Total Economic Impact
Cost Savings And Business Benefits Enabled By Anaconda
A Forrester Total Economic Impact™ Study Commissioned By Anaconda, April 2025
Total Economic Impact
A Forrester Total Economic Impact™ Study Commissioned By Anaconda, April 2025
Forrester research states that “by using open-source, firms can accelerate AI initiatives, reduce costs, and increase architectural openness to create a more dynamic and inclusive tech ecosystem.”1 However, managing open-source packages presents significant challenges, including ensuring security, maintaining operational efficiency, and achieving compliance. A comprehensive solution to these challenges must offer secure, efficient, and compliant package management. This study highlights why decision-makers should consider solutions that can safeguard operations, enhance productivity, and unlock open-source’s full potential, resulting in business growth.
The Anaconda AI Platform provides a comprehensive solution for managing and securing open-source packages, streamlining package security management, and ensuring compatibility across various environments. The platform automates vulnerability scanning, package vetting, and security policy enforcement, reducing the risk of cybersecurity breaches. Additionally, it enhances operational efficiency and supports innovation by providing access to a wide range of open-source packages. Anaconda allows organizations to build once and run anywhere; safely scale with embedded enterprise-grade governance, user management, and permissions; and safely use open-source packages.
Anaconda commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study and examine the potential return on investment (ROI) enterprises may realize by deploying Anaconda.2 The purpose of this study is to provide readers with a framework to evaluate the potential financial impact of Anaconda on their organizations.
To better understand the benefits, costs, and risks associated with this investment, Forrester interviewed four decision-makers with experience using Anaconda. For the purposes of this study, Forrester aggregated the interviewees’ experiences and combined the results into a single composite organization that has an annual revenue of $1 billion, is headquartered in the United States, and operates globally.
Interviewees said that prior to using Anaconda, their organizations relied on multiple legacy data analytics and business intelligence tools, which were costly and inefficient. Some noted that their companies could not securely access open-source packages or programming languages like Python. Prior attempts to secure open-source packages yielded limited success, leaving them with high licensing fees, manual package management, and inconsistent environments. These limitations led to high operational costs, increased security risks, and reduced productivity.
After the investment in Anaconda, the interviewees’ organizations experienced streamlined package security management, improved operational efficiency, and enhanced security. Key results from the investment included significant time savings for developers and data scientists, reduced risk of cybersecurity breaches, and overall technology cost savings.
Quantified benefits. Three-year, risk-adjusted present value (PV) quantified benefits for the composite organization include:
Improved operational efficiency worth $840,000. The composite organization improves its operational efficiency by 80% after deploying security and governance functionality within the Anaconda AI Platform. By automating and streamlining package security management, developers and data scientists save time previously spent on vetting manual packages, implementing updates, managing dependencies, and reducing troubleshooting time. Enhanced collaboration due to standardized package management and consistent environments facilitates better communication and coordination, leading to more efficient project execution. This efficiency allows employees to focus on core tasks such as coding, model development, and data analysis. Additionally, IT administrators and security analysts benefit from reduced time spent on package security management and approvals.
Strengthened security and reduced risk of breaches worth $157,000. The composite organization experiences strengthened security postures with the Anaconda AI Platform. By providing comprehensive vulnerability scanning, curated packages, and automated security policies, the Anaconda ensures that the composite only uses secure and vetted packages — a proactive approach to package security management that helps prevent potential vulnerability exploitation and reduces the likelihood of data breaches. As a result, the composite organization experiences fewer security incidents and reduces the costs associated with breach remediation and compliance violations.
Technology cost savings of $179,000. Deploying the security and governance features within the Anaconda AI Platform enables the composite organization to retire legacy solutions and consolidate multiple functionalities into a single platform. The results include cost savings from licensing fees and maintenance and support resources and a simplified technology stack.
Unquantified benefits. Benefits that provide value for the composite organization but are not quantified for this study include:
Advanced open-source package access. Anaconda provides access to a wide range of open-source packages that might otherwise be unavailable due to internal IT and security standards. This access ensures that developers and data scientists in the composite organization have the tools they need to innovate and stay competitive.
Increased portability and avoided cloud-vendor lock-in. Anaconda allows the composite organization to move between environments without the need for refactoring and ensures that code written in an Anaconda environment is portable between local machines and any cloud vendor. This flexibility avoids cloud-vendor lock-in, so critical workflows do not depend on any one cloud vendor’s policies or pricing.
Improved time to value and innovation. Anaconda enables faster time to market for the composite by allowing teams to innovate and experiment with new technologies without worrying about package security. Improving time to value and innovation results in a more agile development process, better alignment with customer needs, and improved customer satisfaction.
Enhanced data management and analysis. The composite achieves improved data aggregation, advanced analytics, and better visualization capabilities by using Anaconda, which leads to more accurate and actionable insights.
Expanded employee onboarding and knowledge sharing. Standardized tools and processes make knowledge sharing and new team member onboarding easier for the composite, improving overall team efficiency and collaboration.
Broadened software and hardware interoperability. The composite improves its compatibility with various software environments and hardware configurations with Anaconda, facilitating integration and operation across different systems.
Improved compliance and governance. Automated security checks and policy enforcement help the composite organization meet internal policies and external regulatory requirements, including those that mandate multicloud strategies. Additionally, Anaconda provides enhanced governance by offering enterprise-grade user management and permissions, ensuring that the composite organization can effectively control access and maintain oversight of its Python environments.
Strengthened employee satisfaction and talent attraction. The Anaconda AI Platform reduces manual tasks and streamlines workflows, so the composite’s developers experience increased satisfaction and reduced stress. The composite organization also becomes more attractive to top talent who are familiar with Anaconda versus legacy solutions.
Costs. Three-year, risk-adjusted PV costs for the composite organization include:
Anaconda subscription cost of $164,000. The composite organization pays subscription costs for its security and governance features based on a per user per month annual subscription fee. These costs are lower than its legacy solution, making Anaconda a more cost-effective option. It is important to note that pricing may vary, and organizations are advised to contact Anaconda for additional details.
Implementation, training, and ongoing costs of $373,000. The composite organization incurs various costs associated with the implementation, training, and ongoing maintenance of the Anaconda AI Platform, which are essential to its smooth operation and long-term success. Implementation costs include expenses related to solution setup and configuration, which typically takes one month. Training costs encompass the time and resources required to train employees on using the new system effectively, which varies depending on an employee’s experience and role complexity. Ongoing costs include continuous system maintenance and optimization, such as monitoring production environments and addressing performance issues.
The representative interviews and financial analysis found that a composite organization experiences benefits of $1.18 million over three years versus costs of $537,000, adding up to a net present value (NPV) of $638,000 and an ROI of 119%.
Reduced risk of breaches from addressable attacks with security and governance controls in the Anaconda AI Platform
Return on investment (ROI)
Benefits PV
Net present value (NPV)
Payback
| Role | Industry | Region | Annual Revenue |
|---|---|---|---|
| Head of data management | Financial services | US HQ, global operations | >$40 billion |
| Python technology lead | Industrial | US HQ, global operations | >$40 billion |
| Data scientist | Financial technology | US HQ, global operations | >$25 billion |
| Senior manager of quantitative analysis | Oil and gas | North America HQ, global operations | >$1 billion |
Before investing in the Anaconda AI Platform, the interviewees’ organizations relied on several legacy solutions and manual processes to manage package security. These environments typically included legacy data analytics and business intelligence tools that provided packages for developer and data scientists and other disparate systems. The reliance on multiple tools led to high costs, inefficient operations, and manual efforts to manage package dependencies and security. The interviewees noted how their organizations struggled with common challenges, including:
Inefficient operations. Legacy solutions often lacked comprehensive package management capabilities and contained inconsistent environments. The resulting inefficiencies and manual efforts hindered interviewees’ organizations’ ability to manage dependencies and maintain productivity. The Python technology lead at an industrial organization told Forrester: “The solution coming from [our legacy solution] was only covering 70% or so of the content that we needed. The remaining 30% we would need to pull directly from less secure open-source sites.” Additionally, the head of data management at a financial services organization told Forrester: “We never had an enterprisewide solution. We had pockets of different tools coming together.”
More security risks. Legacy solutions posed security and compliance risks for interviewees’ organizations due to their lack of comprehensive security measures and visibility into potential vulnerabilities, which increased the likelihood of data breaches and other security incidents. The head of data management at a financial services organization told Forrester, “We had incidents where we had a security breach, especially around packages.”
High cost of legacy solutions. The high costs of legacy solutions due to expensive licensing fees and needing multiple tools to manage package security were a significant burden, especially for smaller teams with limited budgets. The senior manager of quantitative analysis at an oil and gas organization told Forrester, “We were using [a legacy solution] and Excel-based tools, which cost us $40,000 a year in licensing for just four licenses.”
Slower time to value. Manual package management and security checks delayed the time to production and impacted the overall time to value for interviewees’ organizations. These delays affected their ability to develop and deploy new features and insights quickly. The data scientist at a financial technology organization told Forrester: “The approval process for new packages took about a month, delaying the time to production and impacting customer satisfaction. With Anaconda, this process was automated and significantly faster.”
Difficulty meeting compliance and governance standards. Interviewees said that ensuring their organizations’ compliance with various governance and privacy standards was challenging using legacy solutions. The risk of noncompliance increased the potential for fines and required manual effort to document data governance and lineage.
Challenges to hire and train employees on legacy software. The steep learning curve of legacy software and the limited supply of talent that could use it made it difficult for interviewees’ organizations to hire and train new employees. This skills gap led to inefficiencies and a limited ability to scale the tools internally. The senior manager of quantitative analysis at an oil and gas organization told Forrester, “[Our legacy solution] has a massive learning curve.”
These challenges collectively underscored the need for a more efficient, secure, and integrated package security management solution, which led the interviewees’ organizations to invest in the Anaconda AI Platform. The interviewees’ organizations searched for a solution that could provide:
Comprehensive package management. Anaconda offered comprehensive package management capabilities, which ensured that all necessary packages were available and compatible and eliminated the need for manual sourcing and package integration from different repositories.
Enhanced security and compliance. Anaconda’s pre-vetted packages and security features provided better visibility into potential vulnerabilities, reducing the risk of data breaches and ensuring compliance with governance and privacy standards.
Cost efficiency. Anaconda was more cost-effective than legacy data analytics and business intelligence tools. By consolidating multiple functionalities into a single platform, interviewees’ organizations could reduce licensing fees and overall costs.
Faster time to value. By automating package management and security checks, Anaconda reduced the time required to put models and insights into production. This acceleration improved overall time to value and customer satisfaction for interviewees’ organizations.
Based on the interviews, Forrester constructed a TEI framework, a composite company, and an ROI analysis that illustrates the areas financially affected. The composite organization is representative of the interviewees’ organizations, and it is used to present the aggregate financial analysis in the next section. The composite organization has the following characteristics:
Description of composite. The composite organization has an annual revenue of $1 billion, is headquartered in the United States, and operates globally. The organization employs a team of developers and data scientists who are dedicated to creating applications, models, and data insights using open-source packages. These packages are integral to various operational aspects, including application development, model creation, and data analysis.
Developers use open-source packages to build and maintain software applications that support the organization’s business processes, ranging from customer-facing platforms to internal tools. Data scientists leverage these packages to develop machine learning and statistical models for predictive analytics, risk assessments, and data-driven decision-making. Additionally, the organization relies on open-source packages to analyze large datasets and extract valuable insights, which inform strategic decisions, optimize operations, and enhance customer experiences. By integrating open-source packages into their workflows, the composite organization can rapidly innovate, reduce development costs, and maintain a competitive edge in the market.
Deployment characteristics. The composite organization deploys security and governance controls within the Anaconda AI Platform to enhance its package management and security capabilities. The deployment includes 100 total users comprised of developers and data scientists. While Anaconda offers other solutions, the focus of this deployment is on its security and governance features.
It is important to note that this analysis represents only a few business units that use the Anaconda AI Platform within the larger composite organization, hence the seemingly low number of users.
The composite completes its rollout of the security and governance features within the Anaconda AI Platform in one month during the initial implementation phase, ensuring that it can leverage the solution’s benefits quickly. By the end of the initial implementation period, the organization had fully utilized the Anaconda AI Platform, integrating it into its workflows and achieving improvements in operational efficiency, security, and compliance.
$1 billion annual revenue
100 Anaconda AI Platform users in developer and data scientist roles
| Ref. | Benefit | Year 1 | Year 2 | Year 3 | Total | Present Value |
|---|---|---|---|---|---|---|
| Atr | Improved operational efficiency | $337,644 | $337,644 | $337,644 | $1,012,932 | $839,671 |
| Btr | Strengthened security | $63,126 | $63,126 | $63,126 | $189,377 | $156,984 |
| Ctr | Technology cost savings | $72,000 | $72,000 | $72,000 | $216,000 | $179,053 |
| Total benefits (risk-adjusted) | $472,770 | $472,770 | $472,770 | $1,418,309 | $1,175,708 |
Evidence and data. Interviewees experienced improved operational efficiency by deploying the security and governance features within the Anaconda AI Platform. By automating and streamlining package security management, developers and data scientists saved time previously spent on vetting manual packages, making updates, and managing dependencies, which had several benefits for interviewees’ organizations.
Reducing the time spent on package security management and approvals enabled IT administrators and security analysts to focus on higher-priority tasks like infrastructure management and threat detection. The Anaconda AI Platform automation eliminated the need for manual security checks and package approvals, which were time-consuming and prone to errors. Interviewees provided the following evidence:
The data scientist at a financial technology organization told Forrester that Anaconda users saved 3 to 4 hours per week on package security management compared to time spent on this task with the prior solution. They found the most time savings in finding and accessing secure packages, avoiding rework, and streamlining collaboration. Furthermore, they explained that Anaconda eliminated a one-month turnaround time for IT and security to approve open-source packages, saving time for users and IT and security roles.
The Python technology lead at an industrial organization mentioned that they saved the equivalent of two FTEs by using Anaconda to handle tasks that were previously done manually. They shared: “Compared to our prior state or using open-source solutions, Anaconda saves us the work of two FTEs. … Anaconda’s environment management features reduced the time our data scientists spent on managing their environments, allowing them to focus on more valuable tasks.”
The head of data management at a financial services organization told Forrester that their data scientists reduced the time spent on package security management from 5% to 1% and rededicated the time to building, training, and deploying models. They stated, “Anaconda streamlined our package management and saved us a lot of time.”
The senior manager of quantitative analysis at an oil and gas organization shared: “[Our legacy solution] has a massive learning curve. My folks are still learning it. Anaconda’s user-friendly interface and extensive documentation made training and onboarding much easier.”
Interviewees noted that ensuring package consistency across teams and standardizing its use throughout their organizations eliminated the risk from using unvetted packages. Additionally, managing package dependencies and keeping them up to date prevented potential issues and reduced troubleshooting time.
Interviewees also noted enhanced collaboration among team members due to these improvements. The standardization facilitated better communication and coordination, leading to more efficient project execution and allowing teams to focus on core tasks such as coding, model development, and data analysis.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
Developers and data scientists spend 5% of their total time on package management prior to Anaconda (104 hours annually).
With Anaconda, these users improve their efficiency by 80%, eliminating 83 hours spent on package management annually.
There are 100 total Anaconda users.
The average fully burdened annual salary for a developer or a data scientist is $162,240 ($78 an hour).
IT administrators and security analysts collectively save 1,660 hours, or 20% of the total developer and data scientist time savings, by eliminating the need to manually vet, approve, and monitor open-source packages.
The average fully burdened annual salary for an IT administrator or a security analysts is $128,960 ($62 an hour).
Forrester assumes a 50% productivity recapture rate, which employees reinvest in other value-added tasks, thereby enhancing overall productivity and contributing to additional business value.
Risks. Forrester recognizes that these results may not be representative of all experiences. The impact of this benefit will vary depending on:
Integration challenges.
The learning curve for new users.
Limited support for niche packages.
Results. To account for these risks, Forrester adjusted this benefit downward by 10%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $840,000.
Efficiency improvement on package security management time
IT administrator and security analyst hours saved with Anaconda
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| A1 | Developer and data scientist hours spent on package security management per user before Anaconda | 2,080 hours*5% | 104 | 104 | 104 | |
| A2 | Efficiency improvement in time spent on package security management | Interviews | 80% | 80% | 80% | |
| A3 | Reduced hours spent on package security management | A1*A2 | 83 | 83 | 83 | |
| A4 | Anaconda users (FTEs) | Composite | 100 | 100 | 100 | |
| A5 | Productivity recapture | TEI standard | 50% | 50% | 50% | |
| A6 | Fully burdened hourly rate for developers and data scientists | Composite | $78 | $78 | $78 | |
| A7 | Subtotal: Developer and data scientist efficiency gains | A3*A4*A5*A6 | $323,700 | $323,700 | $323,700 | |
| A8 | IT administrator and security analyst hours dedicated to package security management saved with Anaconda | A3*A4*20% | 1,660 | 1,660 | 1,660 | |
| A9 | Productivity recapture | TEI standard | 50% | 50% | 50% | |
| A10 | Fully burdened hourly rate for IT administrators and security analysts | Composite | $62 | $62 | $62 | |
| A11 | Subtotal: IT administrator and security analyst efficiency gains | A8*A9*A10 | $51,460 | $51,460 | $51,460 | |
| At | Improved operational efficiency | A7+A11 | $375,160 | $375,160 | $375,160 | |
| Risk adjustment | ↓10% | |||||
| Atr | Improved operational efficiency (risk-adjusted) | $337,644 | $337,644 | $337,644 | ||
| Three-year total: $1,012,932 | Three-year present value: $839,671 | |||||
Evidence and data. Interviewees’ organizations experienced strengthened security postures with the Anaconda AI Platform. By providing comprehensive vulnerability scanning, curated packages, and automated security policies, the Anaconda AI Platform ensured that only secure and vetted packages were used. This proactive approach to package security management helped prevent potential vulnerability exploitation, thereby reducing the likelihood of data breaches.
Interviewees reported that the centralized security management and standardized security policies of the Anaconda AI Platform provided detailed insights into vulnerabilities and enabled efficient mitigation strategy development. The solution’s ability to enforce custom security policies further enhanced their organizations’ ability to proactively address emerging threats. As a result, interviewees experienced fewer security incidents and reduced the costs associated with breach remediation and compliance violations. Interviewees provided the following evidence:
The head of data management at a financial services organization stated: “We had incidents where we had a data breach, a security breach, especially around packages. Package security breaches happened a couple of times. Anaconda’s security features have reduced these incidents.” The head of data management continued, “Anaconda provides consistency across packages used by the entire team, which standardizes what we use and eliminates the risk of using unvetted packages.”
The data scientist at a financial technology organization explained: “Ensuring compliance with security and privacy standards required extensive manual efforts and approvals. With Anaconda, this process was automated, and we could enforce custom security policies tailored to our compliance standards.”
The senior manager of quantitative analysis at an oil and gas organization mentioned that their legacy solution exposed them to risks associated with Log4j incidents, a concern they no longer had with Anaconda. They shared, “Anaconda’s centralized security management and detailed insights into potential threats have enabled us to implement efficient mitigation strategies.”
The Python technology lead at an industrial organization mentioned: “We were not really paying much attention to the security aspects. It’s just really lately, within the last year or two, that you start seeing increased supply chain attacks. Anaconda’s comprehensive vulnerability scanning and curated packages have been crucial in mitigating these risks.”
Strengthened security measures enabled the interviewees’ organizations to reduce their risk of cybersecurity breaches, minimize the costs associated with breach remediation, and ensure compliance with regulatory standards.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
Based on the size and defining characteristics of the composite organization, Forrester estimates the total annual cost of breaches to be $2,791,000. Forrester calculates breach cost by considering a range of factors, including labor for issue prevention, identification, and remediation from across the organization; the cost of fines, lawsuits, lost revenue, and more; and the negative impact on employee productivity, stock price, and investment capabilities.
The composite has a 62% likeliness of experiencing one or more breaches per year.
Seventy-six percent of breaches originate from external attacks targeting organizations, internal incidents, and attacks or incidents involving the external ecosystem.
Anaconda addresses 10% of these attacks.
Anaconda reduces the risk of breaches by 60%.
Risks. Forrester recognizes that these results may not be representative of all experiences. The impact of this benefit will vary depending on:
Delayed updates for new vulnerabilities.
Limited scope of security checks.
Compliance with evolving security standards.
Varying breach severity and downstream financial impact.
Results. To account for these risks, Forrester adjusted this benefit downward by 20%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $157,000.
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| B1 | Cumulative cost of breaches | Forrester research | $2,791,000 | $2,791,000 | $2,791,000 | |
| B2 | Likelihood of experiencing one or more breaches | Forrester research | 62% | 62% | 62% | |
| B3 | Percentage of breaches originating from external attacks, internal incidents, and attacks or incidents involving the external ecosystem | Forrester research | 76% | 76% | 76% | |
| B4 | Percentage of attacks addressable with Anaconda | Interviews | 10% | 10% | 10% | |
| B5 | Annual risk exposure addressable with Anaconda | B1*B2*B3*B4 | $131,512 | $131,512 | $131,512 | |
| B6 | Reduced risk of breaches from addressable attacks with Anaconda | Interviews | 60% | 60% | 60% | |
| Bt | Strengthened security | B5*B6 | $78,907 | $78,907 | $78,907 | |
| Risk adjustment | ↓20% | |||||
| Btr | Strengthened security (risk-adjusted) | $63,126 | $63,126 | $63,126 | ||
| Three-year total: $189,377 | Three-year present value: $156,984 | |||||
Evidence and data. Deploying the Anaconda AI Platform resulted in technology cost savings for the interviewees’ organizations by allowing them to retire legacy data analytics and business intelligence tools. Legacy solutions often incurred high licensing fees and required significant resources for maintenance and support. By consolidating multiple functionalities into a single platform, Anaconda AI Platform reduced overall technology costs and simplified the technology stack. Interviewees reported that the transition to Anaconda enabled them to eliminate expenses associated with maintaining and supporting multiple legacy tools, reduce manual efforts required for package management and security, and streamline processes. These savings allowed the interviewees’ organizations to reallocate resources to more strategic initiatives and improve their overall financial performance. Interviewees provided the following evidence:
The senior manager of quantitative analysis at an oil and gas organization shared, “Anaconda’s comprehensive platform allowed us to sunset [our legacy solution] and save on licensing and support costs.”
The head of data management at a financial services organization stated: “We were able to retire several legacy tools and reduce our overall technology costs. Anaconda provided a comprehensive solution that met all our needs.”
The Python technology lead at an industrial organization mentioned, “By switching to Anaconda, we eliminated the high licensing fees associated with [our legacy solution] and reduced our maintenance costs.”
The data scientist at a financial technology organization explained: “The cost savings from retiring our legacy tools and consolidating our package management with Anaconda were significant. We were able to reallocate those funds to other important projects.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
The composite organization avoids $80,000 in licensing and support costs for legacy technology.
Risks. Forrester recognizes that these results may not be representative of all experiences. The impact of this benefit will vary depending on:
Initial transition costs.
Compatibility issues with existing systems.
User resistance to change.
Results. To account for these risks, Forrester adjusted this benefit downward by 10%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $179,000.
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| C1 | Cumulative legacy technology licensing and support costs replaced by Anaconda | Composite | $80,000 | $80,000 | $80,000 | |
| Ct | Technology cost savings | C1 | $80,000 | $80,000 | $80,000 | |
| Risk adjustment | ↓10% | |||||
| Ctr | Technology cost savings (risk-adjusted) | $72,000 | $72,000 | $72,000 | ||
| Three-year total: $216,000 | Three-year present value: $179,053 | |||||
Avoided legacy licensing and support costs
Interviewees mentioned the following additional benefits that their organizations experienced but were not able to quantify:
Advanced open-source package access. Anaconda provided access to open-source packages that may otherwise have been unavailable due to internal IT and security standards. The access ensured that developers and data scientists had the necessary tools to innovate and stay competitive. The senior manager of quantitative analysis at an oil and gas company said they would not have been able to use Python for development if they did not use Anaconda.
Increased portability and avoided cloud-vendor lock-in. Anaconda allowed developers within organizations to move between environments without the need for refactoring, ensuring that code written in an Anaconda environment was portable between local machines and any cloud vendor. This flexibility avoided cloud-vendor lock-in, ensuring that critical workflows did not depend on any one cloud vendor’s policies or pricing.
Improved time to value and innovation. Anaconda enabled faster time to market and improved customer satisfaction by allowing teams to innovate and experiment with new technologies without worrying about package security. The data scientist at a financial technology organization explained: “The approval process for new packages took about a month, delaying the time to production and impacting customer satisfaction. With Anaconda, this process was automated and significantly faster.” Additionally, the senior manager of quantitative analysis at an oil and gas company stated that “innovation would have been impossible” in their legacy environment.
Enhanced data management and analysis. Interviewees’ organizations improved data aggregation, analytics, and visualization with Anaconda, leading to increased efficiency and more accurate and actionable insights. The Python technology lead at an industrial organization added, “Anaconda’s environment management features reduced the time our data scientists spent on managing their environments, allowing them to focus on more valuable tasks.”
Enhanced employee onboarding and knowledge sharing. Standardized tools and processes facilitated easier knowledge sharing and new team member onboarding, improving overall team efficiency and collaboration. The head of data management at a financial services organization noted, “Anaconda provides consistency across packages used by the entire team, which standardizes what we use.”
Broadened software and hardware interoperability. Anaconda ensured compatibility with various software environments and hardware configurations, facilitating seamless integration and operation across different systems. The Python technology lead at an industrial organization mentioned, “We were able to create software with Anaconda that integrated into our hardware, ensuring smooth operation and compatibility.”
Improved compliance and governance. Automated security checks and policy enforcement helped interviewees’ organizations meet internal policies and external regulatory requirements, including those mandating multicloud strategies. Additionally, Anaconda provided enhanced governance by offering enterprise-grade user management and permissions, ensuring that organizations could control access and maintain oversight of their Python environments effectively. This oversight ensured that organizations remained compliant with evolving standards and regulations, reducing the risk of fines and legal issues while maintaining robust governance practices. The data scientist at a financial technology organization explained: “Ensuring compliance with security and privacy standards required extensive manual efforts and approvals. With Anaconda, this process was automated, and we could enforce custom security policies tailored to our compliance standards.”
Strengthened employee satisfaction and talent attraction. The Anaconda AI Platform reduced manual tasks and streamlined workflows, so developers experienced increased satisfaction and reduced stress. Interviewees’ organizations also became more attractive to top talent who are familiar with Anaconda versus legacy solutions.
The value of flexibility is unique to each customer. There are multiple scenarios in which a customer might implement Anaconda and later realize additional uses and business opportunities, including:
Deployment across major cloud providers. Anaconda’s flexibility allowed interviewees’ organizations deploy across major cloud providers, avoiding vendor lock-in and supporting multicloud strategies. The head of data management at a financial services organization noted, “Anaconda’s flexibility in deployment options helped us avoid vendor lock-in and supported our multicloud strategy.”
Flexibility would also be quantified when evaluated as part of a specific project (described in more detail in Appendix A).
| Ref. | Cost | Initial | Year 1 | Year 2 | Year 3 | Total | Present Value |
|---|---|---|---|---|---|---|---|
| Dtr | Anaconda subscription cost | $0 | $66,000 | $66,000 | $66,000 | $198,000 | $164,132 |
| Etr | Implementation, training, and ongoing costs | $196,560 | $70,980 | $70,980 | $70,980 | $409,500 | $373,077 |
| Total costs (risk-adjusted) | $196,560 | $136,980 | $136,980 | $136,980 | $607,500 | $537,209 |
Evidence and data. Interviewees’ organizations paid subscription costs for the Anaconda AI Platform with pricing based on a per user per month annual subscription fee. These costs were lower than expenses associated with legacy solutions, making Anaconda a more cost-effective option. It is important to note that pricing may vary, and organizations are advised to contact Anaconda for additional details.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
The composite organization pays a $50 per user per month Anaconda AI Platform cost for 100 users, equating to an annual subscription cost of $60,000.
Risks. Forrester recognizes that these results may not be representative of all experiences. The impact of this benefit will vary depending on:
Price increases for subscription renewals.
Additional costs for premium features or higher tiers.
Changes in licensing terms.
Results. To account for these risks, Forrester adjusted this cost upward by 10%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $164,000.
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|---|
| D1 | Anaconda subscription cost | A4*$50 per user per month | $60,000 | $60,000 | $60,000 | ||
| Dt | Anaconda subscription cost | D1 | $0 | $60,000 | $60,000 | $60,000 | |
| Risk adjustment | ↑10% | ||||||
| Dtr | Anaconda subscription cost (risk-adjusted) | $0 | $66,000 | $66,000 | $66,000 | ||
| Three-year total: $198,000 | Three-year present value: $164,132 | ||||||
Evidence and data. Interviewees’ organizations incurred costs associated with the implementation, training, and ongoing maintenance of the Anaconda AI Platform, which were essential to its smooth operation and long-term success.
Implementation costs included expenses related to setting up and configuring the solution, which typically took one month. For example, the head of data management at a financial services organization reported that their initial implementation phase lasted six to eight weeks, including preparation, setup, and configuration.
Training costs encompassed the time and resources required to train employees on using the new system effectively and varied depending on employee experience, familiarity with similar tools, and the complexity of their roles.
Ongoing costs were comprised of continuous system maintenance and optimization, such as monitoring production environments and addressing performance issues. The Python technology lead at an industrial organization highlighted that it incurred ongoing costs for solution maintenance and fine tuning, which involved a team of ten people dedicating 20% to 30% of their time to these tasks.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
The composite pays $50,000 for external implementation support.
The composite dedicates five developer, data scientist, and IT operations FTEs to change management and implementation over one month.
The fully burdened hourly rate for a developer, data scientist, or IT operations role is $74.
One hundred developers and data scientists dedicate 10 hours each to learning and training for Anaconda.
The composite dedicates 0.5 IT infrastructure FTEs per year to ongoing management of Anaconda.
The fully burdened hourly rate for an IT infrastructure FTE is $65.
Risks. Forrester recognizes that these results may not be representative of all experiences. The impact of this benefit will vary depending on:
Longer-than-expected implementation time.
Higher-than-expected training costs due to complexity.
The need for additional training sessions.
Increased maintenance costs due to system updates or issues.
Unanticipated technical support costs.
Potential downtime during implementation or updates.
Additional costs for integrating with existing systems.
Results. To account for these risks, Forrester adjusted this cost upward by 5%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $373,000.
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|---|
| E1 | Implementation fees | Interviews | $50,000 | ||||
| E2 | Total implementation FTEs | Composite | 5 | ||||
| E3 | Total implementation months | Composite | 1 | ||||
| E4 | Total implementation hours | E2*E3*160 hours | 800 | ||||
| E5 | Fully burdened hourly rate for developers, data scientists, and IT infrastructure employees | Composite | $74 | ||||
| E6 | Total internal implementation labor costs | E4*E5 | $59,200 | ||||
| E7 | Subtotal: Implementation costs | E1+E6 | $109,200 | ||||
| E8 | Data scientist and developer FTEs | A4 | 100 | ||||
| E9 | Hours of training required per data scientist and developer FTE | Interviews | 10 | ||||
| E10 | Fully burdened hourly rate for developers and data scientists | A6 | $78 | ||||
| E11 | Subtotal: Total training cost for data employees | E8*E9*E10 | $78,000 | ||||
| E12 | Ongoing IT infrastructure FTE labor dedicated to ongoing management | Interviews | 0.50 | 0.50 | 0.50 | ||
| E13 | Total hours dedicated to ongoing management | E12*2,080 hours | 1,040 | 1,040 | 1,040 | ||
| E14 | Fully burdened hourly rate for an IT infrastructure FTE | Composite | $65 | $65 | $65 | ||
| E15 | Subtotal: Total ongoing costs | E13*E14 | $67,600 | $67,600 | $67,600 | ||
| Et | Implementation, training, and ongoing costs | E7+E11+E15 | $187,200 | $67,600 | $67,600 | $67,600 | |
| Risk adjustment | ↑5% | ||||||
| Etr | Implementation, training, and ongoing costs (risk-adjusted) | $196,560 | $70,980 | $70,980 | $70,980 | ||
| Three-year total: $409,500 | Three-year present value: $373,077 | ||||||
| Initial | Year 1 | Year 2 | Year 3 | Total | Present Value | |
|---|---|---|---|---|---|---|
| Total costs | ($196,560) | ($136,980) | ($136,980) | ($136,980) | ($607,500) | ($537,209) |
| Total benefits | $0 | $472,770 | $472,770 | $472,770 | $1,418,309 | $1,175,708 |
| Net benefits | ($196,560) | $335,790 | $335,790 | $335,790 | $810,809 | $638,499 |
| ROI | 119% | |||||
| Payback | 8 months |
The financial results calculated in the Benefits and Costs sections can be used to determine the ROI, NPV, and payback period for the composite organization’s investment. Forrester assumes a yearly discount rate of 10% for this analysis.
These risk-adjusted ROI, NPV, and payback period values are determined by applying risk-adjustment factors to the unadjusted results in each Benefit and Cost section.
The initial investment column contains costs incurred at “time 0” or at the beginning of Year 1 that are not discounted. All other cash flows are discounted using the discount rate at the end of the year. PV calculations are calculated for each total cost and benefit estimate. NPV calculations in the summary tables are the sum of the initial investment and the discounted cash flows in each year. Sums and present value calculations of the Total Benefits, Total Costs, and Cash Flow tables may not exactly add up, as some rounding may occur.
From the information provided in the interviews, Forrester constructed a Total Economic Impact™ framework for those organizations considering an investment in Anaconda.
The objective of the framework is to identify the cost, benefit, flexibility, and risk factors that affect the investment decision. Forrester took a multistep approach to evaluate the impact that Anaconda can have on an organization.
Interviewed Anaconda stakeholders and Forrester analysts to gather data relative to Anaconda.
Interviewed four people at organizations using Anaconda to obtain data about costs, benefits, and risks.
Designed a composite organization based on characteristics of the interviewees’ organizations.
Constructed a financial model representative of the interviews using the TEI methodology and risk-adjusted the financial model based on issues and concerns of the interviewees.
Employed four fundamental elements of TEI in modeling the investment impact: benefits, costs, flexibility, and risks. Given the increasing sophistication of ROI analyses related to IT investments, Forrester’s TEI methodology provides a complete picture of the total economic impact of purchase decisions. Please see Appendix A for additional information on the TEI methodology.
Benefits represent the value the solution delivers to the business. The TEI methodology places equal weight on the measure of benefits and costs, allowing for a full examination of the solution’s effect on the entire organization.
Costs comprise all expenses necessary to deliver the proposed value, or benefits, of the solution. The methodology captures implementation and ongoing costs associated with the solution.
Flexibility represents the strategic value that can be obtained for some future additional investment building on top of the initial investment already made. The ability to capture that benefit has a PV that can be estimated.
Risks measure the uncertainty of benefit and cost estimates given: 1) the likelihood that estimates will meet original projections and 2) the likelihood that estimates will be tracked over time. TEI risk factors are based on “triangular distribution.”
The present or current value of (discounted) cost and benefit estimates given at an interest rate (the discount rate). The PV of costs and benefits feed into the total NPV of cash flows.
The present or current value of (discounted) future net cash flows given an interest rate (the discount rate). A positive project NPV normally indicates that the investment should be made unless other projects have higher NPVs.
A project’s expected return in percentage terms. ROI is calculated by dividing net benefits (benefits less costs) by costs.
The interest rate used in cash flow analysis to take into account the time value of money. Organizations typically use discount rates between 8% and 16%.
The breakeven point for an investment. This is the point in time at which net benefits (benefits minus costs) equal initial investment or cost.
Total Economic Impact is a methodology developed by Forrester Research that enhances a company’s technology decision-making processes and assists solution providers in communicating their value proposition to clients. The TEI methodology helps companies demonstrate, justify, and realize the tangible value of business and technology initiatives to both senior management and other key stakeholders.
1 Source: Navigate The Open-Source AI Ecosystem In The Cloud, Forrester Research, Inc., February 26, 2025.
2 Total Economic Impact is a methodology developed by Forrester Research that enhances a company’s technology decision-making processes and assists solution providers in communicating their value proposition to clients. The TEI methodology helps companies demonstrate, justify, and realize the tangible value of business and technology initiatives to both senior management and other key stakeholders.
Readers should be aware of the following:
This study is commissioned by Anaconda and delivered by Forrester Consulting. It is not meant to be used as a competitive analysis.
Forrester makes no assumptions as to the potential ROI that other organizations will receive. Forrester strongly advises that readers use their own estimates within the framework provided in the study to determine the appropriateness of an investment in Anaconda. For the interactive functionality using Configure Data/Custom Data, the intent is for the questions to solicit inputs specific to a prospect’s business. Forrester believes that this analysis is representative of what companies may achieve with Anaconda based on the inputs provided and any assumptions made. Forrester does not endorse Anaconda or its offerings. Although great care has been taken to ensure the accuracy and completeness of this model, Anaconda and Forrester Research are unable to accept any legal responsibility for any actions taken on the basis of the information contained herein. The interactive tool is provided ‘AS IS,’ and Forrester and Anaconda make no warranties of any kind.
Anaconda reviewed and provided feedback to Forrester, but Forrester maintains editorial control over the study and its findings and does not accept changes to the study that contradict Forrester’s findings or obscure the meaning of the study.
Anaconda provided the customer names for the interviews but did not participate in the interviews.
Luca Son
Marianne Friis
April 2025
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© Forrester Research, Inc. All rights reserved. Unauthorized reproduction is strictly prohibited. Information is based on best available resources. Opinions reflect judgment at the time and are subject to change. Forrester®, Technographics®, Forrester Wave, and Total Economic Impact are trademarks of Forrester Research, Inc. All other trademarks are the property of their respective companies.
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