One server. Multiple active agents.
Most managed AI tools charge per agent. You end up paying for three or four separate subscriptions to run specialized agents — a researcher, an operator, a support triage bot.
Hivra takes a different approach: you pay for a compute pool, not per seat. Pro includes up to 3 active agents and Power includes up to 5, each with its own memory, tools, and role.
Why multiple agents outperform one generalist
A single agent that handles everything — research, coding, customer support, 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 optimized 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.
This is how larger AI teams are being built in practice in 2026 — not one big agent, but a portfolio of focused ones with defined handoff points between them.
Isolated memory, shared infrastructure
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.
At the infrastructure level, the profiles share compute resources — the same vCPU pool and RAM allocation. This is efficient: not every agent is active simultaneously, so the compute that would sit idle on a per-agent plan is instead available across the full profile set.
Practical configurations
The most common setup on the Pro plan (2 vCPU, 4 GB RAM): up to 3 active agents running a mix of research and coding tasks at different schedules. Morning: the research agent runs its daily brief. Afternoon: the coding agent handles a batch of file processing tasks. Overlap is minimal and the compute is shared efficiently.
On the Power plan (4 vCPU, 8 GB RAM): up to 5 active agents with more headroom 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. Burst CPU on the shared pool keeps things responsive when several agents wake at once.
- 3 active agents on Pro, 5 on Power — no per-seat pricing
- Each agent has isolated memory, tools, and configuration
- Manage all agents from a single dashboard
- Individual scheduling per agent profile
- Compute shared efficiently across inactive profiles
- Burst CPU on Power when capacity allows
Is there a limit on how many agent profiles I can create?
Yes. Free includes 1 active agent, Pro includes 3, and Power includes 5. Those slot limits match the compute pool so the dashboard and provisioning gate stay consistent.
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 is the difference between Pro and Power for multi-agent use?
Pro gives you 3 active agents on 2 vCPU and 4 GB RAM. Power gives you 5 active agents on 4 vCPU and 8 GB RAM, plus more headroom for overlapping scheduled work.
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.