Novita

Novita is a scalable AI cloud platform offering over 200 powerful APIs for LLMs, image/video generation, speech, and more. With serverless GPU infrastructure, seamless model deployment, and Hugging Face integration, Novita makes advanced AI accessible and affordable for developers and enterprises.

General
Release date	2023
Author	Novita
Type	Multimodal AI Cloud Platform

Novita - APIs & Services

Explore Novita’s wide range of ready-to-use APIs for building AI applications

All Models Over 200 pre-integrated APIs including text-to-image, speech synthesis, and video generation
Quickstart Guide Get started with Novita in minutes
API Docs Full documentation for integrating APIs
Serverless GPU Inference Scalable, pay-as-you-go inference infrastructure

Novita - Integrations

Tools and integrations to supercharge your workflow

Novita on Hugging Face Use Novita as your inference backend directly from Hugging Face

Novita - Tutorials & Demos

Written guides and demos to help you get started

How to Use Novita APIs End-to-end guide to integrating Novita APIs
Deploy Your Custom Model Scale your custom models with Novita support
Video-to-Image & Diffusion APIs Visual AI tools with just an API call

Novita - Key Resources

Important links to explore the platform

Homepage Start here
GPU Pricing Compare pricing for A100, RTX 4090, L40S and more
Referral Program Invite friends and earn up to $500 in credits
Blog & Updates News, feature drops, and tutorials

Fresno LUX: A Living City

Fresno LUX is a prototype for a living intelligence layer across an entire city. Fresno already contains enormous amounts of intelligence: residents know where problems exist, nonprofits know where help is needed, businesses know where opportunities exist, educators understand gaps in learning, researchers generate knowledge, and government maintains critical infrastructure and data. The problem is that much of this intelligence is fragmented across disconnected systems. Fresno LUX connects those pieces. The platform creates a shared city graph where people, organizations, resources, needs, projects, observations, experiments, and outcomes become connected nodes. Instead of simply collecting data, LUX continuously asks: What is happening? What do we know? What is missing? What can we test? What changed? A community problem can become an observable signal. That signal can become an experiment. The experiment produces evidence. Evidence changes the system's understanding. That updated understanding informs the next action. Observe → Connect → Understand → Act → Measure → Learn → Converge. The goal is to move Fresno from disconnected information toward collective intelligence—a system where knowledge can travel across boundaries and successful solutions can be discovered, tested, measured, and replicated. LUX is designed to make the invisible connections visible: the relationship between a neighborhood and its resources, between a student and educational opportunity, between a business and workforce capacity, between environmental conditions and community health, and between an idea and measurable real-world impact. This hackathon prototype demonstrates the beginning of a Living Fresno Graph: a city that can observe itself, learn from evidence, and coordinate toward better outcomes.

TruthTone: One Truth, Four Voices

TruthTone is an evidence-grounded video captioning studio built for the AMD Developer Hackathon Track 2 challenge. Instead of asking one model to understand a video and produce a joke in a single prompt, TruthTone separates facts from tone. The application first samples important moments from the full video timeline, combining evenly spaced frames with scene-change detection. These timestamped frames are analyzed through Fireworks AI to create a Temporal Truth Spine: the visible subjects, ordered events, visible text, evidence frame references, uncertainty, and the central event of the clip. Next, TruthTone generates three caption candidates for each required style: formal, sarcastic, humorous-tech, and humorous-non-tech. A second AI verification stage compares all candidates against the Truth Spine and scores factual consistency, temporal order, tone match, main-event coverage, and clarity. Captions that introduce unsupported people, dialogue, causes, objects, or outcomes are rejected or repaired before being shown. The result is not simply four prompts. It is one shared evidence system with four distinct voices. The interface makes the process visible through an event timeline, evidence references, quality scores, candidate rejections, repair notes, latency, and token usage. The original video remains in the browser; only selected timestamped frames are sent for analysis. TruthTone proves that tone can change, but truth cannot.

CloudSense AI

CloudSense AI is a hackathon-built multi-cloud FinOps platform that tackles one of enterprise IT's biggest pain points — cloud waste. Organizations today lose 20–35% of their cloud budget to idle resources, over-provisioned infrastructure, and billing anomalies they can't easily detect. CloudSense AI solves this by unifying AWS, Azure, and GCP into a single intelligent platform powered by an AI chat assistant that answers plain-English questions like "Why is our cloud bill high?" — no technical expertise required. The platform features dedicated smart dashboards per cloud provider, automatic waste detection across idle resources and billing spikes, and security compliance checks. Its four-layer architecture — Cloud Provider Configuration → Cloud APIs & Metrics → AI Processing Engine → AI Chat + Dashboards — makes it simple to deploy and scale. CloudSense AI is built for everyone: non-technical users get plain-language insights, finance teams get budget visibility, leadership gets cost governance, and DevOps teams get infrastructure optimization. Future roadmap includes predictive cost forecasting, autonomous resource optimization, and auto-shutdown of idle instances — making it a complete enterprise FinOps intelligence platform.

AI-driven Team Formation

When a company gets a big project with a tight deadline, they face several challenges: Problem: - Time pressure: Not enough time to manually assess every employee's suitability. - Talent blind spots: Hidden or underutilized talents go unnoticed. - Bias and inconsistency in role assignments. - Cross-team collaboration gaps (who's available, who’s overloaded, etc.) - Need for immediate team readiness without the luxury of trial and error. My Tool Fixes This By: - Rapidly analyzing internal employee data (CVs, project history, feedback). - Auto-matching people to project roles based on real evidence, not just titles. - Providing explanations: Why this person fits, what gaps they might have, how to cover them. - Suggesting team composition instantly → making project kickoff much faster and smarter. I am proposing a role-fit AI engine that: - Works under tight timelines - Analyzes holistic data - Suggests ready-to-go teams - Justifies its decisions with explainable AI reasoning