules. Morning: the research agent runs its daily brief. Afternoon: the coding agent handles a batch of file processing tasks. Overlap is minimal, so the shared pool goes further.
The $19.99/mo size (4 vCPU, 8 GB RAM) adds room for parallel work: a support agent triages incoming messages while a research agent runs daily briefs and a coding agent handles a batch of file-processing tasks.
What's included
- More than one agent on paid plans, with no per-seat pricing
- Each agent has isolated memory, tools, and configuration
- Manage all agents from a single dashboard
- Individual scheduling per agent profile
- Agents split one plan's compute pool
- Mix agent types: Hermes, OpenClaw, Claude Code, Codex and more
Common questions
Is there a limit on how many agent profiles I can create?
Yes. Each plan has a cap on active agents that matches its compute pool, and the checkout shows the cap for each plan before you pay.
Do the agents work together automatically?
Not yet. Each agent runs on its own. Built-in orchestration is planned but not shipped, so today you decide how work passes from one agent to another.
Can different agents use different AI models?
Yes. Each agent profile can be configured with its own model preference. One agent can use Claude Sonnet while another uses Haiku for cheaper high-frequency tasks, all from the same API key.
What changes on a larger plan size?
More vCPU, more RAM, and room for more agents running at once. Hosting is $9.99/mo for 2 vCPU and 4 GB RAM, or $19.99/mo for 4 vCPU and 8 GB RAM.
Can two agents write to the same output, like a shared document or spreadsheet?
This depends on your external tool configuration. Agents can be given access to the same Google Sheet, Notion database, or file share. Coordination on write timing is handled at the task level: you define which agent writes first and what the handoff looks like.