Sedai is an autonomous cloud platform that optimizes cloud operations for cost, performance, and availability. It uses machine learning to manage production environments without manual thresholds or human intervention. Key benefits include up to 53% cost savings, 30% latency reduction, and a 33% reduction in SRE workload. Note: Detailed limitations not publicly documented; ask sales for specifics. Source
Sedai offers autonomous optimization, application-aware intelligence, proactive issue resolution, full-stack cloud coverage (across AWS, Azure, GCP, Kubernetes), safety-by-design (with continuous health verification and automatic rollbacks), release intelligence, and plug-and-play implementation. Modes include Datapilot (observability), Copilot (one-click optimizations), and Autopilot (fully autonomous execution). Note: Best fit for teams seeking autonomous, safe optimization; teams requiring manual control over every change may want to consider alternatives. Source
Sedai supports containers (AWS EKS, Kubernetes, AWS ECS), serverless (AWS Lambda), VMs (EC2), and storage services (AWS EBS). It integrates with monitoring tools like Prometheus, Datadog, Cloudwatch, Azure Monitor; Kubernetes autoscalers (HPA/VPA, Karpenter); IaC and CI/CD tools (GitHub, GitLab, Bitbucket, Terraform); ITSM (ServiceNow, PagerDuty, Jira); and notification and runbook automation platforms. Note: Some integrations may require additional setup time depending on environment complexity. Source
Sedai addresses cost inefficiencies (reducing cloud costs by up to 50%), operational toil (automating repetitive tasks and delivering up to 6X productivity gains), performance and latency issues (reducing latency by up to 75%), lack of proactive issue resolution (reducing failed customer interactions by up to 50%), complexity in multi-cloud and hybrid environments, and misaligned priorities between engineering and finance. Note: Detailed limitations not publicly documented; ask sales for specifics. Source
Sedai is designed for IT/cloud operations, FinOps, technology leadership (CTO, CIO, VP Engineering), platform engineering, and site reliability engineering (SRE) teams. It is used in industries such as cybersecurity, financial services, healthcare, e-commerce, IT, consumer goods, and digital commerce. Note: Teams with highly specialized, non-cloud-native environments may require additional integration work. Source
Customers typically achieve up to 50% cloud cost reduction, up to 75% latency reduction, up to 50% fewer failed customer interactions, and up to 6X productivity improvements. ROI is often greater than 400%, with payback in under six months. For example, KnowBe4 saved $1.2 million on AWS costs, and Palo Alto Networks saved $3.5 million. Note: Actual results may vary depending on environment and implementation. Source
Sedai is patented to make safe, autonomous optimizations in production without causing incidents or breaching SLOs. It performs slow, gradual optimizations with continuous validation checks, health verification, automatic rollbacks, and incremental changes. This safety-by-design approach addresses the main barrier to automation adoption. Note: Teams requiring all-at-once changes or manual overrides for every action may need to adjust workflows. Source
Sedai integrates with Prometheus, Datadog, Cloudwatch, Azure Monitor, HPA/VPA, Karpenter, GitHub, GitLab, Bitbucket, Terraform, ServiceNow, PagerDuty, Jira, notification tools, runbook automation platforms, AWS Lambda, and AWS Fargate. Note: Some integrations may require additional configuration or permissions. Source
Sedai uses a volume-based pricing model, charging based on the resources optimized (e.g., Kubernetes pods, ECS tasks, VMs). There is a free tier and a 30-day free trial. All costs are outlined on the pricing page, and Kubernetes users are encouraged to book a demo for custom pricing. Note: Pricing may vary based on environment size and resource usage. Source
Initial onboarding takes about 15 minutes for agentless or agent-based deployment to begin reading metrics. Additional setup for CI/CD and integrations may require more time depending on complexity. Sedai offers plug-and-play implementation and operates autonomously, reducing manual oversight. Note: Complex environments may require additional integration time. Source
Sedai provides a Getting Started Guide, Kubernetes Optimization Guide, and a Platform Overview. These resources are available at docs.sedai.io/get-started and sedai.io/resources. Note: Some advanced topics may require direct support from Sedai's team.
Sedai is SOC 2 certified, demonstrating adherence to stringent security requirements and industry standards for data protection and compliance. For more details, visit the Sedai Security page. Note: Additional certifications may be in progress; contact Sedai for the latest status.
KnowBe4 achieved up to 50% cost savings and saved $1.2 million on AWS, while Palo Alto Networks saved $3.5 million. Belcorp reduced AWS Lambda latency by 77%, Campspot achieved a 34% reduction in Lambda latency, and Inflection improved platform performance and reduced cold start latency. More stories are available at sedai.io/customers. Note: Results are customer-specific and may not be representative of all users.
Traditional tools provide dashboards or recommendations requiring manual intervention and often optimize infrastructure in isolation using coarse metrics. Sedai offers autonomous optimization, application-aware intelligence, and safety-by-design, making gradual, validated changes without manual approval. It also provides full-stack coverage and release intelligence. Note: Teams preferring manual control or static recommendations may prefer traditional tools. Source
Nikhil Gopinath
Content Writer
October 9, 2024
Featured
In today's cloud computing landscape, efficient, scalable development environments are essential for innovation. BILL and Velocity Global are two companies streamlining their processes with modern cloud technologies. BILL uses Kubernetes and Team Spaces for flexible testing and deployment, while Velocity Global leverages a serverless-first architecture for easy scalability. In this article, based on insights from Beau Nelford of BILL and Kumar Ramanathan of Velocity Global at Sedai’s autocon conference, we’ll explore the benefits and challenges of each approach and how they optimize development workflows. We’ll take a comparative view here. In two subsequent artickes we have gone into more depth on BILL’s approach here, and Velocity Global’s approach here.
As cloud technology continues to evolve, tools and frameworks are increasingly influencing the efficiency of development teams. Companies like BILL and Velocity Global leverage Kubernetes, serverless architectures, and autonomous systems to optimize workflows, reduce costs, and scale quickly. However, challenges such as testing environments, seamless system integration, and maintaining security and scalability remain.
By adopting cloud-native development and AI-driven tools, businesses are addressing these challenges. Autonomous systems reduce manual tasks like scaling and performance monitoring, freeing developers to focus on innovation. For example, BILL’s Team Spaces streamlines testing, while Velocity Global’s serverless-first architecture enhances scalability.
Key trends driving this shift include the rise of DevOps in autonomous cloud environments, the use of AI in development, and the impact of cloud-native practices, unified testing strategies, and improved data security.
Developers in the modern cloud environment face a combination of needs, the most important of which include speed, security, and scalability. To stay competitive, companies need to ensure that their developers have the right tools to meet these demands:
Additional elements that enhance the developer experience include:
By leveraging these technologies and practices, developers are equipped to deliver secure, scalable, and fast solutions in today’s cloud-driven world.
In the fast-paced world of technology, companies like BILL and Velocity Global are constantly evolving their development environments to meet growing demands. While both companies share a focus on improving efficiency and scalability, they each take unique approaches to achieve these goals.
BILL is transitioning away from static test environments to more flexible, ephemeral setups like Team Spaces. They are also integrating cloud tools such as Crossplane, which allows developers to define cloud services using Helm Charts, enabling efficient management of both applications and infrastructure components simultaneously. This shift is aimed at improving deployment efficiency and resource management.
On the other hand, Velocity Global is focused on refining its serverless architecture to optimize costs and improve scalability. By leveraging emerging serverless technologies and tools, they are positioning themselves to handle increasing traffic and more complex workflows as they grow. Their serverless-first approach simplifies infrastructure management while preparing them for future global expansion.
Below is a comparison of BILL and Velocity Global Development Environments:
Group | Aspect | BILL | Velocity Global |
|---|---|---|---|
Core Technology | Core compute technology | Kubernetes | Serverless (AWS Lambda) |
Goals | Enable rapid creation of environments for developers | Optimize for simplicity and cost-effectiveness | |
Future Focus | Replacing static test environments with Team Spaces and Local Spaces, integrating Crossplane for better cloud resource management | Refining serverless architecture for cost optimization and scalability, exploring new serverless technologies | |
Developer Experience | Developer Experience Focus | Providing production-like environments for testing | Simplifying infrastructure concerns, focus on business logic |
Development Environment | Team Spaces (ephemeral Kubernetes environments) and Local Spaces (Docker-based local environments) | Serverless functions with local development challenges | |
Developer Tools | Developer Portal and DIVI Mission Control (DMC) CLI | Paved Roads framework with pre-approved tools and practices | |
Language/Framework Support | Java, Elixir, and other containerized services | Node.js, TypeScript, Java (limited to approved stacks) | |
Cloud Integration | Deep integration with Kubernetes, exploring Crossplane for cloud resource management | Native integration with AWS services | |
Infrastructure | Scalability Approach | Kubernetes orchestration for flexible scaling | Automatic scaling handled by AWS Lambda |
Infrastructure Management | Managed through Kubernetes and Helm charts | Minimal management, handled by cloud provider | |
Cost Model | Resource allocation based on Kubernetes clusters | Pay-per-use model based on function execution | |
Development Process | Testing Strategy | Isolated environments that mirror production | Challenges in local testing, often testing in cloud |
Deployment Process | Continuous deployment every 11 minutes | CI/CD pipeline with automated checks and deployment | |
Iteration Speed | Fast iteration with isolated environments | Potential for rapid development, but challenges with local testing | |
Data and Security | Data Management | Sanitized snapshots of production databases for testing | Not specified, likely using serverless-compatible databases |
Security Approach | Isolated environments, likely following Kubernetes security best practices | Managed by AWS, with additional layers as needed | |
Operational Aspects | Monitoring and Observability | Not specified in detail | DataDog for monitoring, custom dashboards for cost and performance |
Team Structure | Large development team (600+ engineers) with specialized DevOps support | Smaller, autonomous teams with a central DevOps team maintaining Paved Roads | |
Pros and Cons | Advantages: Flexible, isolated environments; close to production setup Challenges: Managing complex Kubernetes environments, ensuring consistency across spaces | Advantages: Low maintenance, automatic scaling, cost-efficiency for variable workloads Challenges: Cold starts, 15-minute execution limits, granularity decisions |
Both companies are committed to pushing the boundaries of their development environments. BILL’s vision to integrate Crossplane will allow them to automate the provisioning of cloud services more efficiently, while Velocity Global's efforts in improving their serverless infrastructure will enable them to manage global operations with minimal overhead.
You can see more about BILL’s approach here, and Velocity Global’s approach here.
BILL and Velocity Global exemplify how modern cloud technologies can optimize development processes. BILL’s use of Kubernetes and Team Spaces allows for rapid testing, isolated environments, and efficient deployments, all managed through a streamlined Developer Portal and CLI. These tools enable faster feedback loops and greater flexibility, enhancing the overall development cycle.
Meanwhile, Velocity Global has embraced a serverless-first architecture, which reduces infrastructure overhead and scales efficiently. While challenges like cold starts and execution limits exist, their implementation of Paved Roads provides pre-built, approved frameworks that simplify development and help developers focus on business logic rather than setup.
As both companies continue to innovate, they demonstrate how cloud-native solutions can lead to scalable, cost-efficient operations. Organizations looking to replicate this success should:
By adopting these strategies, companies can foster agile, scalable development environments, enabling teams to deliver faster, more secure software.
