SandBase

Company profile:

Startup name: SandBase

Tagline: One API for AI models & sandboxes

Elevator Pitch: SandBase gives developers one account and one API key for models, data APIs, sandboxes, and agents — OpenAI-compatible Agents API, E2B-compatible sandboxes, and unified billing.

One API for AI models & sandboxes. Freemium developer tools for AI builders who want models, sandboxes, and agents without juggling multiple vendors.

Target Market: AI / Developer tools / SaaS — developers building AI apps, agents, and tooling who need models, sandboxes, and APIs in one place.

How will you make money?: Freemium. Free tier for developers to try models, sandboxes, and agents; paid plans and usage-based billing for higher limits, teams, and production workloads with unified billing across the API.

How much capital have you raised?: None

Website: https://www.sandbase.ai

City/Country: Singapore

AI-assisted summary:

SandBase is a developer platform that combines AI models, real-world data APIs, reusable agents, and code sandboxes behind one account and API key. Its proposition matters because teams building agentic software often need to coordinate model access, external tools, execution environments, and operational workflows rather than simply call a single language model. (Source: https://www.sandbase.ai/)

The problem and target users

SandBase is aimed at AI-tool users, application builders, and field-deployment or product teams that need reusable agents for customer-facing or internal workflows. The company specifically identifies users of tools such as Claude Code, Codex, Cursor, and Kiro; developers calling models and APIs directly; and teams creating reusable agents. (Source: https://www.sandbase.ai/docs/faq)

The underlying problem is integration complexity: an AI application may require model inference, access to external data or actions, multi-step orchestration, code execution, and a way to inspect costs and outcomes. SandBase positions itself as a single platform for models, APIs, agents, and sandboxed execution rather than requiring developers to assemble separate credentials and interfaces for each component. (Source: https://www.sandbase.ai/)

This positioning is most relevant to builders moving beyond simple chat interfaces into workflows where an agent must retrieve information, call tools, generate or manipulate files, execute code, and return a usable result. SandBase’s own example describes an agent that searches public X posts, removes duplicates in a sandbox, and uses a language model to prepare a sourced research brief. (Source: https://www.sandbase.ai/)

Product and solution

SandBase offers direct access to language, image, video, audio, and third-party APIs, alongside managed agents that can be configured as reusable and versioned workflows. In the managed-agent path, developers define an agent’s model, instructions, skills, and MCP connections, then run it through persistent sessions. (Source: https://www.sandbase.ai/docs/)

• Its API is designed to let developers use one key across models, APIs, and agents, while the public site advertises access to more than 2,000 models and APIs across more than 100 platforms. (Source: https://www.sandbase.ai/)
• SandBase supports an OpenAI-compatible chat-completions interface and documents an agent workflow API based on sessions, which retain an agent execution’s conversation, tool activity, and output. (Source: https://www.sandbase.ai/docs/for-agents/full)
• Its sandbox product provides isolated environments for executing commands and reading or writing files, with an E2B-compatible SDK integration. (Source: https://www.sandbase.ai/docs/sandbox/)
• The company also publishes open-source projects, including agent runtimes, MCP tooling, a CLI, and installable agent skills, suggesting that developer adoption and compatibility are important parts of its distribution strategy. (Source: https://github.com/sandbaseai/)

SandBase’s documentation makes an important distinction between direct model/API calls and managed agents: developers retain responsibility for prompts, orchestration, retries, and application state when using direct calls, while managed agents are intended for reusable, multi-step, asynchronous, or repeatable work. (Source: https://www.sandbase.ai/docs/)

Business model, pricing signals, and traction

SandBase uses a usage-based model rather than a mandatory subscription for its entry-level offering: its pricing page describes free starter credits, pay-as-you-go charges for underlying model, API, and runtime work, and team or enterprise options for production organizations. (Source: https://www.sandbase.ai/pricing)

The public site also states that eligible data APIs include an organization-level allowance of 100 free calls per day, while calls beyond that allowance are billed at listed prices and model usage is charged separately. (Source: https://www.sandbase.ai/)

For buyers, the useful signal is that pricing is attached to actual components consumed—such as model tokens, media generation, API calls, and agent-runtime work—rather than a single flat “agent” price. SandBase says its console provides usage history and records costs for activity runs. (Source: https://www.sandbase.ai/pricing)

Expert take

SandBase is positioning itself as an agent-infrastructure consolidation play: its potential strength is reducing the number of platforms a developer must integrate when building tool-using AI software. The main challenge will be proving that its unified layer remains reliable, transparent, and competitively priced as model providers, agent frameworks, MCP ecosystems, and sandbox vendors evolve quickly.

Note: Information based on publicly available sources at the time of writing, and summarized by AI.