- AI Developers & ML Engineers: Those building applications that require LLMs or multimodal models and need reliable inference infrastructure.
- Enterprises & Startups Scaling AI: Organizations that want to deploy custom models or scale usage without owning all hardware.
- Research Teams: For prototyping or experimenting with many models and large architectures.
- Product Teams Using Generative AI: Apps using text, image, or video generation, or those using embedding/ranking or speech-to-text etc.
- Firms Concerned with Privacy & Security: Businesses that want to bring their own cloud, isolate data, or maintain strong control over deployment.
- Companies Watching Costs Closely: Those looking for cost-effective inference, scaling, and fine-tuning without massive waste.
How to Use It?
- Sign Up / Create an Account: Register and log in.
- Browse Models & Details: View what models are available (LLMs, image, video, embeddings etc.), see speed, limits, pricing.
- Try in Playground: Use the platform’s playground or model-experience center to send prompts, generate outputs etc.
- Get API Key: Create an API key for programmatic access.
- Call APIs or Fine-Tune: Use REST or OpenAI-compatible APIs for inference; fine-tune models on your data if needed.
- Deploy & Scale: Choose deployment mode (serverless, reserved GPU, private setup), monitor performance and cost.
- Wide Model Variety: Supports many large models across domains — language, image, video, speech etc.
- Inference Acceleration: Low latency, high throughput, optimized engine so models run efficiently.
- Flexible Deployment Options: Serverless or reserved instances or private cloud, so you can choose what fits your use case.
- Cost Efficiency: Pay-as-you-go pricing, optimized resource usage, tools to reduce wasted compute.
- Security Features: Support for data isolation, privacy controls, enterprise reliability.
- OpenAI-Compatible API: Makes it easier for developers already used to that style to integrate.
- Extensive catalog of LLMs and multimodal models
- Fast inference and good latency/throughput optimizations
- Flexible deployment modes for diverse needs
- Strong focus on cost control and scalability
- Enterprise-grade security, data isolation, and monitoring
- For smaller users, pricing might still be high depending on usage
- Fine-tuning large models can require significant data preparation
- Running very large or special models may still hit limits depending on availability
- Learning curve for configuring deployments, monitoring, scaling etc
Starter
Free
Custom
Custom Pricing.
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FAQs
You can run large language models, image generation, video, embeddings, ranking, speech, etc.
It is usually pay-as-you-go, with options for reserved GPU capacity and private setups; cost depends on model, latency, throughput.
Yes — the platform provides fine-tuning services, allowing you to adapt base models to your needs.
Yes — many models and APIs are OpenAI-compatible, which simplifies integration for developers familiar with OpenAI-style usage.
Supports private cloud or “bring your own cloud” deployments, data isolation, secure operations and enterprise-level reliability.
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Editorial Note
This page was researched and written by the ATB Editorial Team. Our team researches each AI tool by reviewing its official website, testing features, exploring real use cases, and considering user feedback. Every page is fact-checked and regularly updated to ensure the information stays accurate, neutral, and useful for our readers.
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