Google APIs

274 APIs7,055 Endpoints22 Categories

API Catalog(17 of 274)

AI Platform Training & Prediction API

AI Platform Training & Prediction is Google Cloud's earlier-generation managed service for training custom machine learning models and serving online and batch predictions. The API exposes models, model versions, jobs, online predict and explain calls, hyperparameter trial measurement, and study management. Most teams new to Google Cloud now use the Vertex AI API for the same workloads, but AI Platform remains supported for existing pipelines.

29 endpoints

Cloud AutoML API

The Cloud AutoML API trains and serves custom machine learning models for image classification, object detection, text classification, translation, and tabular prediction without requiring the caller to write training code. It exposes operations to manage datasets, kick off training, deploy or undeploy models, and run online or batch predictions. Long-running operations track training and deployment progress, and the API integrates with Cloud Storage for dataset import and batch prediction results. Note that AutoML is in maintenance with Vertex AI as Google's go-forward platform.

25 endpoints

Cloud Document AI API

Google Cloud Document AI parses structured information from unstructured and semi-structured documents using Google's pretrained processors for forms, invoices, receipts, contracts, and identity documents. The API supports synchronous processing for single documents and batch processing for large volumes, with optional human-in-the-loop review for low-confidence extractions. Custom processors can be deployed, undeployed, evaluated, and trained against project-specific document types. Operations are scoped under projects, locations, and processors with full lifecycle management for processor versions.

20 endpoints

Cloud Natural Language API

The Cloud Natural Language API applies Google's pretrained NLP models to plain text and HTML documents and returns structured analysis: named entities, document and sentence-level sentiment, syntactic annotation, content classification into a 1000+ category taxonomy, and content moderation labels. It is callable on text up to 1 MB per request and supports multiple languages depending on the feature. The v2 endpoints replace the older analyzeEntitySentiment and analyzeSyntax features with a streamlined annotateText combination call.

5 endpoints

Cloud Speech-to-Text API

The Cloud Speech-to-Text API converts spoken audio into text using Google's speech recognition models. It supports synchronous recognition for short clips, long-running recognition for multi-minute audio passed by Cloud Storage URI, and adaptation through phrase sets and custom classes that bias the recogniser toward domain-specific vocabulary. Typical inputs are LINEAR16, FLAC, or OGG_OPUS audio with the language code declared on the request.

11 endpoints

Cloud Text-to-Speech API

Cloud Text-to-Speech synthesises natural-sounding speech from text or SSML using Google's neural network voices, including WaveNet, Neural2, and Studio voice tiers. It supports more than 50 languages and locales, dozens of voice variants per language, and SSML controls for pacing, pitch, pauses, and pronunciation. The synchronous synthesize endpoint returns audio for short text; the synthesizeLongAudio endpoint streams long-form audio (audiobooks, course content) to a Cloud Storage bucket via a long-running operation.

7 endpoints

Cloud Translation API

Cloud Translation v3 (Advanced) translates text and documents between more than 130 languages using Google's neural machine translation models. Beyond basic string translation it supports glossaries (consistent terminology), AutoML and adaptive custom models, batch translation jobs that read source files from Cloud Storage and write results back, language detection, and document translation that preserves layout for DOCX, PDF, PPTX, and XLSX files. The v3 surface also exposes datasets and model resources for training custom translation models on parallel corpora.

31 endpoints

Cloud Video Intelligence API

The Cloud Video Intelligence API analyzes video stored in Cloud Storage or supplied inline to detect objects, label content, identify shot changes, recognize on-screen text, transcribe speech, and flag explicit content. Annotation is performed asynchronously via long-running operations, with results returned per shot, per frame, or per segment depending on the requested feature. It powers content moderation, video search, and automated metadata pipelines.

8 endpoints

Cloud Vision API

The Cloud Vision API performs image and PDF analysis including label detection, OCR, face detection, landmark and logo recognition, explicit content (SafeSearch) detection, object localization, and product search. Requests can analyze images inline or stored in Cloud Storage and can be batched synchronously or run asynchronously for large PDF and TIFF documents. It powers content moderation, document digitization, and visual search workloads.

23 endpoints

Data Labeling API

The Google Cloud Data Labeling API exposes the public surface of the Data Labeling Service for AI training data preparation. It covers 28 endpoints for creating datasets, importing raw data, requesting human-labeled annotations across image, video, and text tasks, and exporting annotated datasets back to Cloud Storage. The service is designed for teams who need labeled corpora for supervised ML model training but do not want to run their own annotation pipeline.

28 endpoints

Dialogflow API

Google Dialogflow is a conversational AI platform for building chatbots, voice bots, and IVR experiences. The CX (v3) API exposes agents, flows, pages, intents, entity types, webhooks, and test cases so teams can author, version, and continuously evaluate sophisticated multi-turn conversations programmatically. It powers contact-center automation, virtual agents, and voice assistants integrated with telephony providers and websites.

63 endpoints

Discovery Engine API

Google Discovery Engine (the API behind Vertex AI Search and Vertex AI Conversation) lets developers build retrieval, search, and grounded conversational experiences over their own structured and unstructured data. The API exposes data stores, schemas, documents, completion configs, and grounding/check operations so teams can run semantic search, recommendations, and RAG-grounded chat against enterprise content. It supports custom model training, evaluation, and identity-mapped retrieval.

75 endpoints

Firebase ML API

The Firebase ML API provides programmatic access to long-running operations spawned by Firebase ML model management workflows, such as uploading and publishing custom machine learning models for use by Firebase mobile apps. The v1 surface exposes operations to list, cancel, and delete those operations. Use it to monitor model upload pipelines and manage cleanup of operation history during model publishing automation.

3 endpoints

Notebooks API

The Notebooks API manages Vertex AI Workbench managed notebook instances on Google Cloud. It provisions, starts, stops, upgrades, resets, and diagnoses Jupyter-based development environments backed by Compute Engine VMs with pre-installed ML frameworks. Use it to spin up GPU/TPU-equipped notebook VMs for data scientists, restore from backup, rollback to prior states, and pull diagnostic bundles when an instance misbehaves. The spec exposes 23 endpoints across regional locations.

23 endpoints

Perspective Comment Analyzer API

The Perspective Comment Analyzer API scores user-generated text on attributes such as toxicity, severe toxicity, insult, profanity, and threat. Built by Jigsaw and Google's Counter Abuse Technology team, it powers comment moderation for newsrooms, forums, and chat apps. Callers submit a comment and receive per-attribute probability scores and span-level annotations, plus a suggestscore endpoint to send labels back into the model's training pipeline.

2 endpoints

Recommendations AI (Beta)

Recommendations AI (Beta) is Google Cloud's managed personalization service for predicting catalog items a user is likely to engage with next. The API exposes operations to manage catalogs and catalog items, ingest user events, register prediction API keys, and call serving endpoints that return ranked recommendations. Google strongly recommends new customers use the Retail API, which incorporates the GA version of these capabilities; this beta API remains for existing integrations.

17 endpoints

Vertex AI API

The Vertex AI API is Google Cloud's unified surface for training, tuning, deploying, and serving machine learning models, including Google's foundation models such as Gemini and PaLM and customer-trained models. It exposes operations on datasets, training pipelines, models, endpoints, batch prediction jobs, feature stores, indexes, and model lineage. With 202 endpoints, it covers the full MLOps lifecycle from data ingestion through online and batch inference. It is the right tool for teams building production ML systems on Google Cloud rather than just calling a hosted LLM endpoint.

202 endpoints

Connect Your Agent

Step 1: Jentic One Host machine

# On the machine that will host your Jentic One instance:
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh

Step 2: Agent machine

# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh
jentic register       # connects your agent to your Jentic One instance

Jentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.