What makes Cognitora different from traditional cloud platforms like AWS or GCP?
Cognitora is designed exclusively for AI agents, offering high-performance infrastructure with sub-second provisioning using Firecracker microVMs, millisecond-precision billing, and agent-native APIs. It eliminates complex infrastructure management, providing optimal resource allocation specifically for AI workloads, unlike general-purpose cloud providers.
What SDKs and programming languages does Cognitora support for integration?
Cognitora provides professional-grade SDKs for Python and JavaScript/TypeScript, complete with async support, error handling, and automatic retries. It also supports REST API, gRPC, WebSocket, A2A protocols, and MCP (Model Context Protocol) for seamless integration with various AI frameworks and tools.
What are the technical specifications and security measures of Cognitora's microVMs?
Cognitora's microVMs are built on Cloud Hypervisor with Kata Containers for hardware-level isolation, booting in under 150ms. They support configurable CPU (1-16 cores), memory (1GB-32GB), and persistent storage. Security includes AES-256 encryption, zero-trust architecture, SOC 2 Type II and ISO 27001 compliance, and isolated agent environments.
How does Cognitora integrate with popular AI frameworks like LangChain and AutoGPT?
Cognitora provides native tools and plugins for LangChain, AutoGPT, and CrewAI. Its LangChain integration includes secure code execution tools, document processing utilities, and multi-agent coordination primitives. Framework-specific SDKs automatically handle authentication, resource management, and error handling for seamless workflow integration.
What are the pricing options for using Cognitora's AI agent compute platform?
Cognitora offers a Freemium model starting with a free Hobby tier that includes $50 in usage credits. Paid plans include Developer ($50/month) and Pro ($150/month), offering increased concurrent executions, custom resource allocation, and advanced features. Enterprise solutions are available for large-scale deployments with custom requirements.