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About Sidenet
Sidenet provides infrastructure for software platforms to ship, scale and monetize in-product AI agents without building the underlying stack. It handles the operational layers between a product and AI models, including agents, workflows, memory, evaluations, traces, costs, guardrails and model selection. Teams configure agents in Studio using prompt variables, memory modes, guardrails and versioned releases without needing to learn a framework or host infrastructure. Tools can be added via OpenAPI specs or MCP servers, with access to over 1,000 integrations. The platform supports 605 models from 60+ providers, filtered by region, retention and training policy, with EU-hosted and zero-retention options available. Workflows automate known processes such as repricing or reporting, while networks allow a primary assistant to route requests to specialist agents. The system is embedded directly into a product’s API with the same permissions, enabling users to ask, act, schedule and automate tasks within the application.
Key features
- Agent configuration in Studio with prompt variables and guardrails
- Over 1,000 integrations via OpenAPI or MCP
- 605 models from 60+ providers with EU-hosted options
- Workflows for automated processes like repricing and reporting
- Networks for routing requests to specialist agents
- Memory modes and versioned releases for agent management
- Traces and evaluations for monitoring and optimization
- Cost tracking per customer, agent and model
Use cases
- Embedding AI assistants in hospitality PMS platforms
- Automating group sales demand scoring for hotel platforms
- Building AI-powered workflows for hotel operations and reporting
Pros
- Handles operational layers between product and AI models
- Supports 605 models from 60+ providers with EU-hosted and zero-retention options
- Embeds agents directly into existing product APIs with same permissions
- Includes workflows, guardrails, memory and evaluations out of the box
- Allows configuration of agents, networks and workflows in Studio without hosting
Cons
- No free tier beyond initial credits
- Pro tier required for white-glove onboarding and advanced monetization
- Limited to vertical SaaS use cases
- Requires API or MCP connection to integrate tools
Frequently asked questions about Sidenet
What is Sidenet and what does it do?
Sidenet provides infrastructure for software platforms to launch, scale, and monetize in-product AI agents without building the underlying stack. It handles operational layers such as agents, workflows, memory, evaluations, traces, costs, guardrails, and model selection, allowing teams to focus on user experience rather than infrastructure.
Who is Sidenet designed for?
Sidenet is designed for vertical SaaS companies and software platforms that want to embed AI agents directly into their products. It suits teams that need to ship AI capabilities quickly without hiring an AI team or managing infrastructure.
How does Sidenet integrate with existing products?
Sidenet integrates directly into a product’s API with the same permissions, enabling users to ask, act, schedule, and automate tasks within the application. It supports OpenAPI specs, MCP servers, and over 1,000 integrations, with authentication handled seamlessly.
What models and providers does Sidenet support?
Sidenet supports 605 models from 60+ providers, including OpenAI, Anthropic, Gemini, Mistral, Meta, Cohere, Grok, Groq, DeepSeek, and Qwen. Users can filter models by region, retention, and training policy, with EU-hosted and zero-retention options available.
Can Sidenet handle workflows and automation?
Yes, Sidenet supports workflows that automate known processes such as repricing, reporting, or reconciliation. These workflows can run on a schedule, via webhooks, or when triggered by an agent, delivering results directly to users without additional tasks.
How does Sidenet help with agent operations and monitoring?
Sidenet provides traces to show why an agent gave a specific answer, including model calls, memory, and tool usage, each timed and priced. It also offers evaluations to test prompt or model changes against golden sets, ensuring improvements before deployment.
