jurniti vs Kastra: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of jurniti and Kastra — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

jurniti

Paid

Managed 24/7 hosting for coding agents, each running in its own Firecracker microVM with your own model keys and no token markup.

Firecracker microVM Isolation: Every agent runs in its own KVM-backed virtual machine with hardware-enforced tenant isolation instead of a shared-kernel container.
Bring Your Own Key: OpenRouter, OpenAI or Anthropic keys live only inside the customer's VM — jurniti never sees them, never proxies calls and never marks up model spend.
Multi-Harness Support: Runs Claude Code, Codex CLI, OpenClaw, Hermes, OpenCode, Devin CLI, Mastra and Pi, each in its own dedicated microVM.
Fleet CLI: A jurniti command-line tool to boot agents, list fleet status, dispatch work and copy results back, so the whole fleet is managed from a terminal.
Swarm Runtime: Boots dozens of isolated microVM workers at once and dispatches the same brief to every worker, with results collected in a single command.
Flat Per-VM or Hourly Billing: A flat monthly or annual price per agent VM, or per-second metered On-Demand and Spot pricing for bursty workloads, with prepaid credits.
Automated Provisioning: Payment triggers a magic-link sign-in and an auto-provisioner that has a live microVM running in about three minutes with no human in the loop.
Custom Subdomain and Sidecars: Pro tiers add a custom subdomain, alongside separate microVM services for multi-agent communication and long-term agent memory.

Always-On Coding Agents: Keeping a Claude Code or Codex agent working on a backlog overnight without leaving a laptop running.
Secure Key Handling: Running agents for a team that cannot let model API keys leave its own infrastructure boundary.
Parallel Agent Fleets: Dispatching one brief to fifty isolated workers to compare approaches or parallelize a large refactor.
Bursty Batch Work: Using per-second Spot or On-Demand VMs for agents that only run a few hours a day, paying only for active runtime.
Self-Hosting Alternative: Replacing hand-rolled VPS setups for open-source agent harnesses like Hermes, OpenClaw or OpenCode.
Long-Running Agent Memory: Pairing an agent VM with a dedicated memory microVM so knowledge persists between sessions.

Kastra Labs Inc

Freemium

Kastra is the runtime authorization layer for AI: it decides in sub-milliseconds what agents, models, and tools are allowed to do before they act.

Sub-Millisecond Policy Decisions: Every prompt, tool call, and API request is evaluated in under a millisecond so enforcement never becomes the bottleneck.
Deterministic Policy Engine: Rules are written and evaluated deterministically, not by an LLM judge, so the same input always produces the same allow/deny.
Kastra Edge (Local Enforcement): A local enforcement component that runs next to the agent so decisions happen even without a network round-trip.
Cryptographic Audit Trails: Signed logs of every decision give security and compliance teams tamper-evident evidence of agent behavior.
Coding Agent Integrations: First-class hooks for Claude Code, Cursor, Codex, and OpenClaw let policies wrap the tool calls those agents already make.
Kastra Recon: Discovers what actions an agent actually attempts in a codebase or environment, so policies can be authored from observed behavior instead of guesses.
Zero Implicit Trust Model: Nothing an agent asks to do runs until it is explicitly allowed by policy, aligning agent access with zero-trust principles.

Guardrails for Coding Agents: Prevent an autonomous coding agent from dropping tables, force-pushing to main, or leaking secrets during long runs.
Enterprise Rollout Approvals: Give security teams a control plane before letting employee-facing AI agents access internal APIs.
Regulated-Industry Agent Deployments: Provide the auditable authorization trail required in finance, healthcare, or government agent pilots.
Multi-Agent Systems: Enforce per-agent scopes so a research agent can read data but only a deploy agent can trigger production changes.
Incident Forensics: Reconstruct exactly what an AI agent was allowed or blocked from doing after a suspicious action.