EcoHashEcoHash Platform Technology
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Related Products
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About
EcoHash is a cloud platform for AI compute and model serving. It provides on-demand NVIDIA RTX Pro 6000 GPU workspaces and clusters, persistent cloud drives and shared filesystems, dataset validation, managed LoRA and QLoRA fine-tuning, dedicated model endpoints, and an inference API for text, vision, embeddings, reranking, image, and speech workloads. Developers can run their own containers or deploy open models and call platform or dedicated models through an OpenAI-compatible API. A browser console, SSH, JupyterLab, Compute API, and MCP tools support building, training, deploying, and managing workloads. EcoHash serves AI developers and platforms, including startups and cloud providers, and energy and mining operators developing AI compute infrastructure.
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About
Together AI provides an AI-native cloud platform built to accelerate training, fine-tuning, and inference on high-performance GPU clusters. Engineered for massive scale, the platform supports workloads that process trillions of tokens without performance drops. Together AI delivers industry-leading cost efficiency by optimizing hardware, scheduling, and inference techniques, lowering total cost of ownership for demanding AI workloads. With deep research expertise, the company brings cutting-edge models, hardware, and runtime innovations—like ATLAS runtime-learning accelerators—directly into production environments. Its full-stack ecosystem includes a model library, inference APIs, fine-tuning capabilities, pre-training support, and instant GPU clusters. Designed for AI-native teams, Together AI helps organizations build and deploy advanced applications faster and more affordably.
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Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
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Android
Not Supported
Chromebook
Not Supported
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Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Audience
AI developers and AI platforms—including startups, GPU marketplaces, and cloud providers—building or running AI models. The company also addresses energy and Bitcoin-mining operators interested in supplying infrastructure for AI inference.
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Audience
Together AI is ideal for AI-native teams, researchers, and enterprises that require high-performance GPU infrastructure, frontier-scale model training, and cost-optimized inference at massive scale
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Support
Phone Support
Not Supported
24/7 Live Support
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Online
Supported
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Support
Phone Support
Not Supported
24/7 Live Support
Not Supported
Online
Supported
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API
Offers API
Supported
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API
Offers API
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Screenshots and VideosNo images available
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Pricing
$0.02/1M tokens
Inference API usage starts at $0.02 per million input tokens for Jina-Embeddings-V3; other models and modalities have separate usage rates. RTX Pro 6000 GPU instances, clusters, and dedicated inference cost $1.89 per GPU-hour, billed per second with no minimum commitment. Storage starts at $5 per month for 50 GB; reserved nodes are contract-priced. A $1 signup credit is offered.
Free Version
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Free Trial
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Pricing
$0.0001 per 1k tokens
Free Version
Not Supported
Free Trial
Not Supported
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Reviews/
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Reviews/
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Training
Documentation
Supported
Webinars
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Live Online
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In Person
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Training
Documentation
Not Supported
Webinars
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Live Online
Not Supported
In Person
Not Supported
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Company InformationEcoHash Platform Technology
Founded: 2025
United States
ecohash.com
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Company InformationTogether AI
Founded: 2022
United States
www.together.ai/
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Integrations
Assembly
Not Supported
DeepCoder
Not Supported
DeepSWE
Not Supported
DeepSeek-V4-Flash
Not Supported
Kimi K2.6
Not Supported
LFM2
Not Supported
LLMetrics
Not Supported
LangDB
Not Supported
LiteLLM
Not Supported
MiniMax M2.7
Not Supported
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Integrations
Assembly
Supported
DeepCoder
Supported
DeepSWE
Supported
DeepSeek-V4-Flash
Supported
Kimi K2.6
Supported
LFM2
Supported
LLMetrics
Supported
LangDB
Supported
LiteLLM
Supported
MiniMax M2.7
Supported
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