AI Computer Vision & Speech APIs
Add the ability to see, read, and listen to your applications — via APIs for image recognition, OCR, object detection, speech-to-text, and speaker identification. These are the building blocks behind AI apps that process documents, analyze photos, or transcribe audio at scale.
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AI Computer Vision & Speech APIs
Computer vision and speech APIs give your applications the ability to process images, video, and audio — capabilities that would have required significant ML expertise to build a few years ago. Now they're available as simple API calls from the major cloud providers and specialized platforms.
Common capabilities available via API
- Image classification and object detection — identifying what's in an image or locating specific objects within it.
- OCR (Optical Character Recognition) — extracting text from scanned documents, receipts, and forms.
- Speech-to-text — converting audio recordings or live speech into accurate transcripts.
- Speaker identification — distinguishing between different speakers in a multi-person recording.
Specialized vs. cloud provider APIs
Major cloud providers (AWS, Google, Azure) offer solid general-purpose vision and speech APIs. Specialized providers like AssemblyAI and Deepgram focus entirely on audio/speech and tend to offer more accurate transcription, better speaker diarization, and more granular controls for production use cases.
Also explore in AI Developer APIs & Platforms
AI Agent & Orchestration Frameworks
Build AI applications that do more than chat — agents that search the web, run code, query databases, call APIs, and hand off tasks between specialized sub-agents. These frameworks give you the building blocks for multi-step AI workflows without building the orchestration layer from scratch.
AI Cloud ML Platforms
Build, train, deploy, and monitor machine learning models on enterprise-grade cloud infrastructure from AWS, Google, Microsoft, and IBM. These platforms handle the heavy lifting of data management, model training at scale, and deployment pipelines — so your ML team focuses on the models, not the infrastructure.
AI LLM APIs (Foundation Models)
Access the world's most capable language models via API to power your product's AI features — from chatbots and content generation to complex reasoning and data extraction. These platforms handle the model infrastructure so you focus on building, not running GPU servers.
AI Model Hosting & Open-Source Model APIs
Run open-source models like Llama, Mistral, and Qwen at scale without managing your own GPU infrastructure — through APIs that feel familiar but give you access to open-weight models you can customize, fine-tune, or deploy under your own terms.
AI Vector Databases & RAG Infrastructure
Power semantic search and retrieval-augmented generation (RAG) apps with a database built for AI embeddings. Store and query millions of vectors fast — the infrastructure layer behind AI applications that need to search documents, memories, or knowledge bases by meaning, not just keywords.