Find and compare the best Artificial Intelligence software for Amazon Elastic Container Service (Amazon ECS) in 2025
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Use the comparison tool below to compare the top Artificial Intelligence software for Amazon Elastic Container Service (Amazon ECS) on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.
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Amazon CodeGuru
Amazon
1 RatingAmazon CodeGuru is an intelligent developer tool that uses machine learning to make intelligent recommendations for improving code quality, and identifying the most costly lines of code in an application. Integrate Amazon CodeGuru in your existing software development workflow to get built-in code reviews that will help you identify and optimize the most expensive lines of code to lower costs. Amazon CodeGuru Profiler allows developers to find the most expensive lines in an application's code. It also provides visualizations and suggestions on how to improve code to make it more affordable. Amazon CodeGuru Reviewer uses machine-learning to identify critical issues and difficult-to-find bugs in application development to improve code quality. -
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Splunk Enterprise
Splunk
2 RatingsSplunk makes it easy to go from data to business results faster than ever before. Splunk Enterprise makes it easy to collect, analyze, and take action on the untapped value of big data generated by technology infrastructures, security systems, and business applications. This will give you the insight to drive operational performance, and business results. You can collect and index logs and machine data from any source. Combine your machine data with data stored in relational databases, data warehouses, Hadoop and NoSQL data storages. Multi-site clustering and automatic loads balancing scale can support hundreds of terabytes per day, optimize response time and ensure continuous availability. Splunk Enterprise can be customized easily using the Splunk platform. Developers can create custom Splunk apps or integrate Splunk data in other applications. Splunk, our community and partners can create apps that enhance and extend the power and capabilities of the Splunk platform. -
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Edge Delta
Edge Delta
$0.20 per GBEdge Delta is a new way to do observability. We are the only provider that processes your data as it's created and gives DevOps, platform engineers and SRE teams the freedom to route it anywhere. As a result, customers can make observability costs predictable, surface the most useful insights, and shape your data however they need. Our primary differentiator is our distributed architecture. We are the only observability provider that pushes data processing upstream to the infrastructure level, enabling users to process their logs and metrics as soon as they’re created at the source. Data processing includes: * Shaping, enriching, and filtering data * Creating log analytics * Distilling metrics libraries into the most useful data * Detecting anomalies and triggering alerts We combine our distributed approach with a column-oriented backend to help users store and analyze massive data volumes without impacting performance or cost. By using Edge Delta, customers can reduce observability costs without sacrificing visibility. Additionally, they can surface insights and trigger alerts before data leaves their environment. -
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Elastic Observability
Elastic
$16 per monthThe most widely used observability platform, built on the ELK Stack, is the best choice. It converges silos and delivers unified visibility and actionable insight. All your observability data must be in one stack to effectively monitor and gain insight across distributed systems. Unify all data from the application, infrastructure, user, and other sources to reduce silos and improve alerting and observability. Unified solution that combines unlimited telemetry data collection with search-powered problem resolution for optimal operational and business outcomes. Converge data silos with the ingesting of all your telemetry data from any source, in an open, extensible and scalable platform. Automated anomaly detection powered with machine learning and rich data analysis can speed up problem resolution. -
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NVIDIA Triton Inference Server
NVIDIA
FreeNVIDIA 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. -
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Jovu
Amplication
Amplication AI allows you to easily create new services and extend existing applications. From idea to production within four minutes. AI assistant that generates production ready code, ensuring consistency and predictability. With production-ready code built for scale, you can go from concept to deployment within minutes. Amplication's AI provides more than just prototypes. Get fully operational, robust services that are ready to go live. Streamline your development workflows to reduce time and optimize resources. With the power of AI, you can do more with your existing resources. Jovu will translate your requirements into code components that are ready to use. Data models, APIs and other components that are ready for production, including authentication, authorization and event-driven architecture. Add architecture components and integrations, and extend Amplication plugins. -
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Amazon EC2 Inf1 Instances
Amazon
$0.228 per hourAmazon EC2 Inf1 instances were designed to deliver high-performance, cost-effective machine-learning inference. Amazon EC2 Inf1 instances offer up to 2.3x higher throughput, and up to 70% less cost per inference compared with other Amazon EC2 instance. Inf1 instances are powered by up to 16 AWS inference accelerators, designed by AWS. They also feature Intel Xeon Scalable 2nd generation processors, and up to 100 Gbps of networking bandwidth, to support large-scale ML apps. These instances are perfect for deploying applications like search engines, recommendation system, computer vision and speech recognition, natural-language processing, personalization and fraud detection. Developers can deploy ML models to Inf1 instances by using the AWS Neuron SDK. This SDK integrates with popular ML Frameworks such as TensorFlow PyTorch and Apache MXNet. -
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Amazon EC2 G5 Instances
Amazon
$1.006 per hourAmazon EC2 instances G5 are the latest generation NVIDIA GPU instances. They can be used to run a variety of graphics-intensive applications and machine learning use cases. They offer up to 3x faster performance for graphics-intensive apps and machine learning inference, and up to 3.33x faster performance for machine learning learning training when compared to Amazon G4dn instances. Customers can use G5 instance for graphics-intensive apps such as video rendering, gaming, and remote workstations to produce high-fidelity graphics real-time. Machine learning customers can use G5 instances to get a high-performance, cost-efficient infrastructure for training and deploying larger and more sophisticated models in natural language processing, computer visualisation, and recommender engines. G5 instances offer up to three times higher graphics performance, and up to forty percent better price performance compared to G4dn instances. They have more ray tracing processor cores than any other GPU based EC2 instance. -
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Amazon EC2 P4 Instances
Amazon
$11.57 per hourAmazon EC2 instances P4d deliver high performance in cloud computing for machine learning applications and high-performance computing. They offer 400 Gbps networking and are powered by NVIDIA Tensor Core GPUs. P4d instances offer up to 60% less cost for training ML models. They also provide 2.5x better performance compared to the previous generation P3 and P3dn instance. P4d instances are deployed in Amazon EC2 UltraClusters which combine high-performance computing with networking and storage. Users can scale from a few NVIDIA GPUs to thousands, depending on their project requirements. Researchers, data scientists and developers can use P4d instances to build ML models to be used in a variety of applications, including natural language processing, object classification and detection, recommendation engines, and HPC applications. -
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AWS Copilot
Amazon
Build common application architectures quickly with scalable, production-ready, and secure infrastructure-as-code (IaC) templates. Automate deployments using a single command and configure the delivery pipeline to connect a code repository with your application's environment. Use end-to-end processes to build, release and operate your microservices with a single tool. AWS Copilot provides a command-line interface to launch and manage containerized AWS applications. It simplifies the running of applications on Amazon Elastic Container Service, AWS Fargate and AWS App Runner. Simplify operations with automatic provisioning of infrastructure, scaling resources and optimizing costs. This allows you to focus more on applications than cluster management. Create, release and operate production-ready containers and services using Amazon Elastic Container Service and AWS Fargate in one command. -
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AWS Neuron
Amazon Web Services
It supports high-performance learning on AWS Trainium based Amazon Elastic Compute Cloud Trn1 instances. It supports low-latency and high-performance inference for model deployment on AWS Inferentia based Amazon EC2 Inf1 and AWS Inferentia2-based Amazon EC2 Inf2 instance. Neuron allows you to use popular frameworks such as TensorFlow or PyTorch and train and deploy machine-learning (ML) models using Amazon EC2 Trn1, inf1, and inf2 instances without requiring vendor-specific solutions. AWS Neuron SDK is natively integrated into PyTorch and TensorFlow, and supports Inferentia, Trainium, and other accelerators. This integration allows you to continue using your existing workflows within these popular frameworks, and get started by changing only a few lines. The Neuron SDK provides libraries for distributed model training such as Megatron LM and PyTorch Fully Sharded Data Parallel (FSDP). -
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Amazon EC2 P5 Instances
Amazon
Amazon Elastic Compute Cloud's (Amazon EC2) instances P5 powered by NVIDIA Tensor core GPUs and P5e or P5en instances powered NVIDIA Tensor core GPUs provide the best performance in Amazon EC2 when it comes to deep learning and high-performance applications. They can help you accelerate the time to solution up to four times compared to older GPU-based EC2 instance generation, and reduce costs to train ML models up to forty percent. These instances allow you to iterate faster on your solutions and get them to market quicker. You can use P5,P5e,and P5en instances to train and deploy increasingly complex large language and diffusion models that power the most demanding generative artificial intelligent applications. These applications include speech recognition, video and image creation, code generation and question answering. These instances can be used to deploy HPC applications for pharmaceutical discovery. -
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Amazon EC2 capacity blocks for ML allow you to reserve accelerated compute instance in Amazon EC2 UltraClusters that are dedicated to machine learning workloads. This service supports Amazon EC2 P5en instances powered by NVIDIA Tensor Core GPUs H200, H100 and A100, as well Trn2 and TRn1 instances powered AWS Trainium. You can reserve these instances up to six months ahead of time in cluster sizes from one to sixty instances (512 GPUs, or 1,024 Trainium chip), providing flexibility for ML workloads. Reservations can be placed up to 8 weeks in advance. Capacity Blocks can be co-located in Amazon EC2 UltraClusters to provide low-latency and high-throughput connectivity for efficient distributed training. This setup provides predictable access to high performance computing resources. It allows you to plan ML application development confidently, run tests, build prototypes and accommodate future surges of demand for ML applications.
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Amazon EC2 UltraClusters
Amazon
Amazon EC2 UltraClusters allow you to scale up to thousands of GPUs and machine learning accelerators such as AWS trainium, providing access to supercomputing performance on demand. They enable supercomputing to be accessible for ML, generative AI and high-performance computing through a simple, pay-as you-go model, without any setup or maintenance fees. UltraClusters are made up of thousands of accelerated EC2 instance co-located within a specific AWS Availability Zone and interconnected with Elastic Fabric Adapter networking to create a petabit scale non-blocking network. This architecture provides high-performance networking, and access to Amazon FSx, a fully-managed shared storage built on a parallel high-performance file system. It allows rapid processing of large datasets at sub-millisecond latency. EC2 UltraClusters offer scale-out capabilities to reduce training times for distributed ML workloads and tightly coupled HPC workloads. -
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Amazon EC2 Trn2 Instances
Amazon
Amazon EC2 Trn2 instances powered by AWS Trainium2 are designed for high-performance deep-learning training of generative AI model, including large language models, diffusion models, and diffusion models. They can save up to 50% on the cost of training compared to comparable Amazon EC2 Instances. Trn2 instances can support up to 16 Trainium2 accelerations, delivering up to 3 petaflops FP16/BF16 computing power and 512GB of high bandwidth memory. Trn2 instances support up to 1600 Gbps second-generation Elastic Fabric Adapter network bandwidth. NeuronLink is a high-speed nonblocking interconnect that facilitates efficient data and models parallelism. They are deployed as EC2 UltraClusters and can scale up to 30,000 Trainium2 processors interconnected by a nonblocking, petabit-scale, network, delivering six exaflops in compute performance. The AWS neuron SDK integrates with popular machine-learning frameworks such as PyTorch or TensorFlow. -
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AWS Deep Learning Containers
Amazon
Deep Learning Containers are Docker images pre-installed with the most popular deep learning frameworks. Deep Learning Containers allow you to quickly deploy custom ML environments without the need to build and optimize them from scratch. You can quickly deploy deep learning environments using prepackaged, fully tested Docker images. Integrate Amazon SageMaker, Amazon EKS and Amazon ECS to create custom ML workflows that can be used for validation, training, and deployment.
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