Sold by: Sparkflows.io
Deployed on AWS
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Sparkflows is an enterprise-grade, self-service platform for Data Analytics, AI Agents, Generative AI, and Data Science, turning enterprise data into insights, intelligent automation, and AI-powered applications up to 15x faster. With a visual no-code/low-code environment, teams can build AI agents, GenAI applications, RAG solutions, dashboards, ML models, and data workflows on a unified platform. Sparkflows integrates with AWS services including Amazon Bedrock, SageMaker, EMR, S3, Redshift, and Glue, accelerating the journey from data to analytics, AI, and production.
Overview
Sparkflows is an enterprise-grade, self-service AI and data platform for building AI Agents, Generative AI applications, Data Analytics, Data Science, Data Engineering workflows, and Business Intelligence solutions on a unified, low-code/no-code platform. Deployed via AMI on Amazon EC2, Sparkflows enables technical and business teams to move from enterprise data to analytics, AI, and intelligent automation without extensive coding. With 450+ pre-built processors, 50+ ready-to-use AI agents, and pre-built agent templates, teams can accelerate solution AI Agents & Intelligent Automation Build and orchestrate intelligent agents using Sparkflows' visual Agent Builder and drag-and-drop workflow designer. Create multi-step and multi-agent workflows that combine LLMs, RAG, enterprise data, ML models, APIs, and business logic. Pre-built agent templates enable teams to quickly deploy and customize solutions across Sales, Marketing, Customer Support, Analytics, IT, and other business functions. Data Analytics & BI Give business and analytics teams a self-service environment for data preparation, blending, exploration, visualization, dashboards, KPIs, and analytical applications. Users can move from raw enterprise data to actionable insights without depending on specialized development teams. Data Science & ML Build, train, deploy, and operationalize machine learning models using H2O, Scikit-learn, SparkML, and XGBoost. Support classification, regression, clustering, NLP, experimentation, and MLOps, with ML models available directly within analytics and AI agent workflows. Data Engineering Design, automate, and scale data pipelines using visual workflows and push-down compute across AWS services. Support data preparation, transformation, quality, scheduling, lineage, and governance across structured and unstructured data. AWS Integration Built for AWS, Sparkflows integrates across the AWS data, AI, and compute ecosystem. Run workloads on Amazon EC2, EMR, and AWS Glue; connect to Amazon S3, Redshift, and Kinesis; build and deploy ML models with SageMaker; and leverage foundation models through Amazon Bedrock. Sparkflows brings these services together through a unified visual development environment.
Highlights
- Unlock more value from your AWS data with Sparkflows. Combine Data Analytics, BI, AI Agents, Generative AI, and Data Science in a self-service, low-code/no-code platform. Build agents, automate workflows, create dashboards, and develop AI/ML applications faster. Sparkflows integrates with Amazon Bedrock, SageMaker, EMR, S3, Redshift, Glue, and Kinesis, helping teams turn enterprise data into insights, intelligent automation, and production-ready AI all from one unified platform.
- Connect enterprise data through Amazon S3, Redshift, and Kinesis. Run workloads on EC2, EMR, and AWS Glue. Build and deploy ML models with SageMaker and power GenAI and AI agents with Amazon Bedrock all from a unified platform.
- Deploy governed AI and analytics with role-based access, audit trails, monitoring, and enterprise security. SOC 2, ISO/IEC 27001:2022, and HIPAA certifications support enterprise requirements, with 90+ industry accelerators across BFSI, Healthcare, CPG, Manufacturing, and more.
Details
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Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
AmazonLinux Amazon Linux 2023
Deployed on AWS
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Pricing
Free trial
Try this product free for 21 days according to the free trial terms set by the vendor. Usage-based pricing is in effect for usage beyond the free trial terms. Your free trial gets automatically converted to a paid subscription when the trial ends, but may be canceled any time before that.
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time. Alternatively, you can pay upfront for a contract, which typically covers your anticipated usage for the contract duration. Any usage beyond contract will incur additional usage-based costs.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Cost/hour |
|---|---|
m4.xlarge Recommended | $0.45 |
r5.12xlarge | $0.55 |
r5.metal | $0.55 |
r5.8xlarge | $0.55 |
m3.xlarge | $0.35 |
m4.10xlarge | $0.65 |
m4.16xlarge | $0.65 |
r5.24xlarge | $0.55 |
m4.2xlarge | $0.65 |
r5.4xlarge | $0.45 |
Dimensions summary
You pay by the hour for the EC2 instance type you run the platform on. Pricing scales with the compute size you pick. The t2 and t3 2xlarge options are general-purpose burstable instances for lighter workloads. The m3 and m4 families are general-purpose instances across a range of sizes, from large up to 16xlarge. The r5 family covers memory-optimized instances, from xlarge up to 24xlarge and bare metal. As you move to larger instances or memory-optimized types, the hourly rate rises with added compute and memory capacity.
Top-of-mind questions for buyers
Each unit is one running EC2 instance of the type you choose, billed per hour. The rate covers the Sparkflows software running on that instance. Underlying AWS compute is billed separately by AWS. You pick the instance family and size that fits your workload.
Hourly software charges apply only while an instance runs. Stopped instances stop accruing software charges. Note that AWS may still bill storage tied to a stopped instance, but the Sparkflows software meters running hours only.
The t2, t3, m3, and m4 families are general-purpose instances suited to lighter or balanced workloads. The r5 family is memory-optimized, giving more memory per instance for data-heavy pipelines and models. Choose based on how memory-intensive your analytics and AI workloads are.
www.sparkflows.ai
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Please contact us at support@sparkflows.io if there is need for refund.
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Delivery details
64-bit (x86) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
The latest Sparkflows release (up to version 3.3.30) brings significant improvements across AI, security, workflow orchestration, and integrations.
Key highlights include:
- AI & Copilot: AI & Copilot: Natural language querying, chatbot export/import, and enhanced Copilot capabilities
- Feedback button for AI responses
- Admin visibility into feedback and reported issues
- Email notifications for issue reporting
- AI-generated content disclaimer
- Security: Dedicated CORS Configuration Page : A new CORS Settings page has been implemented for managing cross-origin configurations centrally.
- Workflow Enhancements: Dynamic EMR scaling, MWAA SSL control, SFTP & Email nodes
- Data & Integrations: Support for Confluence, ServiceNow, SharePoint, and Pinecone
- Databricks Expansion: Advanced operators for clusters, jobs, and notebooks
- Pipeline Orchestration: Workflow-to-workflow parameter passing using ${NAME}
Additional details
Usage instructions
Sparkflows is running on port "8080 for http & 8443 for https" when instance is launched. Access it in your browser by going to:
- http://INSTANCE_PUBLIC_ADDRESS:8080
- https://INSTANCE_PUBLIC_ADDRESS:8443
- For HTTPS URL to work, Port HTTPS(443) & 8443 Should be open Login with below to get started:
- Username : admin
- Password : instance id of your machine Administrative (command-line) access can be obtained through ssh ec2-user@INSTANCE_PUBLIC_ADDRESS. Sparkflows runs under linux user account "ec2-user". For additional information, or any issue, please see our docs at https://docs.sparkflows.io/en/latest/user-guide/index.html For Compute Connection Integration, please see our docs at https://docs.sparkflows.io/en/latest/user-guide/connection/compute-connection/index.html
Support
Vendor support
Sparkflows offers multiple support channels to help customers get up and running quickly and resolve issues at any stage:
Documentation: Full installation, administration, and user guides at docs.sparkflows.ai Video Tutorials: Step-by-step walkthroughs at sparkflows.ai/videos Community Forum: Peer and expert support at community.sparkflows.ai Direct Support: Raise issues and contact the Sparkflows team at sparkflows.ai/contact-us Professional Services: Available for onboarding, implementation, and custom AI solution development
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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