Mainframe Managed Service Providers (MSPs) have built impressive capabilities over the last several decades. They excel at infrastructure management, code conversion, and supporting complex hosting environments. Many have invested millions in advanced tools for code parsing, refactoring, and other technical aspects of mainframe management and modernization. Yet despite these strengths, MSPs consistently encounter the same bottlenecks that threaten mainframe modernization project timelines, profit margins, and client satisfaction.
In this article, we’ll explore the most common gaps MSPs face, how Zengines platform helps fill those gaps, and why Mainframe MSPs are partnering with Zengines.
The Critical Gaps in Current MSP Toolkits
Gap 1: Business Rules Discovery
While MSPs have sophisticated tools for parsing and reverse engineering COBOL code—they can extract syntax, identify data structures, and map technical dependencies—they lack capabilities for intelligent business logic interpretation. These parsing tools tell you what the code does technically, but not why it does it from a business perspective.
Current approaches to understanding the embedded business rules within parsed code require:
- Manual analysis by teams of expensive mainframe experts and business teams to interpret the business meaning behind technical code structures
- Line-by-line COBOL review to understand calculation logic and translate technical operations into business requirements
- Manual correlation between parsed code elements and actual business processes
- Expert interpretation of conditional logic to understand business rules and decision trees
- Guesswork about the original business intent behind complex branching statements and embedded calculations
Even with advanced parsing capabilities, MSPs still need human experts to bridge the gap between technical code structure and business logic understanding. This discovery phase often represents 30-40% of total project time, yet MSPs have limited tools to accelerate the critical transition from "code parsing" to "business intelligence."
The result: MSPs can quickly identify what exists in the codebase, but still struggle to efficiently understand what it means for the business—creating a bottleneck that no amount of technical parsing can solve.
Gap 2: Data Migration Tools
A critical step in any mainframe modernization project involves migrating data from legacy mainframe systems to new modern platforms. This data migration activity often determines project success or failure, yet it's where many MSPs face their biggest challenges.
While MSPs excel at physical data ETL and have tools for moving data between systems, they struggle with the intelligence layer that makes migrations fast, accurate, and low-risk:
- Manual data mapping between legacy mainframe schemas and modern system structures—a time-intensive process due to guesswork of trial-and-error
- Insufficient data quality analysis relevant to the business context, leading to "garbage in, garbage out" scenarios in the new system
- Manual transformation rule creation that requires expensive technical resources and extends project timelines
- Limited validation capabilities to ensure data integrity during and after migration
- Rules-based tools that constantly need to be adjusted when the data doesn’t fit the expected pattern - more often then not, this is the case.
These gaps expose organizations to costly risks: project delays, budget overruns, compromised data integrity, and client dissatisfaction from failed transfers. Delays and cost overruns erode margins and strain client relationships. Yet the most significant threat remains post-go-live discovery of migration mistakes. Today’s approach of manual processes are inherently time-constrained—teams simply cannot identify and resolve all issues before deployment deadlines. Unfortunately, some problems surface only after go-live, forcing expensive emergency remediation that damages client trust and project profitability.
The result: MSPs can move data technically, but lack intelligence tools to do it efficiently, accurately, and with confidence—making data migration the highest-risk component of mainframe modernization projects.
Gap 3: Testing and Validation Blind Spots
Once data is migrated from mainframe systems to modern platforms, comprehensive testing and validation becomes critical to ensure business continuity and data integrity. This phase determines whether the migration truly preserves decades of embedded business logic and data relationships.
Without comprehensive understanding of embedded business logic and data interdependencies, MSPs face significant validation challenges:
- Incomplete test scenarios based on limited business rules knowledge—testing what they assume exists rather than what actually exists in the legacy system
- Inefficient testing based on what the code presents versus what actually exists within the data
- Manual reconciliation between old and new systems that's time-intensive, error-prone, and often misses subtle data discrepancies
- Inadequate validation coverage of complex business calculations and conditional logic that may only surface under specific data conditions
- Reactive testing methodology that discovers problems during user acceptance testing rather than proactively during migration validation
- Post-go-live surprises when undocumented logic fails to transfer, causing business process failures that weren't caught during testing
The consequences: validation phases that stretch for months, expensive post-implementation fixes, user confidence issues, and potential business disruption when critical calculations or data relationships don't function as expected in the new system.
The result: MSPs have inadequate and non-optimized testing where teams test what they think is important rather than what the business actually depends on.
How Zengines Fills These Critical Gaps
Zengines has built AI-powered solutions that directly address each of these critical gaps in MSP capabilities. Our platform works alongside existing MSP tools, enhancing their technical strengths with the missing intelligence layer that transforms good modernization projects into exceptional ones.
Zengines Mainframe Data Lineage: Bridging the Business Logic Intelligence Gap
While parsing tools can extract technical code structures, Zengines Mainframe Data Lineage translates that technical information into actionable business intelligence:
- Automated Business Logic Interpretation: Our AI doesn't just parse COBOL—it understands what the code means from a business perspective. Zengines automatically identifies calculation logic, conditional business rules, and decision trees, then presents them in business-friendly visualizations that eliminate the need for manual interpretation.
- Intelligent Business Rules Extraction: Transform months of manual analysis into minutes of automated discovery. Zengines maps the relationships between data elements, business processes, files, and embedded logic, creating comprehensive documentation of how your mainframe actually implements business requirements.
- Visual Logic Flow Mapping: Interactive visualizations show not just technical data flow, but business process flow—helping teams understand why certain calculations exist, when conditional logic triggers, and how business rules cascade through the system.
- Contextual Information based on Analyst Research: Automatically generates business-readable information that explains the "why" behind technical code, enabling business analysts and technical teams to collaborate effectively without requiring scarce mainframe expertise.
MSP Impact: Transform your longest project phase into your fastest. Business logic discovery that previously required months and years of expert time now completes in days with comprehensive information that your entire team can understand and act upon.
Zengines AI Data Migration: Solving the Data Intelligence and Migration Gap
Our AI Data Migration platform transforms data migration from a risky, manual process into an intelligent, automated workflow:
- Intelligent Schema Discovery and Mapping: AI algorithms automatically understand both source mainframe schemas and target system structures, then predict optimal field mappings with confidence scoring. No more guesswork—get AI-recommended mappings that you can validate and refine.
- Automated Data Quality Analysis: Comprehensive data profiling identifies quality issues, completeness gaps, and anomalies before migration begins. Address data problems proactively rather than discovering them post-go-live.
- AI-Powered Transformation Rule Generation: Describe your transformation requirements in plain English, and Zengines Transformation Assistant generates the precise transformation syntax. Business analysts can create complex transformation rules without needing technical programming expertise.
- Automated Load File Generation: Execute data mapping and transformation into ready-to-use load files that integrate seamlessly with your existing migration tools and processes.
MSP Impact: Accelerate data migration timelines by 80% while dramatically reducing risk. Business analysts become 6x more productive, and data migration transforms from your highest-risk project component to a predictable, repeatable process.
Comprehensive Testing and Validation: Closing the Validation Loop
Zengines doesn't just help with discovery and migration—it ensures successful validation:
- Business Rules-Based Test Scenario Generation: Because Zengines understands the embedded business logic, it informs test scenarios that cover the actual business rules in the legacy system, not just the ones you know about.
- Automated Data Reconciliation: Zengines offers helpful tools like reconciliation to expose discrepancies between source and target systems, with intelligent filtering that highlights meaningful differences while ignoring irrelevant variations.
- Comprehensive Audit Trail: Complete documentation of what was migrated, how it was transformed, and validation that it works correctly—providing confidence to stakeholders and regulatory compliance where needed.
MSP Impact: Transform validation from an uncertain phase into a systematic process that focuses on exceptions. Reduce validation timelines by 50% while dramatically improving coverage and reducing post-go-live surprises.
The Integrated Zengines Advantage
Unlike point solutions in the mainframe modernization ecosystem that address isolated problems, Zengines provides an integrated platform where business logic discovery, data migration, and validation work together seamlessly:
- Light into the Black Box: Transparency into the most challenging system in the enterprise: mainframe and midranges.
- Connected Intelligence: Business rules discovered in the lineage phase automatically inform data migration mapping and validation scenarios
- End-to-End Visibility: Complete traceability from original business logic through final validation, providing unprecedented project transparency
- Unified Data Source: Single source of truth for business rules, data transformations, and validation results
This integrated approach transforms modernization from a series of risky, disconnected phases into a cohesive, intelligent process that dramatically improves outcomes while reducing timelines and risk.
The Business Impact: Why MSPs are Choosing Zengines
Accelerated Project Delivery
MSPs can deliver 50% faster overall project completion times. The discovery and data migration phases—traditionally the longest parts of modernization projects—now complete in a fraction of the time.
Improved Profit Margins
By automating the most labor-intensive aspects of modernization, MSPs can deliver projects with fewer billable hours while maintaining quality. This directly improves project profitability.
Enhanced Client Experience
Clients appreciate faster time-to-value and reduced business disruption. Comprehensive business rules documentation also provides confidence that critical logic won't be lost during migration.
Competitive Differentiation
MSPs with Zengines capabilities can bid more aggressively on timeline and cost while delivering superior outcomes. This creates a significant competitive advantage in the marketplace.
Risk Mitigation
Better understanding of business logic before migration dramatically reduces post-implementation surprises and costly remediation work.
The Strategic Imperative
As the mainframe skills shortage intensifies—with 70% of mainframe professionals retiring by 2030—MSPs face an existential challenge. Traditional manual approaches to business rules discovery and data migration are becoming unsustainable.
The most successful MSPs will be those that augment their technical expertise with AI-powered intelligence. Zengines provides that intelligence layer, allowing MSPs to focus on what they do best while dramatically improving client outcomes.
The question isn't whether to integrate AI-powered data intelligence into your modernization methodology. The question is whether you'll be an early adopter who gains competitive advantage, or a late adopter struggling to keep pace with more agile competitors.
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The "I" in CIO has always stood for Information, but in 2026 that responsibility takes on new urgency.
As the market pours resources into AI and enterprises face mounting pressure to manage it - whether deploying it internally, partnering with third parties who use it, or satisfying regulators who demand clarity on its use - the CIO's priority isn't another technology platform. It's data lineage and provenance as an unwavering capability.
This is what separates CIOs who treat technology management as an operational function from those who deliver trustworthy information as a strategic outcome.
Three Industry Drivers Making Data Lineage Urgent
Three industry drivers make this imperative urgent:
First, AI's transformative impact on business: Gartner reports that, despite an average spend of $1.9 million on GenAI initiatives in 2024, less than 30% of AI leaders report their CEOs are happy with AI investment return—largely because organizations struggle to verify their data's fitness for AI use.
Second, the massive workforce retirement in legacy technology: 79% cited their top mainframe-related challenge is acquiring the right resources and skills to get work done, according to Forrester Research, as seasoned experts retire and take decades of institutional knowledge about critical data flows with them.
Third, the ever-increasing regulatory landscape: Cybersecurity vulnerabilities, data governance, and regulatory compliance are three of the most common risk areas expected to be included in 2026 internal audit plans, with regulators demanding verifiable data lineage across industries.
As the enterprise's Information Officer, the CIO must be accountable for the organization's ability to produce and trust information - not just operate technology systems. Understanding the complete journey of data, from origin through every transformation to final use, supports every strategic outcome CIOs need to deliver: enabling AI capabilities, satisfying regulatory requirements, and partnering confidently with third parties. Data lineage provides the technical foundation that makes trustworthy information possible across the enterprise.
The Burning Platform: Why CIOs Must Act Now
Three forces converge to create a burning platform:
First, regulatory compliance demands now span every industry - from BCBS-239 and DORA in financial services to HIPAA in healthcare to SEC analytics requirements across public companies. Regulators are enforcing data lineage mandates with substantial penalties.
Second, every business needs to demonstrate AI innovation, yet AI initiatives succeed or fail based on verified training data quality and explainability.
Third, in a connected world demanding "always on," enterprises must be agile enough to globally partner with third parties, whether serving customers through partner ecosystems or trusting data from their own vendors and service providers.
The urgency intensifies because mainframe systems house decades of critical business logic while the workforce that understands these systems is retiring, making automated lineage extraction essential before institutional knowledge disappears.
What Enterprise-Wide Data Lineage Capability Requires
Given these converging pressures, CIOs need enterprise-wide data lineage capability that captures information flows across the entire technology landscape, including legacy systems. This means automated lineage extraction from mainframes, mid-tier applications, cloud platforms, and third-party integrations - creating a comprehensive map of how data moves and transforms throughout the organization.
Manual documentation fails because it can't keep pace with system complexity and depends on human compliance. The solution requires technology that captures lineage at the technical level where data actually flows, then makes this intelligence accessible for business understanding.
For mainframe environments specifically, this means extracting lineage from COBOL and RPG code before retiring experts leave. The strategic outcome: a single, verifiable source of truth about data provenance that serves regulatory needs, AI development, and partnership confidence simultaneously.
From Operational Execution to Strategic Accountability
This shift elevates the CIO's accountability from operational execution to strategic outcomes. Rather than simply providing systems, CIOs become accountable for the infrastructure that proves information integrity and lineage.
This transforms conversations with boards and regulators from "we operate technology systems" to "we can verify our information's complete journey and quality"—a fundamentally stronger position.
The CIO role expands from technology delivery to information assurance, directly supporting enterprise risk management, innovation initiatives, and strategic partnerships through verifiable capability.
Three Strategic Business Outcomes from Data Lineage
Ultimately, data lineage capability delivers three strategic business outcomes:
- Regulatory compliance transforms from expensive fire drills into routine capability—examiners receive complete, accurate lineage documentation on demand across multiple industry requirements.
- AI and analytics initiatives launch faster with confidence because teams can verify training data quality, understand transformations, and explain model inputs to stakeholders and regulators.
- Third-party partnerships expand safely because the enterprise can verify data quality across organizational boundaries, whether integrating partner data to serve customers or trusting vendor information for operations.
The enterprise moves from defensive compliance postures to offensive information leverage, with the CIO providing infrastructure that turns data into a strategic asset rather than a regulatory liability.
For CIOs in 2026, owning Information means proving it - and data lineage is what makes that promise possible.
To learn more about how Zengines can support your data lineage priorities, schedule a call with our team.
Every enterprise eventually faces a pivotal question: should we connect our systems together, or move our data to a new home entirely? The answer seems simple until you're staring at a 40-year-old mainframe with dwindling support, a dozen point solutions held together by ever-growing integrations, and a budget that doesn't accommodate mistakes.
Data migration and data integration are often confused because they both involve moving data. But they serve fundamentally different purposes - and choosing the wrong approach can cost you years of technical debt, millions in maintenance, or worse, a failed transformation project.
The Fundamental Difference
Data migration is about transition and consolidation.
Systems reach end-of-life. Platforms get replaced. Acquisitions require consolidation. Companies outgrow their technology stack and need to move from functionally siloed point solutions to consolidated platforms.
Migration addresses all of these - relocating data from a source system to a target, transforming it to fit the new data model, then retiring the source. The result is a cleaner footprint: fewer systems, fewer dependencies, a tidier architecture.
Data integration is about coexistence.
You're connecting systems so they can share data continuously, in real-time or near-real-time. Both systems stay alive. Think of it like building a bridge between two cities - traffic flows both directions, indefinitely.
On the surface, integration can seem more appealing - it preserves optionality and avoids the hard decision of retiring systems. But optionality has carrying costs. Every bridge you build is a bridge you must maintain, monitor, and update when either system changes. Migration delivers a leaner architecture with less operational overhead.
When Migration Is the Right Choice
Migration makes sense when you're ready to consolidate and simplify - especially for operational systems.
Consider migration when:
When Integration Is the Right Choice
Integration makes sense when systems genuinely need to coexist and communicate -- particularly for analytical use cases.
Consider integration when:
The Hidden Costs of Each Approach
Migration: Historically Front-Loaded - But That's Changing
Migration projects have traditionally been expensive upfront. Research shows that over 80% of data migration projects run over time or budget. A 2021 Forbes analysis found that 64% of data migrations exceed their forecast budget, with 54% overrunning on time.
But here's what those statistics don't capture: much of this cost and risk stems from outdated approaches to migration. Legacy migration projects often relied on manual analysis, hand-coded transformation scripts, and armies of consultants reverse-engineering undocumented systems. The migration itself wasn't inherently expensive - the lack of proper tooling made it expensive.
When migration succeeds, you have a clean slate. The old system is retired. There's no pipeline to maintain, no nightly sync jobs to monitor, no integration layer to update when either system changes. You've reduced your technology footprint.
Integration: Lower Entry Cost, Compounding Maintenance
Integration appears easier at first. You're not touching the legacy data - you're just building a bridge. The upfront cost looks manageable. But that bridge requires constant attention.
According to McKinsey, the "interest" on technical debt includes the complexity tax from "fragile point-to-point or batch data integrations." Engineering teams spend an average of 33% of their time managing technical debt, according to research from Stripe. When you build an integration instead of migrating, you're committing to that maintenance indefinitely.
Gartner estimates that about 40% of infrastructure systems across asset classes already carry significant technical debt. Organizations that ignore this debt spend up to 40% more on maintenance than peers who address it early.
The key insight: integration's "lower cost" is an illusion if you only look at upfront spend. When you factor in total cost of ownership - years of maintenance, incident response, and the opportunity cost of engineers maintaining pipes instead of building value - the calculus often favors migration.
The Real Trade-Off: Optionality vs. Simplicity
Integration preserves optionality. You can defer the retirement decision. You can keep both systems running while you figure out the long-term strategy. But optionality has carrying costs, and those costs compound over time.
Migration forces a constraint - and constraints drive clarity. When you commit to migration, you're forced to answer hard questions: What data do we actually need? What's the canonical source of truth? What business rules should govern this data going forward? The result is a tidier, more intentional data architecture.
Many organizations choose integration because migration feels too hard. But "too hard" often means "too hard to decide." Integration lets you defer decisions. Migration forces them - and in doing so, delivers a cleaner outcome.
A Framework for Deciding
Ask yourself these questions:
- Is this an operational system or an analytical use case? Operational systems benefit from migration's cleaner footprint -- fewer moving parts means faster troubleshooting and simpler maintenance. Analytical use cases often fit integration naturally, since you're aggregating data for reporting rather than running day-to-day operations.
- Is the source system being retired? If yes, you need migration. Integration with a system you're decommissioning is just deferred work.
- Are you consolidating multiple systems into one platform? If yes, you need migration. You can't integrate your way into a different data model -- the data has to move and transform.
- Do both systems genuinely need to stay alive? If yes, and they serve truly different purposes with no consolidation path, integration makes sense.
- What's your appetite for ongoing maintenance? Integration is a subscription you pay forever. Migration is a one-time investment with long-term dividends.
- What does compliance require? If regulators need data to physically reside in a specific system, integration won't satisfy that requirement.
The Bottom Line
For years, integration was perceived as the lesser evil - not because it was the right choice, but because migration seemed too expensive and risky. Organizations built integrations they didn't really want because migration felt out of reach.
That calculation is changing. Modern migration platforms are lowering the barrier to making the right choice - automating the analysis, transformation, and validation work that used to require armies of consultants. When migration's entry cost drops, total cost of ownership (TCO) becomes the deciding factor. And on TCO, migration often wins.
If you're modernizing legacy systems, consolidating point solutions into an ERP, or keeping operational systems lean for faster troubleshooting, migration gives you a cleaner footprint and eliminates technical debt. Yes, it requires commitment upfront. But you're trading short-term focus for long-term simplicity.
If you're feeding analytical systems, connecting platforms that both serve ongoing purposes, or need real-time data flow between coexisting systems, integration is the right tool. Just go in with your eyes open about the maintenance commitment you're making.
The worst outcome is choosing integration because migration seemed too hard - and then spending the next decade maintaining pipes to systems you should have retired years ago.
Zengines is an AI-native data migration platform built to lower the barrier to making the right choice. If you're weighing migration against integration - or stuck maintaining integrations you wish were migrations - we'd love to show you what's now possible. Let's talk.
If you're evaluating Zengines for your data migration or data lineage projects, one of your first questions is likely: "Where will this run, and where will our data live?"
It's a critical question. Data migrations involve your most sensitive information, and your choice of deployment architecture impacts everything from security and compliance to speed-to-value and ongoing management.
The good news? Zengines offers four deployment options designed to meet different organizational needs. This guide will help you understand each option and identify which might be the best fit for your situation.
Understanding Your Deployment Options
Option 1: Zengines Hosted (AWS US Region)
What it is: Fully managed SaaS deployment in US-based AWS data centers
Who it's designed for:
- Organizations based primarily in the United States
- Teams who need to start analyzing and migrating data quickly
- Teams who are focusing on their business transformation and don’t want to manage all the moving pieces associated with data migrations
- Projects where regulatory requirements don't mandate specific data residency
Key benefits:
- Fastest time to value: You can typically begin working with your data within days of signing up
- Zero infrastructure overhead: No need to provision servers, manage updates, or monitor performance—Zengines handles all of that
- Predictable, straightforward pricing: Standard subscription model with no infrastructure management costs
What to consider: If your organization has data sovereignty requirements (especially for EU data), strict requirements about data leaving your environment, or compliance frameworks that restrict US-based cloud processing, one of the other options below may be a better fit.
Option 2: Zengines Hosted (AWS Non-US Region)
What it is: Fully managed SaaS deployment in your preferred AWS region (EU, APAC, etc.)
Who it's designed for:
- International organizations with regional data residency requirements
- Companies subject to GDPR or other regional data protection regulations
- Teams who want managed SaaS simplicity without US jurisdiction concerns
Key benefits:
- Regional compliance: Meets data sovereignty requirements while maintaining all Zengines capabilities
- Same fast deployment: No compromise on speed or features compared to US hosting
- Still fully managed: Zengines continues to handle all infrastructure, updates, and monitoring
What to consider: While this addresses data residency, it's still a multi-tenant architecture with data processed in Zengines' cloud environment. If your compliance framework requires dedicated infrastructure or data that never leaves your environment, consider Option 3.
Option 3: Zengines Deployed on Your AWS Cloud Account
What it is: Zengines deployed entirely within your own AWS environment under your control
Who it's designed for:
- Financial services, healthcare, and government organizations with stringent compliance requirements
- Enterprises with security frameworks that prohibit multi-tenant SaaS or require tenant isolation at the account level
- Organizations that need administrative control over the compute environment and network boundaries
- Companies with mature AWS environments and DevOps capabilities
Key benefits:
- Complete data sovereignty: Your data never leaves your environment
- Maximum control: You define and enforce all security policies, access controls, and compliance measures
- Dedicated infrastructure: No multi-tenant concerns; this is your exclusive Zengines instance
- Integration with your security tools: Deploy within your existing security perimeter and monitoring systems
What to consider:
- Setup time: Deployment typically takes 2-3 weeks rather than days
- Resource requirements: Your IT team needs to provision AWS resources and support the deployment
- Additional costs: This option includes additional support fees for Zengines to assist with deployment, configuration, and optimization
- Prerequisites: You'll need an existing AWS environment and team members familiar with managing AWS infrastructure
Technical requirements: Zengines will provide detailed specifications for EC2 instances, storage, and AWS services needed. Having this conversation early with your infrastructure team helps ensure smooth deployment.
Option 4: Zengines on Azure or Google Cloud Platform (In Development)
What it is: Private cloud deployment on your Azure or GCP environment
Who it's designed for:
- Organizations with significant commitments to Azure or Google Cloud
- Companies whose cloud strategy or enterprise agreements make AWS deployment impractical
Current status: As of September 2025, multi-cloud support is in active development. If your organization has strong Azure or GCP requirements, we'd welcome a conversation about timeline and potential early adopter partnerships.
What to consider: If you need Zengines capabilities today and your only concern is cloud platform, Option 3 (AWS Cloud Account) might serve as a bridge solution until your preferred platform is supported.
Making Your Decision: Key Questions to Ask
As you evaluate which deployment option fits your needs, consider these questions:
Regulatory and Compliance:
- Do we have specific data residency requirements (geographic restrictions on where data can be processed)?
- Are we subject to regulations like GDPR, HIPAA, or financial services compliance frameworks?
- Does our compliance framework require dedicated infrastructure?
Infrastructure and Resources:
- Do we have an existing AWS, Azure, or GCP environment?
- Do we have DevOps or infrastructure team members who can support Zengines deployment on our cloud account?
- What's our organizational comfort level with managing cloud infrastructure?
Timeline and Urgency:
- How quickly do we need to begin analyzing and migrating data?
- Is a 2-3 week deployment timeframe acceptable, or do we need to start within days?
Security Requirements:
- Does our security framework allow data processing in external cloud environments?
- Do we require dedicated infrastructure, or is secure multi-tenant architecture acceptable?
- What level of control do we need over the processing environment?
Budget Considerations:
- What's our budget for not just software licensing but also infrastructure support?
- Do we have budget for the additional support costs associated with private cloud deployment?
Comparing Your Options at a Glance
What Happens After You Choose?
- For Options 1 & 2 (Zengines Hosted): After you sign up, you'll receive access credentials within 1-2 business days. You can immediately begin creating projects, uploading schemas, and working with data. Our team will schedule an onboarding and training session to help you get started.
- For Option 3 (Your AWS): We'll schedule a technical workshop with your infrastructure team to review requirements, discuss your AWS environment, and plan the deployment. Zengines will provide detailed specifications and work alongside your team through the setup process. Once deployed, you'll receive training for both end users and administrators.
- For Option 4 (Azure/GCP): If you're interested in Azure or GCP deployment, let's have a conversation about your timeline and requirements to better estimate the development effort.
Getting Started
Choosing the right deployment architecture is an important decision, but it shouldn't slow down your evaluation. Here's how to move forward:
- Start with an assessment of your compliance, security, and resource requirements using the questions above
- Have a conversation with our team about your specific situation—we've helped dozens of organizations navigate this decision
- Involve your stakeholders early: Security, compliance, and infrastructure teams should be part of the conversation from the beginning
- Consider a phased approach: Some organizations start with Option 1 or 2 for initial projects, then move to Option 3 as they expand usage
- Don't let deployment questions stop progress: We can work with you on pilot projects using sample data while larger deployment decisions are being made
Data migration and mainframe modernization are complex enough without worrying about whether your tools can work within your architecture. Zengines' flexible deployment options mean you don't have to compromise between the capabilities you need and the compliance, security, or infrastructure requirements you must meet.
Whether you need to start analyzing data tomorrow (hosted options) or require complete control within your own infrastructure (private cloud), there's a path forward.
Ready to discuss which deployment option fits your needs? Contact our team to start the conversation. We'll ask the right questions, understand your requirements, and help you make a confident decision.
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