Use Cases¶

Discover how to integrate stdapi.ai with popular AI applications and tools. stdapi.ai's OpenAI, Anthropic, and Cohere-compatible APIs are already spoken by hundreds of applications and tools, and adopting it takes three client-side changes: the base URL, the API key, and — where the name differs from what the client already sends — the model name, now picked from every provider in the catalogue rather than one vendor's list.

Why use stdapi.ai for integrations?

• Three client-side changes - Point your application at the new base URL and API key, and update the model name if the one it already sends isn't in this deployment's catalogue
• Access 100+ models - Claude, OpenAI GPT, xAI Grok, Kimi, DeepSeek, Qwen, GLM, Nova, Llama, Stability AI, and more
• Enterprise data control - The gateway runs in your own AWS account — no third party sits between your users and your models
• Pay-per-use pricing - Pay Amazon Bedrock rates for actual usage, with no markup and no per-seat fees
• AWS-native features - Leverage prompt caching, reasoning modes, and guardrails through standard OpenAI, Anthropic, and Cohere APIs
• Three-dialect API compatibility - Use the OpenAI, Anthropic, or Cohere SDK with the same deployment

How Integration Works¶

Every integration on this page follows the same four steps, and that's the whole process:

1. Deploy stdapi.ai — on AWS or locally with Docker
2. Copy your endpoint URL and API key
3. Paste them into your tool's AI-provider settings (or your SDK's base_url)
4. Name a model this deployment serves — the Models page shows them and GET /search_models lists what your deployment actually serves; a name the catalogue does not hold returns 404 rather than a lookalike

from openai import OpenAI

client = OpenAI(base_url="https://your-endpoint/v1", api_key="YOUR_KEY")
# Then name a model from the catalogue — the rest of your application is unchanged

Choose Your Integration¶

Pick the category that matches your goal — categories marked have a dedicated step-by-step guide.

Throughout this page, tools in bold are driven end to end by an automated test suite against a real deployment; the others have a documented setup.

Developer Tools — AI Coding Assistants¶

For teams that want frontier coding models in their IDEs and terminals — without sending code to third-party AI clouds.

Enhance your development workflow with AI-powered coding assistants. stdapi.ai integrates with popular IDEs and AI development frameworks, allowing you to leverage Amazon Bedrock models (Claude, Kimi thinking, Qwen3 Coder Next) for code completion, generation, and intelligent assistance.

What you can do:

• Code completion - Real-time suggestions as you type in VS Code, JetBrains IDEs
• Code generation - Natural language to code with Claude and specialized coding models
• Codebase understanding - Chat with your codebase, explain functions, refactor code

Popular tools: Claude Code, Codex, Qwen Code, pi, Cline, OpenCode, Zed, JetBrains AI Assistant

AI Coding Assistants Guide — Universal setup for IDEs and development frameworks

Autonomous Agents — Research & Task Automation¶

For builders of agents that must run on infrastructure you control.

Build self-directed AI agents that can plan, execute, and refine complex tasks autonomously. Integrate stdapi.ai with agent frameworks to create intelligent systems powered by Amazon Bedrock that can conduct research, automate workflows, and solve multi-step problems.

What you can build:

• Personal AI assistants - Autonomous agents connected to messaging, email, and smart home
• Research agents - Autonomous web research, data gathering, and analysis
• Multi-agent systems - Collaborative agents for complex problem-solving
• Task automation - Self-improving workflows that adapt to results
• Code agents - Autonomous development and testing systems
• Grounded agents - Retrieval the agent calls for itself, over vector stores holding your own documents
• Resumable sessions - Threads kept server-side with the Conversations API and continued by id, rather than resent every turn
• Per-caller identity - Agents that discover how to authenticate and whose spend is reported per end user

Compatible frameworks: OpenClaw, Hermes, LangChain, Pydantic AI, OpenAI Agents SDK, Agno, LlamaIndex, LiteLLM, LangGraph, CrewAI, Strands Agents

All agent frameworks that support OpenAI or Anthropic SDKs work immediately — point the SDK's base URL to stdapi.ai. See the API overview for connection details.

Python Client Libraries Guide — Configuring LangChain, pydantic-ai and the OpenAI Agents SDK directly against stdapi.ai

Autonomous Agent CLIs Guide — Configuring Hermes and OpenClaw directly against stdapi.ai

Chat Interfaces — Private ChatGPT Alternative¶

For organizations replacing per-seat AI subscriptions with a private, pay-per-use assistant.

Build ChatGPT-like experiences with Amazon Bedrock models and complete privacy control. Deploy feature-rich web interfaces that provide familiar chat experiences while keeping all data within your AWS environment.

What you can build:

• Private team chat - ChatGPT-style interface for your organization
• Customer support assistant - AI-powered help desk with your data
• Internal knowledge base - RAG-enabled chat with document search
• Multi-modal applications - Process text, voice, images, and documents
• Voice chat & image generation - Speech input/output and in-chat image creation through the same endpoint

Popular tools: Open WebUI, LobeHub, AnythingLLM, LibreChat

Open WebUI Integration Guide — Complete setup with Terraform deployment examples

LobeHub Integration Guide — Complete setup with Terraform deployment examples

Workflow Automation — AI-Powered Business Processes¶

For teams adding AI steps to business processes without writing code.

Integrate Amazon Bedrock AI into your business processes and automation workflows. Connect models to hundreds of services and APIs through visual workflow builders, enabling sophisticated AI-powered automation without writing code.

What you can automate:

• Customer support - Auto-classify tickets, generate responses, route intelligently
• Content creation - Automated blog posts, social media, email campaigns
• Data processing - Extract, transform, and analyze data with AI
• Document workflows - Automated summarization, translation, and classification
• Bulk runs - Push a backlog through the Batch API asynchronously, at the Amazon Bedrock batch price
• Content safety - Screen user-generated content with the Moderations API

Popular tools: n8n, Haystack, Langflow, Dify, Flowise

n8n Integration Guide — Complete setup for AI workflow automation

Voice & Audio — Speech Applications & Voice Agents¶

For products that need speech in and speech out — without adding a second AI vendor.

Build voice-first applications on the same OpenAI-compatible endpoint: text-to-speech with Amazon Polly voices, speech-to-text with Amazon Transcribe and Bedrock audio models (including streaming and speaker diarization), and speech translation with subtitle output.

What you can build:

• Speech-to-speech agents - Hold a spoken conversation over one WebSocket with the Realtime API — the model handles turn taking and interruption, and a live transcript comes back with the audio
• Voice agents - Real-time conversational agents for phone, web, and support lines
• Meeting intelligence - Transcription with speaker diarization and AI summaries
• Live transcription - Return each phrase as it is recognized instead of after the whole recording, with streamed transcriptions
• Subtitles & dubbing - Transcribe and translate audio with SRT/VTT subtitle output
• Long-form narration - Speak up to 100,000 characters per request, streamed as it is synthesized
• Voice interfaces - Add speech input/output to chat interfaces and internal tools

Popular frameworks: Pipecat, LiveKit Agents, TEN Framework — all accept a custom OpenAI-compatible base URL for LLM, speech-to-text, and text-to-speech services, and the first two are also what put WebRTC or a phone line in front of a realtime session

Popular tools: Home Assistant Assist (via the wyoming-openai proxy)

Home Assistant Voice Guide — Complete setup for local voice assistants backed by Amazon Transcribe and Amazon Polly

RAG & Semantic Search — Embeddings and Reranking¶

For teams grounding AI answers in their own documents and data.

Build retrieval-augmented generation and semantic search pipelines with Bedrock embedding models and Cohere-compatible reranking — two-stage retrieval (embed, then rerank) through one deployment.

What you can build:

• Managed retrieval - Attach files to a vector store and search it by meaning, with no chunker, embedder, or vector database of your own to run
• Retrieval the model runs itself - Name a store as a file_search tool and the model searches it mid-answer, citing the files it drew on
• Your existing knowledge base - Address an Amazon Bedrock knowledge base you already operate as a vector store — searched and extended, never recreated
• Document ingestion - Parse PDFs and office documents into Markdown with Docling before embedding
• RAG pipelines - Ground model answers in your documents with embeddings
• Two-stage retrieval - Improve relevance with the Rerank API on top of vector search
• Semantic search - Search by meaning across documents, tickets, and knowledge bases
• Multimodal search - Embed text and images with models like Cohere Embed v4

Popular tools: Docling Serve for document parsing, Haystack, Agno, LlamaIndex, RAGFlow, LightRAG for retrieval — the managed stores need no vector database, and an assembled pipeline works with any of them (pgvector, Qdrant, and others store the vectors; stdapi.ai serves the embeddings)

RAG Pipelines Guide — Configuring document parsing, embeddings, Cohere-compatible reranking, and generation together

RAGFlow Integration Guide — Complete setup with Terraform deployment examples

Content & Media Generation — Images and Video¶

For creative and marketing pipelines that generate visuals at scale.

Generate and edit visual content with Amazon Bedrock media models through the standard OpenAI Images and Videos APIs — from marketing assets to fully automated content pipelines.

What you can build:

• Image generation - Text-to-image with Amazon Nova Canvas and Stability AI models via Images Generations
• Image editing - Inpainting, outpainting, and style transfer via Images Edits
• Video generation - Asynchronous text/image-to-video with Amazon Nova Reel and Luma Ray via the Videos API
• Safe publishing pipelines - Combine generation with the Moderations API for automated content review

Popular tools: Open WebUI (built-in image generation), n8n media workflows, or the APIs directly

Knowledge Management — AI-Enhanced Notes & Research¶

For individuals and teams adding private AI to their notes and research.

Transform your knowledge base with AI-powered insights and generation. Integrate stdapi.ai with note-taking applications to add semantic search, writing assistance, and intelligent content organization.

What you can do:

• AI writing assistance - Generate, edit, and improve your writing
• Semantic search - Find notes by meaning, not just keywords
• Auto-summarization - Extract key points from long documents
• Smart organization - Automatic tagging, linking, and categorization

Compatible tools: Obsidian (Copilot plugin), Khoj (self-hosted), SiYuan

Team Chatbots & Assistants — Slack, Discord, Teams Integration¶

For support and ops teams meeting users where they already chat.

Deploy intelligent AI assistants to your team's communication platforms powered by Amazon Bedrock models.

What you can build:

• Team Q&A bot - Answer common questions in Slack or Teams
• Documentation assistant - Search and cite internal docs in real-time
• Task automation - Create tickets, schedule meetings, update databases via chat
• Moderated channels - Screen messages with the Moderations API

Compatible platforms: Dify, Chatwoot (Captain, self-hosted), Typebot — or build directly for Slack, Discord, and Microsoft Teams with the OpenAI or Anthropic SDK

Common Questions¶

• Where does my data go? The gateway runs in your own AWS account, so no third party sits between your users and your models: inference runs on the AWS services and regions you enable, and Amazon Bedrock does not share prompts with model providers or use them for training. Data sovereignty & compliance →
• What does it cost? $0.10/container-hour for the gateway — the Terraform module runs one container per Availability Zone by default — plus Amazon Bedrock rates, with no markup and no per-seat fees. Each end user's share can be reported separately in Cost Explorer, from the invoice rather than an estimate. Licensing & pricing → · Cost management →
• Am I locked in? No — stdapi.ai speaks the standard OpenAI, Anthropic, and Cohere APIs. Leaving is the same client-side change that got you in.

Ready to Get Started?¶