Building a reliable, scalable AI agent remains a major technical challenge: you have to juggle several language models, wire up a vector memory, orchestrate successive steps, monitor costs and deploy everything without drowning in infrastructure. Langbase sets out to solve this with a serverless platform built for developers. The core idea is simple: expose each building block of an agentic AI application as clean APIs, so you can prototype quickly and then deploy to production without managing servers. The stated promise comes down to three words: Build, Deploy, Scale. In concrete terms, Langbase brings together a unified API to more than 600 language models, a serverless RAG memory engine, a multi-agent workflow system and an observability studio. In this overview, we detail what the platform actually does, its named features, its concrete use cases, its advantages and its pricing tiers, so you can tell whether it fits your development needs.
What is Langbase?
Langbase is a serverless platform dedicated to building AI agents, aimed primarily at developers and technical teams. Rather than imposing a rigid framework, it breaks an AI application down into API-accessible components. The agents, called Pipes, rely on a unified API that provides access to more than 600 LLMs from various providers, which avoids rewriting your code every time you switch models. The Memory layer brings the RAG dimension: vector store, file storage and a retrieval engine to query documents, code repositories and datasets. On top of this come Workflows to orchestrate multiple steps and agents, as well as an AI Studio for design, versioning and evaluation. The whole thing runs in serverless mode, with no infrastructure to manage on the user’s side.
Key features
Langbase is built on several complementary modules. Pipes are agents exposed through a unified API compatible with more than 600 LLMs, with support for tools such as search, crawling and the MCP protocol (Model Context Protocol), and one-click serverless deployment. The Memory module turns RAG into an API: it combines a vector store, file storage and a retrieval engine, and lets you chat with documents, repositories or datasets while aiming for reliable, contextual answers. Workflows orchestrate multi-step, multi-agent processes, with timeouts, retries, backoff, scheduling and durable steps that can be traced step by step. The Ops & Evals side revolves around AI Studio, which is used to create, version and collaborate on agents, while offering full operation tracing with cost and usage forecasting. Finally, Command Code introduces a coding agent that learns the team’s preferences and is shared via npx commands. This modular architecture is billed piece by piece.
Use cases
The use cases Langbase covers revolve around agentic AI applications. A product team can expose a conversational agent backed by internal documentation thanks to the Memory module, to answer user questions with contextual sources. A developer can build an assistant able to query a code repository or a dataset to speed up technical support. Workflows are well suited to multi-step processing: extraction, then classification, then generation, with automatic retries on failure. Compatibility with more than 600 LLMs makes it easy to compare models and optimize the cost/quality ratio directly from AI Studio. SaaS startups find a way to industrialize AI features without managing servers, while large enterprises can target compliant deployments through the dedicated offering. Finally, built-in tracing helps teams monitor costs and consumption.
Advantages
Langbase’s main benefit is reducing the infrastructure complexity tied to AI agents. Thanks to serverless mode, teams focus on application logic rather than managing servers or vector databases. The unified API to more than 600 LLMs adds flexibility: switching models or comparing several doesn’t require rewriting the application. The built-in RAG memory avoids assembling a retrieval stack yourself, and durable workflows with retries strengthen reliability in production. AI Studio centralizes versioning, tracing and cost forecasting, which improves observability and collaboration. For organizations subject to regulatory requirements, the Custom offering covers certifications such as SOC 2 and HIPAA. Overall, the platform speeds up the move from prototype to production while keeping costs under control.
Pricing
Langbase offers four tiers. The Free plan, at $0/month, includes 500 credits, 500 agent runs, 5 public pipes, 5 MB of memory and community support, ideal for testing. The Individual plan, at $100/month, adds 20,000 credits, unlimited public pipes, 10 private pipes, 20 MB of memory and unlimited retrieval. The Growth plan, at $250/month, goes up to 75,000 credits, 30 private pipes, 50 MB of memory and 5 organization seats ($30 per additional seat). Finally, the Custom offering, on quote, targets enterprises with unlimited resources, SOC 2, HIPAA, GDPR, SAML/SSO and RBAC compliance, and dedicated engineers.
Conclusion
Langbase is a coherent, complete platform for anyone who wants to build AI agents in serverless mode, from prototype to production. Its unified API to more than 600 LLMs, its RAG memory, its durable workflows and its observability studio form a credible package for technical teams. The $100/month paid entry point, however, reserves it for serious projects with a suitable budget, with the free plan mainly serving for evaluation. For developers looking to industrialize agentic AI applications, it’s an option well worth a close look.