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Compute with HivenetHivenet Compute gives you on-demand RTX 5090 and RTX Pro 6000 GPUs, plus vCPU instances, billed per second while they run. Hivenet owns and operates the infrastructure, with data centres in France, the UAE and the US, so EU teams can keep workloads and data in Europe. Use it for training and fine-tuning, inference, notebooks, rendering and batch jobs. Launch from ready-made templates in a few minutes, or automate everything through the public Compute API. If you'd rather not run the serving layer yourself, the Inference API gives you an OpenAI-compatible endpoint. Teams get organisations with role-based access, prepaid credits, and S3-compatible object storage alongside compute.
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FinOpslyAsk a CFO what the company spent on AI last quarter and you will get a number. Ask which product line it belonged to, whether anyone approved it, or what it earned, and the room goes quiet. FinOpsly was built for that second set of questions. It is an AI Cost Governance platform. AI does not run in isolation, so FinOpsly does not price it in isolation either. A model call pulls warehouse queries, GPU time and storage behind it, and the engineers building the feature are burning licensed seats the whole time. All of that lands in one cost model, mapped to the company's own structure: owner, team, product, business unit, customer. What teams use it for: Pricing a workload before anyone provisions anything. Describe the architecture, get a cost estimate across the stack, and see which assumptions drove it. Compare model options using consumption you have already paid for. Making chargeback something finance trusts. Hierarchies run nine levels or deeper. Tags get standardized across providers that never agreed on a convention. API keys and resources are labeled in bulk from instructions written in ordinary English. Anything still unowned shows up as a dollar figure. Holding the line during the month. Budgets by team, project or key. Anomalies flagged with a root cause and sent to the person responsible. Waste that provider consoles do not catch, found by FinOpsly's own detection models. Idle compute parked on schedules the customer approved, and reversible. Proving the outcome. One chargeback run covering AI, cloud, data and SaaS together. Savings measured against the base-line along with cost-to-serve metrics: cost per active user, per customer served. Customers have moved attributable spend from 68% to 99% inside 90 days and taken a chargeback cycle from 12.4 days down to under one. Built for CIOs, CTOs, FinOps practitioners and the finance teams who sign off on the bill.
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PapirflyPapirfly is a trusted SaaS provider empowering global businesses to deliver consistent, on-brand content at scale. Combining Digital Asset Management (DAM) with templated content creation, Papirfly enables marketing and brand teams to centralize assets, streamline approvals, and produce brand-compliant materials across every channel—digital, print, and video. Today, over 1 million users across 1,500+ brands, including Mercedes-Benz, Mondelez, and Goldman Sachs, rely on Papirfly to strengthen brand governance, speed up campaign execution, and improve creative efficiency. As part of the Papirfly Group—which includes Keepeek, Brandpad, and Adgistics—Papirfly continues to lead the way in helping enterprises simplify content operations and build stronger, more unified brand experiences worldwide.
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LTXLTX builds open world models, AI systems that generate, simulate, and shape video, audio, and the physical world. Lightricks created LTX so that developers, studios, and enterprises can own the model they build on, not just rent access to someone else's. The current release, LTX-2.5, is a 22B-parameter dual-stream diffusion transformer. It renders native 4K footage at up to 50fps and produces synchronized audio and video in one pass, no separate tools required. Independent benchmarks from Artificial Analysis place LTX in the top three AI video models worldwide. There is no single way to work with LTX. Pull the open weights and run the model yourself on your own machines. Take a commercial license for on-premise deployment with full enterprise support. Or use LTX Studio, the packaged production suite for creative teams that want the model without managing the infrastructure. ElevenLabs, Asteria Film Co., Magnopus, and NVIDIA all build on it today. If you need a quick clip for social media, look elsewhere. LTX exists for AI teams turning video, audio, and simulation into part of their own product, not a novelty.
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RunpodRunpod offers a robust cloud infrastructure designed for effortless deployment and scalability of AI workloads utilizing GPU-powered pods. By providing a diverse selection of NVIDIA GPUs, including options like the A100 and H100, Runpod ensures that machine learning models can be trained and deployed with high performance and minimal latency. The platform prioritizes user-friendliness, enabling users to create pods within seconds and adjust their scale dynamically to align with demand. Additionally, features such as autoscaling, real-time analytics, and serverless scaling contribute to making Runpod an excellent choice for startups, academic institutions, and large enterprises that require a flexible, powerful, and cost-effective environment for AI development and inference. Furthermore, this adaptability allows users to focus on innovation rather than infrastructure management.
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10Duke Enterprise10Duke Enterprise provides a versatile and cloud-based software licensing solution that allows software vendors to effortlessly configure, manage, and generate revenue from the licenses they offer to their clients. By establishing a unified point of license management, 10Duke Enterprise effectively supports desktop applications, SaaS, mobile applications, APIs, virtual machines, and various devices. This innovative platform is cloud-native, accommodates all licensing models, seamlessly integrates with CRM and e-commerce systems, features a built-in Customer Identity Management system, and is equipped to handle offline scenarios. Both small to medium-sized businesses and Fortune 500 companies utilize 10Duke Enterprise, making it an ideal choice for organizations of any size. The solution is favored by rapidly growing software vendors across diverse sectors, especially those offering desktop, SaaS, and mobile applications, as well as devices, APIs, and virtual machines. Specifically tailored for dynamic software enterprises aiming to enhance their licensing processes while reducing friction, 10Duke Enterprise delivers numerous benefits, including a significant reduction in internal licensing administration costs by up to 70%. Furthermore, it enhances customer experiences by streamlining product trials and access, provides insights into customer usage patterns to help boost license sales, and mitigates revenue losses through a real-time licensing and access control framework. Additionally, the solution offers robust integration capabilities with third-party systems such as CRM and e-commerce platforms, ensuring a comprehensive approach to license management.
What is Falcon-7B?
The Falcon-7B model is a causal decoder-only architecture with a total of 7 billion parameters, created by TII, and trained on a vast dataset consisting of 1,500 billion tokens from RefinedWeb, along with additional carefully curated corpora, all under the Apache 2.0 license.
What are the benefits of using Falcon-7B?
This model excels compared to other open-source options like MPT-7B, StableLM, and RedPajama, primarily because of its extensive training on an unimaginably large dataset of 1,500 billion tokens from RefinedWeb, supplemented by thoughtfully selected content, which is clearly reflected in its performance ranking on the OpenLLM Leaderboard.
Furthermore, it features an architecture optimized for rapid inference, utilizing advanced technologies such as FlashAttention and multiquery strategies.
In addition, the flexibility offered by the Apache 2.0 license allows users to pursue commercial ventures without worrying about royalties or stringent constraints.
This unique blend of high performance and operational freedom positions Falcon-7B as an excellent option for developers in search of sophisticated modeling capabilities.
Ultimately, the model's design and resourcefulness make it a compelling choice in the rapidly evolving landscape of machine learning.
What is Aya?
Aya stands as a pioneering open-source generative large language model that supports a remarkable 101 languages, far exceeding the offerings of other open-source alternatives. This expansive language support allows researchers to harness the powerful capabilities of LLMs for numerous languages and cultures that have frequently been neglected by dominant models in the industry.
Alongside the launch of the Aya model, we are also unveiling the largest multilingual instruction fine-tuning dataset, which contains 513 million entries spanning 114 languages. This extensive dataset is enriched with distinctive annotations from native and fluent speakers around the globe, ensuring that AI technology can address the needs of a diverse international community that has often encountered obstacles to access. Therefore, Aya not only broadens the horizons of multilingual AI but also fosters inclusivity among various linguistic groups, paving the way for future advancements in the field. By creating an environment where linguistic diversity is celebrated, Aya stands to inspire further innovations that can bridge gaps in communication and understanding.
Integrations Supported
AI/ML API
C
CSS
Clojure
Elixir
F#
Falcon Chat
HTML
Java
API Availability
API Availability
Pricing Information
Free
Open source
Free Version
Pricing Information
Pricing not provided
Supported Platforms
SaaS
Windows
Mac
On-Prem
Linux
Supported Platforms
SaaS
On-Prem
Customer Service / Support
Not specified
Customer Service / Support
Not specified
Training Options
Documentation Hub
Training Options
Documentation Hub
Company Facts
Organization Name
Technology Innovation Institute (TII)
Date Founded
2019
Company Location
United Arab Emirates
Company Website
www.tii.ae/
Company Facts
Organization Name
Cohere AI
Date Founded
2019
Company Location
Canada
Company Website
txt.cohere.com/aya/
Categories and Features
Categories and Features
AI Models
Not specified
AI Vision Models
Not specified
Large Language Models
Not specified
Open-Weight AI Models
Not specified