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.
No tools found
We couldn't find any tools matching your current filters. Try adjusting your preferences or check back later.
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.