--- title: Amazon SageMaker Reviews meta\_title: 'Amazon SageMaker Reviews 2026: Details, Pricing, & Features | G2' meta\_description: Filter 57 reviews by the users' company size, role or industry to find out how Amazon SageMaker works for a business like yours. aggregate\_rating: rating\_value: 4.3 review\_count: 57 scale: '5' date\_modified: '2026-08-09' parent\_category: name: Artificial Intelligence url: https://www.g2.com/categories/artificial-intelligence --- # Amazon SageMaker Reviews & Product Details Visit Website Amazon SageMaker is a fully managed service that enables data scientists and developers to build, train, and deploy machine learning (ML) models at scale. It provides a comprehensive suite of tools and infrastructure, streamlining the entire ML workflow from data preparation to model deployment. With SageMaker, users can quickly connect to training data, select and optimize algorithms, and deploy models in a secure and scalable environment. Key Features and Functionality: - Integrated Development Environments (IDEs): SageMaker offers a unified, web-based interface with built-in IDEs, including JupyterLab and RStudio, facilitating seamless development and collaboration. - Pre-built Algorithms and Frameworks: It includes a selection of optimized ML algorithms and supports popular frameworks like TensorFlow, PyTorch, and Apache MXNet, allowing flexibility in model development. - Automated Model Tuning: SageMaker can automatically tune models to achieve optimal accuracy, reducing the time and effort required for manual adjustments. - Scalable Training and Deployment: The service manages the underlying infrastructure, enabling efficient training of models on large datasets and deploying them across auto-scaling clusters for high availability. - MLOps and Governance: SageMaker provides tools for monitoring, debugging, and managing ML models, ensuring robust operations and compliance with enterprise security standards. Primary Value and Problem Solved: Amazon SageMaker addresses the complexity and resource-intensive nature of developing and deploying ML models. By offering a fully managed environment with integrated tools and scalable infrastructure, it accelerates the ML lifecycle, reduces operational overhead, and enables organizations to derive insights and value from their data more efficiently. This empowers businesses to innovate rapidly and implement AI solutions without the need for extensive in-house expertise or infrastructure management. * * * Product Website Amazon SageMaker Seller [Amazon Web Services (AWS)](https://www.g2.com/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd) Discussions [Amazon SageMaker Community](https://www.g2.com/products/amazon-sagemaker/discuss) Solution Type All-in-One Overview by John Miranda (Digital Marketing Specialist at Amazon Web Services) Show More ## Value at a Glance Averages based on real user reviews. ### Time to Implement 2 months ### Perceived Cost $$$$$ [ View More Pricing Information ](https://www.g2.com/products/amazon-sagemaker/pricing) ## Top-Rated Alternatives [ ![Gemini Enterprise Agent Platform](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Gemini Enterprise Agent Platform") Gemini Enterprise Agent Platform 4.3/5(744) ](https://www.g2.com/products/gemini-enterprise-agent-platform/reviews) [ ![Dataiku](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Dataiku") Dataiku 4.4/5(224) ](https://www.g2.com/products/dataiku/reviews) [ ![Azure Machine Learning](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Azure Machine Learning") Azure Machine Learning 4.3/5(90) ](https://www.g2.com/products/microsoft-azure-machine-learning/reviews) [ View All Alternatives ](https://www.g2.com/products/amazon-sagemaker/competitors/alternatives) ## User Insights Average based on 57 real user reviews. Implementation Time 2 months Perceived Cost $$$$$ [Log in to unlock pricing and user insights](/login) ## Amazon SageMaker Integrations (7) What do users say about integrations? Integration information sourced from real user reviews. 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AP Atharva P. Cloud BI Engineer Enterprise (\> 1000 emp.) 7/29/2026 "End-to-End ML Platform That Streamlines the Full Lifecycle" 4.5/5 What do you like best about Amazon SageMaker? Amazon SageMaker AI is an end-to-end machine learning platform that brings data preparation, feature engineering, model training, hyperparameter optimisation, deployment, monitoring, and MLOps together in a single managed service. I especially like SageMaker Studio, JumpStart, Pipelines, Feature Store, Training Jobs, Real-Time Endpoints, Batch Transform, Model Registry, and Clarify, as they help streamline the entire ML lifecycle from experimentation through to production. SageMaker Studio offers a unified workspace for data scientists, and the managed infrastructure significantly reduces operational overhead. Performance is also excellent, thanks to distributed training, managed GPU instances, automatic scaling, and built-in optimisation features that make it easier to run and iterate on workloads efficiently. Integration with Amazon Bedrock further supports hybrid architectures, letting teams combine traditional ML models with foundation models for generative AI applications. Pricing can be substantial for GPU-intensive workloads, but in my experience the reduced infrastructure management burden and faster path to deployment often translate into strong ROI for enterprise ML teams. Review collected by and hosted on G2.com. What do you dislike about Amazon SageMaker? The service has a steep learning curve because it provides a wide range of capabilities across the ML lifecycle. Costs can also rise quickly if notebook instances, endpoints, or GPU resources aren’t monitored and managed carefully. For new users, Studio may feel overwhelming as well, given the sheer number of services and options available. Review collected by and hosted on G2.com. What problems is Amazon SageMaker solving and how is that benefiting you? Amazon SageMaker AI solved the challenge of building, training, deploying, and monitoring machine learning models at scale without managing ML infrastructure. Example: A demand forecasting solution used SageMaker Feature Store to manage training features, SageMaker Pipelines to automate model training, Hyperparameter Tuning Jobs to optimise accuracy, Model Registry for version control, and Real-Time Endpoints for online predictions. Model Monitor continuously detected data drift, enabling the team to maintain prediction quality while reducing manual operational effort. Review collected by and hosted on G2.com. Show More Our network of Icons are G2 members who are recognized for their outstanding contributions and commitment to helping others through their expertise.G2 IconCurrent UserValidated ReviewerIncentivizedSource: G2 invite ![Lokesh S.](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Lokesh S.") LS Lokesh S. Senior Data Scientist Mid-Market (51-1000 emp.) 6/15/2026 "A powerhouse for end-to-end ML, but be prepared for a steep learning curve" 5/5 What do you like best about Amazon SageMaker? Being a Senior Data Scientist in a medium sized company, we wanted an approach that would help us get notebook-based models into production without building too much infrastructure. I love the amazingly wide-ranging ecosystem, it's an end to end platform. We use it a lot across our predictive customer behavior models and NLP pipelines and smoothly jumping between pushing a test job on SageMaker Studio and being able to start heavy distributed training jobs is awesome. No longer do I need to rely on our over bounded dev-ops team to provision certain GPU instances for me. I can just specify the amount of hardware in my code and AWS will spin them up and teard them down for me. The managed endpoints during the deployment process are also a huge timesaver, meaning we'll be able to generate our model predictions through a strong API that supports auto-scaling out of the box. Review collected by and hosted on G2.com. What do you dislike about Amazon SageMaker? The number one obstacle is its starting complexity. SageMaker is a tool that requires some configuration, and the documentation, although comprehensive, can be confusing and could be viewed as a disjointed set of tutorials. There's lots of time involved to get to the "AWS way" of doing stuff, and it's a frequent pitfall for team members coming into AWS for the first time—configuring IAM roles and VPCs and permissions. Secondly, it can be quite harsh on your wallet if you're not penny-pinching. Users can easily forget to turn off their SageMaker Studio instance or an experimental endpoint over the weekend, ending up with a large bill. Last but not least, the mesh of features of the Studio interface sometimes comes at the expense of somewhat slow and cumbersome performance in comparison to a lightweight, on-node, local Jupyter server leverage my own machine. Review collected by and hosted on G2.com. What problems is Amazon SageMaker solving and how is that benefiting you? Previous to SageMaker, the main pain point for us as a mid sized team was getting the models deployed. Then we would train a strong model, pass off the weights and a crappy py script to the software engineering team and wait a couple of weeks for them to get the ball rolling on making it scalable and containerised. By placing the data science team in charge of the entire lifecycle, SageMaker completely addressed this friction. The use of a sentiment analysis in a customer support ticketing system, following the lines of a real-time sentiment analysis feature in our customer support ticketing system, was a true-to-life example for us. In a few days, my team could train a transformer on SageMaker, perform hyperparameter tuning and then deploy it behind a production-ready, secured endpoint all by itself using the integration with HuggingFace. It has really simplified our MLOps process, so that we can develop faster, and bring real business value without having to keep our fingers on the ground waiting for engineering help. Review collected by and hosted on G2.com. Show More Validated ReviewerIncentivizedSource: G2 invite ![Md R.](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Md R.") MR Md R. Strategy Specialist – Operations & Process Improvement Small-Business (50 or fewer emp.) 5/21/2026 "Amazon SageMaker: End-to-End ML Workflow That Gets Models to Production Faster" 5/5 What do you like best about Amazon SageMaker? I like that Amazon SageMaker gives you one managed path from experimentation to deployment without forcing you to assemble every piece yourself. It covers the whole ML workflow: notebooks, training jobs, tuning, pipelines, model registry, endpoints, batch inference. It reduces infrastructure work: you can focus more on models and less on provisioning GPUs, containers, scaling, and endpoint ops. It works well for teams: reproducible training jobs, managed pipelines, and deployment/versioning help when multiple people touch the same system. It scales from simple to serious: you can start with a notebook and later move to distributed training or production endpoints in the same ecosystem. It integrates with AWS well: IAM, S3, CloudWatch, ECR, Lambda, and EventBridge make it easier if the rest of your stack is already on AWS. If I had to pick one thing: the biggest advantage is the operational glue—it makes moving from “model works in a notebook” to “model runs reliably in production” much less painful. Review collected by and hosted on G2.com. What do you dislike about Amazon SageMaker? The biggest downside is complexity: SageMaker is powerful, but it often feels like a toolbox of AWS services rather than one clean, opinionated ML platform. The learning curve is steep; you end up needing to understand SageMaker itself plus IAM, S3, VPCs, ECR, CloudWatch, and AWS networking. Costs can get slippery; notebook instances, endpoints, training jobs, storage, and data transfer can keep running unless you manage them carefully. The UX can feel fragmented; some tasks are easier in the SDK, some in Studio, some in raw AWS configuration. Debugging can be frustrating; failures are often caused by permissions, container setup, networking, or obscure configuration mismatches rather than model code. Vendor lock-in is real; once your pipelines, deployment flow, and monitoring are built around SageMaker/AWS primitives, moving away takes work. The abstractions can be leaky; “managed” does not always mean simple, and you still may need to think like an infra engineer. If I had to sum it up: SageMaker is very capable, but it’s not especially elegant. It rewards teams that already operate comfortably in AWS, and can feel heavy for smaller teams or faster-moving experimentation. Review collected by and hosted on G2.com. What problems is Amazon SageMaker solving and how is that benefiting you? Amazon SageMaker is mainly solving the “ML operationalization” problem: turning model development into something repeatable, scalable, and deployable. For me, the benefit is less about building a single model and more about reducing all the friction around it. It solves infrastructure setup for training and inference; instead of hand-building GPU servers, job schedulers, and serving stacks, you can run managed training jobs and endpoints. It solves workflow fragmentation; data prep, experiments, tuning, pipelines, model registry, and deployment can live in one ecosystem instead of a pile of disconnected tools. It solves scaling problems; you can move from small experiments to larger training jobs or production traffic without redesigning everything. It solves repeatability and team coordination; jobs, pipelines, artifacts, and model versions are easier to track than ad hoc notebook-driven work. It solves production deployment overhead; managed endpoints, batch jobs, and monitoring make it easier to serve models reliably. It solves AWS integration pain; if your data and apps already live in AWS, SageMaker reduces the glue code between ML and the rest of the platform. How that benefits me: I spend less time on DevOps-heavy ML plumbing. I get a faster path from prototype to production. I have a more standardized workflow for teams and projects. I can rely on managed scaling and monitoring instead of inventing it. I avoid stitching together many separate tools unless I want more customization. The tradeoff is that it benefits me most when I actually need that operational structure. If I just want lightweight experimentation, SageMaker can feel heavier than necessary. Review collected by and hosted on G2.com. Show More Validated ReviewerIncentivizedSource: G2 invite ![Sachin N.](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Sachin N.") SN Sachin N. Data Analyst Enterprise (\> 1000 emp.) 5/22/2026 "SageMaker Brilliantly Scales Training Beyond Local Limits" 4.5/5 What do you like best about Amazon SageMaker? Working locally is great—until your dataset outgrows your RAM, or you realize you need a multi-GPU cluster to train a model in hours rather than days. SageMaker addresses that exact friction point, and it does so brilliantly. Review collected by and hosted on G2.com. What do you dislike about Amazon SageMaker? I end up spending a massive amount of time digging through CloudWatch logs just to discover that a library version was mismatched or that an S3 file path was slightly off. It really drags out the debugging process and drastically slows down the inner loop. Review collected by and hosted on G2.com. What problems is Amazon SageMaker solving and how is that benefiting you? By automating provisioning, scaling, and deployment, it keeps me from wasting hours dealing with CUDA drivers, server maintenance, or overly complex Docker pipelines. That means I can focus fully on data science and move models faster from a local idea into live production. Review collected by and hosted on G2.com. Show More Validated ReviewerIncentivizedSource: G2 invite ![Arshiya A.](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Arshiya A.") AA Arshiya A. HR Manager Small-Business (50 or fewer emp.) 5/22/2026 "End-to-End ML Workflow in One Tool: Build, Scale, and Monitor" 5/5 What do you like best about Amazon SageMaker? Best is how it helps in end to end working -model building, scaling, monitoring so that everything is done in one tool. Review collected by and hosted on G2.com. What do you dislike about Amazon SageMaker? Everything is perfect, just needs to be informed to people more about it Review collected by and hosted on G2.com. What problems is Amazon SageMaker solving and how is that benefiting you? It manages everything automatically, teams does not need different tools for the same. Data workflows are easy to manage along with experiment tracking Review collected by and hosted on G2.com. Show More Current UserValidated ReviewerIncentivizedSource: G2 invite ![Amrendra K.](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Amrendra K.") AK Amrendra K. Indigo squad Member Small-Business (50 or fewer emp.) 1/2/2026 "Blazing Fast Model Training, Intuitive Experience" 5/5 What do you like best about Amazon SageMaker? I use Amazon SageMaker for building a deep learning model, specifically an object detection model. It's a really great experience for me, especially because my laptop doesn't have advanced GPU support, and training a model would take around 7-8 hours. With Amazon SageMaker's virtual machine, training my deep learning model only takes 3-4 minutes. This platform is great, and even someone who has never used it before can adapt to it the first time and easily understand all the functionality given on SageMaker. I think the virtual machine of Amazon SageMaker is more advanced than the Microsoft Azure platform. It is more effective and less time-consuming. The ease of use is brilliant; I can easily adapt to this platform compared to Microsoft. The initial setup is very easy, and with single authentication, I have access to the resources I need for my work. In my view, I give it 10 out of 10. Review collected by and hosted on G2.com. What do you dislike about Amazon SageMaker? This is great platform. I don't dislike this. Review collected by and hosted on G2.com. What problems is Amazon SageMaker solving and how is that benefiting you? I use Amazon SageMaker to train deep learning models much faster, reducing training time from 7-8 hours on my laptop to just 3-4 minutes on SageMaker. It's easy to adapt even for first-time users. Review collected by and hosted on G2.com. Show More Validated ReviewerIncentivizedSource: G2 invite ![Gilbert G.](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Gilbert G.") GG Gilbert G. IT Manager -CTO/CISO Enterprise (\> 1000 emp.) 7/1/2025 "A powerful platform for building and deploying ML models efficiently" 4.5/5 What do you like best about Amazon SageMaker? End to end , Scalability and flexibility , Integration with AWS , ease of use , Model monitoring and debugging Review collected by and hosted on G2.com. What do you dislike about Amazon SageMaker? Cost management , challenging to customize or go beyond the pre-built functionalities , documentation clarity , A good understanding of ML and AWS is needed to fully utilize its capabilities Review collected by and hosted on G2.com. What problems is Amazon SageMaker solving and how is that benefiting you? The End to End workflow support handles everything from data prep to deployment. This means we can ingest, explore, train and evaluate models with just one environment to work it. I also really like that SageMaker takes care of the infrastructure, so we dont have to worry about setting up or managing servers. When it comes to deployment, it's very flexible Review collected by and hosted on G2.com. Show More Validated ReviewerIncentivizedSource: G2 invite ![Ranisha R.](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Ranisha R.") RR Ranisha R. Teaching Assistant Mid-Market (51-1000 emp.) 5/25/2025 "Excellent" 5/5 What do you like best about Amazon SageMaker? What I like best about Amazon SageMaker is its ability to manage the entire machine learning lifecycle in one integrated platform. It simplifies model building, training, and deployment while offering scalability and powerful tools like SageMaker Studio and automated model tuning. Review collected by and hosted on G2.com. What do you dislike about Amazon SageMaker? What I dislike about Amazon SageMaker is that its pricing can be complex and quickly become expensive, especially for long-running training jobs or large-scale deployments. Additionally, the learning curve can be steep for new users unfamiliar with AWS services and configurations. Review collected by and hosted on G2.com. What problems is Amazon SageMaker solving and how is that benefiting you? Amazon SageMaker solves key problems in data science and machine learning, such as managing infrastructure, automating model training and tuning, and streamlining deployment. This benefits me by accelerating the ML workflow, reducing time spent on setup and DevOps, and enabling faster experimentation and production-ready model delivery. Review collected by and hosted on G2.com. Show More Validated ReviewerSource: Organic ![Neeraj J.](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Neeraj J.") NJ Neeraj J. Technical Manager Enterprise (\> 1000 emp.) 7/4/2025 "Machine Learning Tool" 4.5/5 What do you like best about Amazon SageMaker? No code & Infra headaches. Fully Managed e2e. Review collected by and hosted on G2.com. What do you dislike about Amazon SageMaker? Cost complications and pricing. Migration in other cloud is bit challenging. Review collected by and hosted on G2.com. What problems is Amazon SageMaker solving and how is that benefiting you? Auto ML for rapid prototyping. Review collected by and hosted on G2.com. Show More Our network of Icons are G2 members who are recognized for their outstanding contributions and commitment to helping others through their expertise.G2 IconValidated ReviewerIncentivizedSource: G2 invite MU Muhamamd U. Individual Retail Small-Business (50 or fewer emp.) 8/23/2024 "Powering the Potential of AWS SageMaker in Data Science Projects" 4.5/5 What do you like best about Amazon SageMaker? It is highly scalable, very compute-powerful, very well integrated with most vendors' data warehouses and data lakes, and can be accessed in the browser. Review collected by and hosted on G2.com. What do you dislike about Amazon SageMaker? I can hardly make an estimate of the price calculation. Even though there is some tool called AWS pricing calculator, the list of available configurations doesn't show the number of configurations you can select while setting up the tool Studio and Notebook instances. Review collected by and hosted on G2.com. What problems is Amazon SageMaker solving and how is that benefiting you? I use AWS SageMaker daily for data science projects, where Studio and Notebook instances are mainly used as a prime development environment. Now, what makes this tool ideal, due to its being in the cloud, is that you can work around large volumes of data with the ability to scale and have more resources as needed with a simple click. Review collected by and hosted on G2.com. Show More Validated ReviewerSource: Organic Review from User Profile ## Questions about Amazon SageMaker? Ask real users or explore answers from the community Get practical answers, real workflows, and honest pros and cons from the G2 community or share your insights. [ Ask about Amazon SageMaker ](https://www.g2.com/products/amazon-sagemaker/discussions/new) VS Vaibhav Singal • Last activity about 6 years ago What is the best way to integrate Sagemaker models with Kubernetes? 0 Upvotes 1 [ Join the conversation ](https://www.g2.com/discussions/28784-what-is-the-best-way-to-integrate-sagemaker-models-with-kubernetes) GU Guest User What is Amazon SageMaker used for? 0 Upvotes 0 [ Join the conversation ](https://www.g2.com/discussions/what-is-amazon-sagemaker-used-for) [ View all Discussions ](https://www.g2.com/products/amazon-sagemaker/discuss) ## Pricing Insights Averages based on real user reviews. ### Time to Implement 2 months ### Perceived Cost $$$$$ [ View More Pricing Information ](https://www.g2.com/products/amazon-sagemaker/pricing) Amazon SageMaker Comparisons ![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/small_square/small_square_aeae116c52945fdecd7ed16d621cb315/gemini-enterprise-agent-platform.png "Product Avatar Image") Gemini Enterprise Agent Platform 4.3/5(744) [ Compare Now ](https://www.g2.com/compare/amazon-sagemaker-vs-gemini-enterprise-agent-platform) ![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/small_square/small_square_d9a7abebb513247473f9558b56431a72/tensorflow.jpg "Product Avatar Image") TensorFlow 4.5/5(138) [ Compare Now ](https://www.g2.com/compare/amazon-sagemaker-vs-tensorflow) ![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/small_square/small_square_f176b4154a751d10150daa67a57b7dc5/azure-machine-learning-studio.jpg "Product Avatar Image") Azure Machine Learning Studio 4.3/5(90) [ Compare Now ](https://www.g2.com/compare/amazon-sagemaker-vs-microsoft-azure-machine-learning) ##### ##### Amazon SageMaker Features Model Development Language Support Drag and Drop Pre-Built Algorithms Machine/Deep Learning Services Computer Vision Natural Language Processing Natural Language Generation Deployment Managed Service Application Scalability System Data Ingestion & Wrangling [ View More Features ](https://www.g2.com/products/amazon-sagemaker/features) ##### Categories on G2 [Data Science and Machine Learning Platforms](https://www.g2.com/categories/data-science-and-machine-learning-platforms)[Generative AI Infrastructure](https://www.g2.com/categories/generative-ai-infrastructure)[MLOps Platforms](https://www.g2.com/categories/mlops-platforms) [Data Science and Machine Learning Platforms](https://www.g2.com/categories/data-science-and-machine-learning-platforms)[Generative AI Infrastructure](https://www.g2.com/categories/generative-ai-infrastructure)[MLOps Platforms](https://www.g2.com/categories/mlops-platforms)[Low-Code Machine Learning Platforms](https://www.g2.com/categories/low-code-machine-learning-platforms) [Show MoreShow Less](javascript:void(0);) ##### Explore More [What application shielding platforms offer runtime protection that deploys quickly with minimal integration effort for a startup or small team?](https://www.g2.com/discussions/what-application-shielding-platforms-offer-runtime-protection-that-deploys-quickly-with-minimal-integration-effort-for-a-startup-or-small-team)[Leading ipaas providers for app integrations](https://www.g2.com/discussions/what-are-the-leading-ipaas-providers-for-app-integrations)[Which are the most trusted enterprise service bus platforms by integration architects based on user reviews?](https://www.g2.com/discussions/which-are-the-most-trusted-enterprise-service-bus-platforms-by-integration-architects-based-on-user-reviews) [Which tools connect suspicious device fingerprints across global networks for real-time financial fraud prevention software?](https://www.g2.com/discussions/which-tools-connect-suspicious-device-fingerprints-across-global-networks-for-real-time-financial-fraud-prevention-software)[What is the top media and influencer targeting software for agencies?](https://www.g2.com/discussions/what-is-the-top-media-and-influencer-targeting-software-for-agencies)[Pros and Cons Details](https://www.g2.com/products/amazon-sagemaker/reviews?qs=pros-and-cons) [Show MoreShow Less](javascript:void(0);)