Best AI Inference Platforms in the Middle East

Your software can see objects in video and images. A few dozen images can be used to train a computer vision model. This takes less than 24 hours. We support innovators just like you in applying computer vision. Upload files via API or manually, including images, annotations, videos, and audio. There are many annotation formats that we support and it is easy to add training data as you gather it. Roboflow Annotate was designed to make labeling quick and easy. Your team can quickly annotate hundreds upon images in a matter of minutes. You can assess the quality of your data and prepare them for training. Use transformation tools to create new training data. See what configurations result in better model performance. All your experiments can be managed from one central location. You can quickly annotate images right from your browser. Your model can be deployed to the cloud, the edge or the browser. Predict where you need them, in half the time.

Vespa is forBig Data + AI, online. At any scale, with unbeatable performance. Vespa is a fully featured search engine and vector database. It supports vector search (ANN), lexical search, and search in structured data, all in the same query. Integrated machine-learned model inference allows you to apply AI to make sense of your data in real-time. Users build recommendation applications on Vespa, typically combining fast vector search and filtering with evaluation of machine-learned models over the items. To build production-worthy online applications that combine data and AI, you need more than point solutions: You need a platform that integrates data and compute to achieve true scalability and availability - and which does this without limiting your freedom to innovate. Only Vespa does this. Together with Vespa's proven scaling and high availability, this empowers you to create production-ready search applications at any scale and with any combination of features.

GMI GPU Cloud allows you to create generative AI applications within minutes. GMI Cloud offers more than just bare metal. Train, fine-tune and infer the latest models. Our clusters come preconfigured with popular ML frameworks and scalable GPU containers. Instantly access the latest GPUs to power your AI workloads. We can provide you with flexible GPUs on-demand or dedicated private cloud instances. Our turnkey Kubernetes solution maximizes GPU resources. Our advanced orchestration tools make it easy to allocate, deploy and monitor GPUs or other nodes. Create AI applications based on your data by customizing and serving models. GMI Cloud allows you to deploy any GPU workload quickly, so that you can focus on running your ML models and not managing infrastructure. Launch pre-configured environment and save time building container images, downloading models, installing software and configuring variables. You can also create your own Docker images to suit your needs.

Pipelines are permanent, models are temporary. Train, Evaluate, Deploy, Repeat. Valohai is the only MLOps platform to automate everything, from data extraction to model deployment. Automate everything, from data extraction to model installation. Automatically store every model, experiment, and artifact. Monitor and deploy models in a Kubernetes cluster. Just point to your code and hit "run". Valohai launches workers and runs your experiments. Then, Valohai shuts down the instances. You can create notebooks, scripts, or shared git projects using any language or framework. Our API allows you to expand endlessly. Track each experiment and trace back to the original training data. All data can be audited and shared.

Navigator provides rapid, location-independent answers to users, allowing them to create custom AI models that meet their individual needs. Experience innovation when technology complements human expertise. Create, manage, and watch content collaboratively with AI, co-workers and friends. Create custom AI models within minutes, not hours. Revitalize large models by streamlining training, reducing compute costs and incorporating attention steering. It seamlessly translates user interaction into manageable tasks. It chooses and executes AI models that are most appropriate for each task. The responses it delivers are in line with the user's expectations. No back doors, distributed storage and seamless inference. It uses distributed, edge-friendly technologies for lightning-fast interaction, wherever you are. Join our vibrant distributed storage eco-system to unlock access to the first watermarked universal models dataset.

Self-service machine learning platform that allows you to turn models into APIs with just a few mouse clicks. Sign up for a Deep Infra Account using GitHub, or login using GitHub. Choose from hundreds of popular ML models. Call your model using a simple REST API. Our serverless GPUs allow you to deploy models faster and cheaper than if you were to build the infrastructure yourself. Depending on the model, we have different pricing models. Some of our models have token-based pricing. The majority of models are charged by the time it takes to execute an inference. This pricing model allows you to only pay for the services you use. You can easily scale your business as your needs change. There are no upfront costs or long-term contracts. All models are optimized for low latency and inference performance on A100 GPUs. Our system will automatically scale up the model based on your requirements.

The complete LLM Platform with a superior developer's experience and robust infrastructure. Build, deploy and manage trusted, hyper-personalized and streamlined generative AI applications. Langbase is a new AI tool and inference engine for any LLM. It's an OpenAI alternative that's open-source. The most "developer friendly" LLM platform that can ship hyper-personalized AI applications in seconds.

Machine learning models can be deployed at scale with greater accuracy. With more models in production, R&D can be turned into ROI. Seldon reduces time to value so models can get to work quicker. Scale with confidence and minimize risks through transparent model performance and interpretable results. Seldon Deploy cuts down on time to production by providing production-grade inference servers that are optimized for the popular ML framework and custom language wrappers to suit your use cases. Seldon Core Enterprise offers enterprise-level support and access to trusted, global-tested MLOps software. Seldon Core Enterprise is designed for organizations that require: - Coverage for any number of ML models, plus unlimited users Additional assurances for models involved in staging and production - You can be confident that their ML model deployments will be supported and protected.

Kubernetes is a highly scalable platform for model inference that uses standards-based models. Trusted AI. KServe, a Kubernetes standard model inference platform, is designed for highly scalable applications. Provides a standardized, performant inference protocol that works across all ML frameworks. Modern serverless inference workloads supported by autoscaling, including a scale up to zero on GPU. High scalability, density packing, intelligent routing with ModelMesh. Production ML serving is simple and pluggable. Pre/post-processing, monitoring and explainability are all possible. Advanced deployments using the canary rollout, experiments and ensembles as well as transformers. ModelMesh was designed for high-scale, high density, and often-changing model use cases. ModelMesh intelligently loads, unloads and transfers AI models to and fro memory. This allows for a smart trade-off between user responsiveness and computational footprint.

NVIDIA Triton™, an inference server, delivers fast and scalable AI production-ready. Open-source inference server software, Triton inference servers streamlines AI inference. It allows teams to deploy trained AI models from any framework (TensorFlow or NVIDIA TensorRT®, PyTorch or ONNX, XGBoost or Python, custom, and more on any GPU or CPU-based infrastructure (cloud or data center, edge, or edge). Triton supports concurrent models on GPUs to maximize throughput. It also supports x86 CPU-based inferencing and ARM CPUs. Triton is a tool that developers can use to deliver high-performance inference. It integrates with Kubernetes to orchestrate and scale, exports Prometheus metrics and supports live model updates. Triton helps standardize model deployment in production.

Towhee can automatically optimize your pipeline for production-ready environments by using our Python API. Towhee supports data conversion for almost 20 unstructured data types, including images, text, and 3D molecular structure. Our services include pipeline optimizations that cover everything from data decoding/encoding to model inference. This makes your pipeline execution 10x more efficient. Towhee integrates with your favorite libraries and tools, making it easy to develop. Towhee also includes a Python method-chaining API that allows you to describe custom data processing pipelines. Schemas are also supported, making it as simple as handling tabular data to process unstructured data.

Production-ready AI models that are fast and accurate. High-availability inference API that leverages the most advanced NVIDIA GPUs. We have selected the most popular open-source natural language processing models (NLP) and deployed them for the community. You can fine-tune your models (including GPT-J) or upload your custom models. Then, deploy them to production. Upload your AI models, including GPT-J, to your dashboard and immediately use them in production.

InferKit provides a web interface as well as an API to create AI-based text generators. There's something for everyone, whether you're an app developer or a novelist looking to find inspiration. InferKit's text generator takes the text you provide and generates what it thinks is next using a state of the art neural network. It can generate any length of text on virtually any topic and is configurable. You can use the tool via the web interface or through the developer API. Register now to get started. You can also use the network to write poetry or stories. Marketing and auto-completion are other possible uses. The generator can only understand a limited amount of text at once (currently, at most 3000 characters), so if you give it a longer prompt it will not use the beginning. The network is already trained and doesn't learn from inputs. Each request must contain at least 100 characters

The AI Knowledge Platform. The Pinecone Database, Inference, and Assistant make building high-performance vector search apps easy. Fully managed and developer-friendly, the database is easily scalable without any infrastructure problems. Once you have vector embeddings created, you can search and manage them in Pinecone to power semantic searches, recommenders, or other applications that rely upon relevant information retrieval. Even with billions of items, ultra-low query latency Provide a great user experience. You can add, edit, and delete data via live index updates. Your data is available immediately. For more relevant and quicker results, combine vector search with metadata filters. Our API makes it easy to launch, use, scale, and scale your vector searching service without worrying about infrastructure. It will run smoothly and securely.

We have the infrastructure to meet all your computing needs, whether you need one or thousands GPUs or one vCPU or tens of thousand vCPUs. Our resources are available whenever you need them. Our platform makes switching between GPU and CPU instances a breeze. You can easily deploy, modify and rescale instances to meet your needs. You can get outstanding machine learning performance without breaking your bank. The latest technology for a much lower price. Modern GPUs are built to meet your workload demands. Get access to computing resources that are tailored for your models. Our OblivusAI OS allows you to access libraries and leverage our infrastructure for large-scale inference. Use our robust infrastructure to unleash the full potential of gaming by playing games in settings of your choosing.

Fireworks works with the leading generative AI researchers in the world to provide the best models at the fastest speed. Independently benchmarked for the fastest inference providers. Use models curated by Fireworks, or our multi-modal and functionality-calling models that we have trained in-house. Fireworks is also the 2nd most popular open-source model provider, and generates more than 1M images/day. Fireworks' OpenAI-compatible interface makes it simple to get started. Dedicated deployments of your models will ensure uptime and performance. Fireworks is HIPAA-compliant and SOC2-compliant and offers secure VPC connectivity and VPN connectivity. Own your data and models. Fireworks hosts serverless models, so there's no need for hardware configuration or deployment. Fireworks.ai provides a lightning fast inference platform to help you serve generative AI model.

Lamini allows enterprises to transform proprietary data into next-generation LLM capabilities by offering a platform that allows in-house software teams the opportunity to upgrade to OpenAI level AI teams, and build within the security provided by their existing infrastructure. Optimised JSON decoding guarantees a structured output. Fine-tuning retrieval-augmented retrieval to improve photographic memory. Improve accuracy and reduce hallucinations. Inferences for large batches can be highly parallelized. Parameter-efficient finetuning for millions of production adapters. Lamini is the sole company that allows enterprise companies to develop and control LLMs safely and quickly from anywhere. It uses the latest research and technologies to create ChatGPT, which was developed from GPT-3. These include, for example, fine-tuning and RLHF.

You can deploy Mystic in your own Azure/AWS/GCP accounts or in our shared GPU cluster. All Mystic features can be accessed directly from your cloud. In just a few steps, you can get the most cost-effective way to run ML inference. Our shared cluster of graphics cards is used by hundreds of users at once. Low cost, but performance may vary depending on GPU availability in real time. We solve the infrastructure problem. A Kubernetes platform fully managed that runs on your own cloud. Open-source Python API and library to simplify your AI workflow. You get a platform that is high-performance to serve your AI models. Mystic will automatically scale GPUs up or down based on the number API calls that your models receive. You can easily view and edit your infrastructure using the Mystic dashboard, APIs, and CLI.

Fully managed infrastructure, tools and workflows allow you to build, train and deploy models faster. Scale inference and deploy custom AI & LLMs in seconds on any infrastructure. Schedule batch jobs to handle your most demanding tasks, and only pay per second. Optimize costs by utilizing GPUs, spot instances, and automatic failover. YAML simplifies complex infrastructure setups by allowing you to train with a single command. Automate the scaling up of workers during periods of high traffic, and scaling down to zero when inactive. Deploy cutting edge models with persistent endpoints within a serverless environment to optimize resource usage. Monitor system and inference metrics, including worker counts, GPU utilization, throughput, and latency in real-time. Split traffic between multiple models to evaluate.

Create your first AI automation in 60 seconds. Inferable seamlessly integrates into your existing infrastructure and codebase, allowing you create powerful AI automation while maintaining control and security. Work with your existing codebase. Integrates with existing services via opt-in. Source code can be used to enforce determinism. Automate and manage automation programmatically. You own your computer and infrastructure. Inferable comes with delightful DX that will kickstart your AI automation journey. We bring you the best vertically integrated LLM Orchestration. You bring your domain and product expertise. Inferable's core is a distributed messaging queue that ensures AI automation is reliable and scalable. It ensures that your automations are executed correctly and that errors are handled gracefully. Decorators can be used to make your existing functions, REST endpoints, or GraphQL APIs require human approval.

Qubrid AI is a company that specializes in Artificial Intelligence. Its mission is to solve complex real-world problems across multiple industries. Qubrid AI’s software suite consists of AI Hub, an all-in-one shop for AI models, AI Compute GPU cloud and On-Prem appliances, and AI Data Connector. You can train infer-leading models, or your own custom creations. All within a streamlined and user-friendly interface. Test and refine models with ease. Then, deploy them seamlessly to unlock the power AI in your projects. AI Hub enables you to embark on a journey of AI, from conception to implementation, in a single powerful platform. Our cutting-edge AI Compute Platform harnesses the power from GPU Cloud and On Prem Server Appliances in order to efficiently develop and operate next generation AI applications. Qubrid is a team of AI developers, research teams and partner teams focused on enhancing the unique platform to advance scientific applications.

WebLLM is an in-browser, high-performance language model inference engine. It uses WebGPU to accelerate the hardware, enabling powerful LLM functions directly within web browsers, without server-side processing. It is compatible with the OpenAI API, allowing seamless integration of functionalities like JSON mode, function calling, and streaming. WebLLM supports a wide range of models including Llama Phi Gemma Mistral Qwen and RedPajama. Users can easily integrate custom models into MLC format and adapt WebLLM to their specific needs and scenarios. The platform allows for plug-and play integration via package managers such as NPM and Yarn or directly through CDN. It also includes comprehensive examples and a module design to connect with UI components. It supports real-time chat completions, which enhance interactive applications such as chatbots and virtual assistances.

NVIDIA TensorRT provides an ecosystem of APIs to support high-performance deep learning. It includes an inference runtime, model optimizations and a model optimizer that delivers low latency and high performance for production applications. TensorRT, built on the CUDA parallel programing model, optimizes neural networks trained on all major frameworks. It calibrates them for lower precision while maintaining high accuracy and deploys them across hyperscale data centres, workstations and laptops. It uses techniques such as layer and tensor-fusion, kernel tuning, and quantization on all types NVIDIA GPUs from edge devices to data centers. TensorRT is an open-source library that optimizes the inference performance for large language models.

Your deep learning workload can be accelerated. AI model training and inference can speed up your time to value. Deep learning is becoming more popular as enterprises adopt it to gain and scale insight through speech recognition and natural language processing. Deep learning can read text, images and video at scale and generate patterns for recommendation engines. It can also model financial risk and detect anomalies. Due to the sheer number of layers and volumes of data required to train neural networks, it has been necessary to use high computational power. Businesses are finding it difficult to demonstrate results from deep learning experiments that were implemented in silos.

DeepCube is a company that focuses on deep learning technologies. This technology can be used to improve the deployment of AI systems in real-world situations. The company's many patent innovations include faster, more accurate training of deep-learning models and significantly improved inference performance. DeepCube's proprietary framework is compatible with any hardware, datacenters or edge devices. This allows for over 10x speed improvements and memory reductions. DeepCube is the only technology that allows for efficient deployment of deep-learning models on intelligent edge devices. The model is typically very complex and requires a lot of memory. Deep learning deployments today are restricted to the cloud because of the large amount of memory and processing requirements.