Kopai vs Suprbox — Secure Storage for Autonomous AI Agents: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Kopai and Suprbox — Secure Storage for Autonomous AI Agents — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Kopai
Kopai
Serverless cloud for building, hosting, and monetizing domain-specialized AI agents, with RAG, orchestration, and per-message billing handled for you.
Key features
- Prompt-to-Agent Builder: Write a prompt, upload documents, and try several models side by side — seven steps from blank page to a shipped agent.
- Managed Infrastructure: Kopai holds the model keys, runs the vector database, and keeps the servers alive; you get an endpoint and a readable bill.
- Agent Marketplace: List an agent and get paid per message, keeping 70% of your markup, with every charge logged in an auditable ledger.
- Multi-Model Gateway: One integration across GPT-4o, Kimi K2, Gemini 2.5, Qwen 3, and DeepSeek, switchable at any time.
- Automatic Document Indexing: Upload PDF, DOCX, or XLSX files and Kopai indexes them and handles retrieval behind the scenes.
- Resilient Streaming: Answers resume from where they stopped after a dropped connection or closed tab, with no tokens lost.
- Conversational Agent Creation: Describe the job in ordinary chat and Kopai drafts the agent, picks its organization, and finishes on your approval.
- Kopai for Teams: Seats and roles, team-private agents, shared knowledge, and usage numbers you can check.
Best for
- A lawyer packages case-preparation expertise into an agent and sells access on the marketplace instead of billing hours.
- A consultant turns a library of internal documents into a domain expert clients can query directly.
- A solo creator wants to ship a RAG agent without standing up a vector database or backend service.
- A SaaS company embeds a specialized agent in its own product while letting Kopai handle billing and payouts.
- A team needs private internal agents with role-based access over a shared knowledge base.
- A developer wants to test the same agent across several model providers before committing to one.
Suprbox — Secure Storage for Autonomous AI Agents
Suprbox
Secure, purpose-built memory fabric that mediates document access and stores context and recall vectors for autonomous AI agents.
Key features
- Purpose-Built Memory Fabric: Provides a dedicated storage layer for agent context and state, optimized for storing and recalling contextual information used by autonomous agents.
- Vector Recall & Retrieval: Stores recall vectors and supports fast retrieval of context vectors so agents can access relevant context quickly during execution.
- Document Gateway: Sits between documents and agents to mediate reads and writes, preventing direct, uncontrolled agent access to source documents.
- Runtime Policy Enforcement: Enforces tight, fine-grained policies at execution time to control what agents can read, write, or execute against stored data.
- Execution Isolation: Isolates agent workspaces and memory to reduce risk of cross-agent data leakage and maintain separation between concurrent agent sessions.
- Controlled Context Delivery: Supplies agents only the allowed slices of context and vectors needed for a task, limiting exposure of sensitive information.
- Interposes between documents and autonomous agents to control access
- Purpose-built memory fabric for storing agent context and recall vectors
- Policy-driven execution and enforcement for agent operations
- Workspace isolation to prevent cross-agent data leakage
- Access controls and document-level gating for agents
- Designed for auditability and runtime visibility of agent access patterns
- Integrates with agent workflows to mediate reads/writes to storage
Best for
- Protecting enterprise document stores from autonomous agents by mediating agent access and preventing unauthorized reads of sensitive files.
- Providing persistent agent memory for multi-step workflows where agents need to store and recall contextual vectors across sessions.
- Enforcing runtime access policies for agents operating in regulated industries (finance, healthcare, legal) to maintain compliance and governance.
- Powering secure Retrieval-Augmented Generation (RAG) pipelines where agents retrieve vetted context vectors rather than raw documents.
- Isolating agent workspaces for research or development teams to experiment with agent behaviors without risking cross-project data leaks.
- Coordinating multi-agent systems by centralizing shared context and controlling which agents can access which pieces of memory.
- Prevent autonomous agents from reading or exfiltrating sensitive documents
- Store and manage agent context and memory vectors securely
- Enforce runtime policies for agentic workflows (who/when/how data is accessed)
- Isolate agent workspaces to reduce risk of data bleed between agents
- Provide a secure intermediary for LLMs and bots accessing corporate documents