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Baseplate

Teams use Baseplate to power their LLM Apps. With Baseplate, you can build scalable backend systems that allow LLMs like GPT-4 to work with your data, without ever being trained on your private information.

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Founded

2023

Location

San Francisco, CA

Funding

$15M Series A (2025)

Baseplate: Deploy, Run, and Sell Custom AI Agents

Baseplate is a developer platform to build, host, and commercialize AI agents—evolving from a “Heroku for LLM apps” into an agent‑centric stack. It provides a production‑ready backend with multimodal context storage, hybrid search, prompt and endpoint management, and hosting so teams can ship agent‑powered apps faster.

  • Website: [baseplate.ai](https://baseplate.ai)
  • Docs: [docs.baseplate.ai](https://docs.baseplate.ai)
  • Tagline: Deploy, run, and sell custom AI agents
  • HQ: San Francisco, CA • YC Winter 2023
  • Ideal for: Developers, startups, and product teams building LLM apps and agent experiences
  • What Baseplate Does

    Baseplate gives teams a managed backend to build reliable, scalable AI agents without DIY infrastructure.

  • Multimodal context database with hybrid search across text, code, docs, images, and links
  • Prompt and endpoint management to move from prototype to production
  • Retrieval‑augmented generation (RAG) via datasets, embeddings, and semantic/hybrid search
  • Fast iteration in a hosted Playground with LLM‑style parameters
  • Key Capabilities

  • Multimodal data ingestion
  • Push files up to 100MB, auto‑chunking, embeddings, and metadata
  • Hybrid search (semantic + keyword) over text, code, files, images, and links
  • Agent orchestration
  • Attach datasets and retrieval to prompts/endpoints
  • Move from Playground testing to deployed endpoints seamlessly
  • Hosted endpoints
  • Deploy a managed LLM backend and call it from your app or service
  • OpenAI‑style model parameters supported in Playground/Completions
  • Team features
  • Organizations and teammates for collaborative development
  • How It Works

    1. Ingest knowledge

  • Upload documents, code, or files; Baseplate chunks and embeds them .
  • 2. Configure retrieval

  • Use semantic or hybrid search to ground agent responses .
  • 3. Design prompts and agents

  • Build in the [Playground](https://docs.baseplate.ai/fancy-stuff/playground) and wire up datasets to prompts.
  • 4. Deploy endpoints

  • Turn your prototype into a production endpoint with versioned configs .
  • 5. Launch and sell

  • The homepage positions agents as a product you can ship to users; commercialization features are implied but not yet documented publicly.
  • Popular Use Cases

  • Customer support assistants grounded in your KB and ticket data
  • AI teaching assistants for course content
  • Internal knowledge hubs and dev Q&A over docs, code, and wiki
  • Proposal and contract drafting help
  • E‑commerce buying guides
  • Explore patterns in [Use Cases](https://docs.baseplate.ai/use-cases)
  • Who It’s For

  • Teams shipping agent‑powered apps and internal tools
  • Startups that want hosted infra for RAG, hybrid search, and agent orchestration
  • PM/engineering groups launching agents to customers without bespoke infra
  • Pros and Cons (Based on Public Sentiment)

    Pros

  • Fast path from prototype to production with clear endpoint workflow
  • Strong “Heroku for LLM apps” positioning focused on reliability and quick deploys
  • Practical RAG via multimodal context and hybrid search in one place
  • Cons

  • Limited third‑party reviews; no verified G2/Capterra listings yet
  • Pricing details have shifted as the product evolved; treat as “contact sales” until confirmed
  • Smaller ecosystem/community versus larger frameworks and hyperscale clouds
  • Differentiators and Competitive Frame

  • Differentiates as a hosted platform with built‑in data ingestion, hybrid search, and endpoint hosting—more than a code library.
  • Trades some flexibility for speed, sensible defaults, and managed reliability.
  • Alternatives: OpenAI Assistants/Responses API, LangChain, LlamaIndex, Vellum, Relevance AI, Vercel AI SDK, Superagent, Fixie, Voiceflow.
  • Integrations and Architecture Hints

  • Models and APIs
  • OpenAI‑style parameters in Playground and Completions
  • Data and retrieval
  • Datasets with auto‑chunking and embeddings; semantic/hybrid search
  • Deployment
  • Endpoint creation and app integration
  • Collaboration
  • Orgs and teammates
  • Pricing and Trial

  • Current public pricing is not clearly listed. Earlier posts referenced Pro/Team plans that may be outdated .
  • Several directories suggest a free trial, but they are not authoritative. Treat pricing as “contact sales” pending confirmation.
  • Notable Customers

  • None publicly listed at this time.
  • Company Snapshot

  • Company: Baseplate
  • Website: [baseplate.ai](https://baseplate.ai)
  • Docs: [docs.baseplate.ai](https://docs.baseplate.ai)
  • HQ: San Francisco, CA • YC Winter 2023
  • LinkedIn: [BaseplateAI](https://www.linkedin.com/company/baseplateai)
  • Employee range: 2–10 (LinkedIn)
  • Focus: Agents + backend for LLM apps, hybrid search, hosting
  • Target users: Developers, startups, product teams
  • Getting Started

  • Read the [Docs Overview](https://docs.baseplate.ai)
  • Prototype in the [Playground](https://docs.baseplate.ai/fancy-stuff/playground)
  • Build and deploy with [Create an Endpoint](https://docs.baseplate.ai/getting-started/create-an-endpoint)
  • Explore RAG patterns in [Use Cases](https://docs.baseplate.ai/use-cases)
  • Notes on data quality: Public reviews are limited; messaging now emphasizes agents, while older posts reflect the earlier LLM backend positioning. Sources include the [Website](https://baseplate.ai), [Docs](https://docs.baseplate.ai), and the [HN launch thread](https://news.ycombinator.com/item?id=35375727).