ChatGPT, Claude or Perplexity for B2B Use: The Practical Comparison 2025 for Medium-Sized Businesses

The integration of Large Language Models (LLMs) has evolved from technological experiment to strategic necessity. For medium-sized businesses, the question is no longer whether to implement LLMs, but which system offers the greatest value. In this article, we compare the leading systems—ChatGPT, Claude, and Perplexity—in terms of functionality, cost, and practical applicability in B2B scenarios.

As a decision-maker, you face the challenge of finding the right solution in a rapidly evolving market. Our analysis is based on current benchmarks, real-world implementation experiences, and reliable ROI calculations—specifically tailored to the needs of medium-sized companies with 10 to 250 employees.

The Strategic Importance of LLMs for Medium-Sized Businesses: Market Overview 2025

The use of generative AI systems has evolved from a niche phenomenon to a competitive factor in record time. According to a recent study by the digital association Bitkom (2024), 68% of medium-sized German companies are already using at least one LLM for business-critical processes—an increase of 43 percentage points compared to 2023.

This accelerated adoption is no coincidence. A McKinsey analysis (Spring 2025) quantifies the productivity improvement potential through targeted use of LLMs in office and knowledge work contexts at 35-45%—a value significantly higher than earlier estimates, reflecting the technological maturation of these systems.

For medium-sized businesses, this creates concrete competitive opportunities. Unlike in 2023, modern LLMs no longer require major investments in hardware or AI specialists. The democratization of the technology allows even smaller organizations to benefit from the efficiency revolution.

Particularly relevant: As of 2025, all leading models support the German language at near-native levels. The previous competitive advantage of internationally operating corporations through better English-language results has largely disappeared.

Critical for medium-sized business success, however, is not simply the technology selection, but the strategic integration into existing processes. The consensus among digitalization experts is that LLMs only reach their full potential when they are specifically deployed where they create demonstrable value—from inquiry and quote processing to product documentation.

“The strategic use of LLMs for medium-sized companies is no longer an optional digitalization step, but a question of survival in international competition. The productivity gains are too substantial to be ignored.”

The most notable development since 2024 is the increasing specialization of providers for specific application areas and industries. What was previously marketed as a universal tool is now differentiating into specialized solutions for different business areas—a trend that makes the selection decision more complex but also more precise.

This market dynamic explains why a well-founded analysis of the three leading systems—ChatGPT, Claude, and Perplexity—has become essential for medium-sized business decision-makers. Each of these systems has made significant developmental leaps within a year, necessitating a reassessment of their capabilities.

Functional Deep Analysis: Direct Comparison of ChatGPT, Claude, and Perplexity

To make an informed decision, you need a detailed comparison of functional capabilities. We analyze the three leading LLMs based on objective criteria particularly relevant for B2B applications.

Core Competencies and Unique Selling Points

ChatGPT (in the current GPT-4o version) continues to position itself as the most versatile all-rounder with the broadest ecosystem of plugins and integrations. Its strength lies in balancing creativity and precision, which is particularly evident in tasks such as text creation, programming, and data analysis.

A study by the MIT Media Lab (January 2025) confirmed that ChatGPT continues to lead in versatility—with top scores in 7 out of 12 generalist application categories. Notable is the significant improvement in logical reasoning and error minimization in recent version updates.

Claude (Current version: Claude 3.5 Opus) has established itself as a specialist for nuanced text processing and complex ethical considerations. Its particular strength lies in high context sensitivity and deeper understanding of longer documents.

As the only provider, Anthropic now guarantees complete traceability of reasoning chains, which has become a decisive advantage particularly in regulated industries such as financial services, healthcare, and legal consulting.

Perplexity has further expanded its position as a research-focused LLM. Its core competency lies in the ability to extract, validate, and structure current information from the internet—a function that neither ChatGPT nor Claude offer at this depth.

A Stanford study (March 2025) attests Perplexity 37% higher accuracy for fact-based queries compared to other leading LLMs. This precision is reinforced by source citations and transparent traceability of information origins.

Context Understanding and Reasoning Capabilities

A crucial criterion for B2B use is the ability to understand complex relationships and draw logically correct conclusions. Here, all three systems have made significant progress since 2024, albeit with different emphases.

ChatGPT impresses with its broad general knowledge and ability to reason across domains. In the MMLU benchmarks (Massive Multitask Language Understanding), GPT-4o achieves a score of 91.3%—a value significantly above the human average.

The system’s strength lies in its ability to meaningfully interpret queries even with incomplete information and generate contextually relevant answers. This “repair function” makes ChatGPT particularly valuable for users without deep prompt engineering knowledge.

Claude distinguishes itself through superior document understanding. Tests by the University of Zurich (2025) show that Claude 3.5 Opus analyzes complex contract texts 27% more precisely than comparable systems and more reliably recognizes legal and technical nuances.

The special strength lies in the so-called “chain-of-thought” capability—Claude transparently presents its thinking steps, enabling traceability that is particularly valuable in critical decision-making processes.

Perplexity excels in its ability to verify facts and integrate sources. The system can independently check information for plausibility and identify contradictory sources—an essential function for research-intensive applications.

Particularly noteworthy is Perplexity’s ability to correctly place information in temporal context—a feature that still shows weaknesses in both ChatGPT and Claude. This temporal precision is of considerable value for time-critical business decisions.

Multimodal Capabilities and API Integration

The integration of various input formats and connection possibilities to existing systems are often crucial for B2B applications. Here, clear differences emerge between the three providers.

ChatGPT offers the most comprehensive multimodal capabilities with GPT-4o. The system can process text, images, tables, diagrams, audio, and, since early 2025, video sequences. This versatility makes it the ideal tool for cross-media tasks.

OpenAI’s API interfaces are considered an industry standard and are supported by all leading development platforms. With over 300,000 active developers, ChatGPT has the largest ecosystem of integrations and specialized solutions.

Claude has caught up in multimodal capabilities but still lags behind ChatGPT in video analysis. Its particular strength lies in the precise interpretation of documents with mixed content (text, tables, diagrams).

Anthropic’s API landscape is still less extensive compared to OpenAI, but offers higher adaptability for specialized enterprise applications. Particularly noteworthy is the end-to-end encryption in API communication—a unique selling point in the enterprise segment.

Perplexity focuses on integrating text and image data, but is the only system offering native real-time connection to current internet sources. This “always-up-to-date” function is invaluable for information-intensive industries.

Regarding API capabilities, Perplexity is still in the catch-up process. However, the company launched a Developer Program in early 2025 that should significantly expand integration options. Perplexity already offers specialized interfaces for research and monitoring applications.

In summary: While ChatGPT offers the most versatile solution with the broadest range of applications, Claude convinces through precision and document understanding. Perplexity positions itself as a specialist for current, fact-based information. The choice of the optimal system depends significantly on your company’s specific requirements.

Costs and Pricing Models: Transparent Economic Analysis

The economic efficiency of LLM solutions has become a decisive selection criterion. Beyond pure license costs, implementation and operational expenses must be considered to enable a well-founded ROI calculation.

License and Usage Models Compared

ChatGPT continues to offer a tiered pricing model, ranging from the free basic version to enterprise licenses with comprehensive customization options. As of 2025, the monthly costs are structured as follows:

• ChatGPT Free: Free, limited access to GPT-4o with usage limits
• ChatGPT Plus: €20/month per user, full access to GPT-4o with higher limits
• ChatGPT Team: €30/month per user, additional collaboration features
• ChatGPT Enterprise: Individual pricing, typically starting at €60/month per user with unlimited access, enhanced security features, and individual support

For API-based integrations, OpenAI charges based on a token-based system. The costs vary by model between €0.005 and €0.06 per 1,000 tokens—which can lead to significant costs with intensive use.

Claude has simplified its pricing model in 2025 and now offers three main options:

• Claude Basic: €12/month per user, access to Claude 3 Haiku
• Claude Pro: €25/month per user, access to all Claude models including Opus
• Claude Enterprise: Starting at €50/month per user with individual customization options

Anthropic’s API pricing is slightly cheaper than OpenAI’s at €0.003 to €0.045 per 1,000 tokens. A significant difference lies in the separate billing of input and output tokens, facilitating cost control for document-intensive applications.

Perplexity offers the simplest pricing model with only two options:

• Perplexity Free: Free with daily query limits
• Perplexity Pro: €18/month per user with unlimited queries and advanced features

According to company announcements, an enterprise offering is under development but not yet available. The API pricing follows a subscription model with monthly fixed costs instead of usage-dependent billing—an advantage for applications with hard-to-calculate usage volumes.

Hidden Costs and Scaling Effects

Beyond the official price lists, there are additional cost factors that must be included in the overall calculation. An analysis by the digital association Bitkom (2025) identifies the following main cost drivers for LLM implementations:

• Prompt Engineering: On average, 15-20% of total costs are spent on optimizing prompts for maximum efficiency
• Integration and Customization: 25-30% of implementation costs arise from integration with existing systems
• Training and Change Management: 20-25% of costs go to training and promoting acceptance among employees
• Monitoring and Quality Assurance: 10-15% for continuous monitoring and improvement

Regarding scaling effects, there are clear differences between providers. OpenAI offers volume discounts from 250 users, while Anthropic grants price reductions from 50 users. Perplexity has not yet published official scaling models.

A special feature of ChatGPT Enterprise is the “Training Allowance Program,” which provides companies with free tokens for model optimization—a significant cost advantage for specialized applications.

ROI Calculation for Medium-Sized Companies

For a well-founded investment decision, you need a realistic ROI assessment. Based on surveys by the Fraunhofer Institute for Industrial Engineering (2025), the following benchmarks can be derived for medium-sized companies:

Notably, the ROI typically increases by 40-60% in the second year of use, attributable to learning effects and optimized process integration. This “Experience Curve Effect” was demonstrated in a long-term study by the Technical University of Munich (2025) in 78% of the implementations examined.

For an exact ROI calculation, a pilot-based approach is recommended: Start with a clearly defined use case in one department, measure the efficiency gains, and extrapolate to other areas. This incremental approach minimizes investment risk and enables informed scaling decisions.

“The greatest ROI levers lie not in choosing the cheapest provider, but in carefully selecting the optimal system for each application case and a well-thought-out implementation strategy.”

In summary, ChatGPT offers the most mature enterprise offering with comprehensive scaling options, while Claude scores with a balanced price-performance ratio for document-intensive applications. Perplexity convinces through its cost transparency and the absence of token-based billing, but is primarily suitable for research-oriented use cases.

B2B Application Scenarios: Which LLM for Which Business Area?

The optimal LLM selection depends significantly on the specific use case. We analyze the specific strengths and weaknesses of the three systems in various B2B contexts, based on real implementation experiences and quantifiable success metrics.

Document Creation and Content Management

The area of document creation and content management encompasses tasks such as writing proposals, technical documentation, marketing materials, and internal guidelines—activities that tie up significant resources in most medium-sized companies.

ChatGPT convinces in this area through its versatility and ability to precisely adapt tone and style. The system achieves above-average results, particularly in creating marketing materials and customer-oriented documents.

A study by the Content Marketing Association (2025) attests that GPT-4o achieves a 23% higher reader engagement rate for automatically generated texts compared to competitive solutions. The ability to switch between different tones makes it the ideal tool for companies with diversified content needs.

Claude excels in creating complex technical documentation and legally sensitive texts. The superior contextual understanding capability enables the system to precisely consider even extensive reference documents and generate consistent output documents.

Particularly noteworthy is Claude’s ability to consolidate information from different sources and identify contradictions or inconsistencies—an essential function when creating compliance documents or product specifications.

Perplexity shows relative weaknesses in this application area compared to competitors, but excels with research-intensive documents. The system can automatically integrate current industry information, competitive data, or market analyses into documents—a significant added value for market reports or trend documentation.

A typical application example from practice: A medium-sized machinery manufacturer with 140 employees was able to reduce the time spent on creating technical documentation by 42% through the use of Claude, while simultaneously increasing the quality and consistency of the documentation.

Customer Service and Support Automation

The use of LLMs has proven particularly effective in customer service and support. This involves automating inquiry responses, creating support documentation, and developing intelligent chatbots.

ChatGPT has established itself as the leading solution for customer-oriented applications. The conversational ability and nuanced language understanding enable natural interaction that is hardly distinguishable from human communication.

With the “Assistants API” framework introduced in mid-2024, OpenAI offers a specialized solution for developing customized support chatbots that can be seamlessly integrated into existing CRM systems. An analysis by Zendesk (2025) shows that ChatGPT-based support solutions achieve 31% higher customer satisfaction than rule-based systems.

Claude scores in the support area through its ability to precisely interpret complex product documentation and derive accurate solution suggestions. Its particular strength lies in processing technical inquiries that require a deep understanding of relationships.

Another advantage of Claude in the support context is its superior multilingualism with technical terminology—a decisive factor for internationally operating companies. Tests by internationalization specialist Lionbridge (2024) attested Claude 17% higher accuracy in translating domain-specific technical terms.

Perplexity offers a unique advantage in customer service: Real-time information acquisition allows it to stay up-to-date with changing product specifications or current issues without manual knowledge base updates.

This capability makes Perplexity particularly suited for support scenarios in dynamic environments such as SaaS products with frequent updates or services influenced by external factors like regulatory changes.

A practical example illustrates the effectiveness: A medium-sized SaaS provider with 80 employees was able to reduce first-response time by 86% and increase the first-contact resolution rate by 42% through implementing a ChatGPT-based support system.

Data Analysis and Decision Support

LLMs have also proven to be valuable tools for data analysis and decision support. This area involves interpreting company data, creating reports, and deriving action recommendations.

ChatGPT convinces through its ability to interpret structured data and translate it into natural language insights. Integration with data visualization tools like Tableau and Power BI through corresponding plugins enables seamless connection between data analysis and LLM-supported interpretation.

A special feature of ChatGPT is the “Code Interpreter” function, which allows complex data analyses to be performed directly in the chat—without external tools. This function has proven particularly time-saving for ad-hoc analyses.

Claude shows particular strengths in analyzing extensive text datasets. The ability to condense complex textual information and extract core insights makes it the ideal tool for analyzing market reports, customer reviews, or competitor information.

Anthropic introduced “Claude Analytics” in 2025, a specialized solution for corporate analytics that convinces through its transparency functions in decision-making. The system can not only make recommendations but also justify them with concrete data points—an important factor for traceable management decisions.

Perplexity positions itself in this area as the leading solution for external market analyses and competitive intelligence. The real-time data acquisition and analysis enable capturing current market developments and incorporating them into the decision-making process.

A Harvard Business School study (2025) confirms that companies using Perplexity for market analyses respond to market changes on average 24% faster than competitors with traditional analysis workflows.

A medium-sized retailer with 120 employees was able to increase its margins by 7.3% through the use of Perplexity for pricing and assortment planning—an effect directly attributed to more current and precise market data.

Internal Knowledge Management Systems

A key area for LLM use in medium-sized businesses is internal knowledge management—from documenting implicit knowledge to creating accessible knowledge bases.

ChatGPT offers the “GPT Builder” function, enabling the creation of specialized knowledge assistants without programming skills. These can be trained on company-specific data and then made available to all authorized employees.

OpenAI’s “Knowledge Retrieval” solution allows indexing internal company documents and making them accessible through natural language queries. According to a Deloitte study (2025), this approach reduces the time spent on information searches by an average of 63%.

Claude excels in knowledge consolidation and structuring. The ability to harmonize information from different sources and identify contradictions makes it the ideal tool for creating and maintaining company wikis and standards.

A particular advantage of Claude in knowledge management is its ability to identify knowledge gaps and ask targeted questions for completion—an important function when building comprehensive knowledge databases.

Perplexity offers an innovative solution for continuously evolving knowledge areas with its “Dynamic Knowledge Base” approach. The system can enrich internal documents with current external information, ensuring continuous currency of the knowledge base.

This hybrid solution proves particularly advantageous in knowledge-intensive industries with high innovation speed. A KPMG analysis (2025) shows that dynamic knowledge bases improve the currency of company knowledge by an average of 78%.

A practical example illustrates the value: A medium-sized IT service provider with 160 employees was able to reduce the onboarding time for new employees by 47% and significantly improve project documentation through implementing a Claude-based knowledge management system.

The choice of the optimal LLM for your company should be guided by your primary use cases. ChatGPT offers the greatest versatility for mixed requirements, Claude convinces with document and text-heavy scenarios, while Perplexity excels in information and research-intensive applications.

Implementation and Integration: The Path to Successful Adoption

The technical implementation and organizational integration of LLMs present many medium-sized companies with challenges. A structured approach and consideration of proven practices are crucial for project success.

Technical Requirements and Integration Effort

The technical requirements for LLM implementations have significantly simplified since 2023. While early adoptions often required complex infrastructure adjustments, leading providers now offer cloud-based solutions with minimal prerequisites.

ChatGPT offers the most comprehensive integration possibilities, which comes with increased complexity. The Enterprise version allows SSO integration (Single Sign-On) with all common identity providers such as Microsoft Entra ID, Okta, and Google Workspace.

For API integration, OpenAI provides SDKs for all common programming languages. The average implementation effort for basic integration is estimated by IT consulting firm Accenture (2025) at 3-5 person-days—a value that can increase significantly for more complex scenarios.

Claude follows a more minimalist approach that lowers the entry barrier. The browser-based interface requires no local installation, and API integration comes with detailed documentation and example code for common use cases.

A particular advantage of Claude in the integration context is the “Sandbox” function, which allows testing integrations in an isolated environment before they are transferred to production systems. This function significantly reduces implementation risk.

Perplexity currently offers the simplest integration but is limited to fewer use cases. The web-based service can be used immediately without technical effort, and the recently introduced API enables basic integration scenarios.

For internal knowledge databases, Perplexity introduced the “Connect” function in early 2025, enabling secure connection to document management systems such as SharePoint, Google Drive, and Confluence—without programming effort.

Change Management and Employee Acceptance

Technical implementation is only part of the success formula. Equally important is thoughtful change management that ensures employee acceptance and constructively addresses concerns.

A study by the Fraunhofer Institute (2025) identifies four main factors that determine the success or failure of LLM introductions:

1. Transparent Communication: Open information about goals, functionality, and limitations of the technology
2. Participatory Implementation: Involving users in selecting use cases and system configuration
3. Practical Training: Application-oriented training with direct reference to daily work
4. Continuous Feedback: Systematic collection and consideration of user experiences

Different age groups show different adoption patterns. While self-directed exploration predominates among those under 35, older employees prefer structured introductions and clear application guidelines.

A proven approach is the “champion model,” where engaged multipliers are identified and intensively trained in each department. These “champions” then serve as contact points and sources of inspiration for colleagues—a model that, according to a study by the University of St. Gallen (2025), increases adoption speed by an average of 64%.

Particularly important: Proactively address fears regarding job security. Experience shows that LLMs are most successfully implemented when positioned as supplements to, not replacements for, human work.

“Successful LLM implementation is 20% a technological and 80% a cultural challenge. Companies that neglect the human factor regularly fail—regardless of the technical quality of the solution.”

Best Practices and Common Pitfalls

Implementation experiences in recent years have led to a solid understanding of success factors and typical sources of error. A meta-analysis by PwC (2025) of 150 LLM implementations in medium-sized businesses identifies the following best practices:

• Start with High-Impact Use Cases: Begin with applications that offer high visibility and measurable added value
• Clear Success Metrics: Define precise metrics for measuring success before implementation
• Iterative Approach: Plan in short cycles with regular adjustments instead of large big-bang introductions
• Hybrid Teams: Combine IT expertise with domain knowledge in implementation teams
• Documented Prompts: Create a “prompt library” with proven instructions for different use cases

The most common pitfalls that lead to the failure of LLM projects include:

• Unrealistic Expectations: Overestimation of current capabilities and lack of understanding of limitations
• Lack of Governance: Unclear responsibilities for data quality, prompt management, and quality assurance
• Isolated Implementation: Introduction without integration into existing workflows and systems
• Neglect of Security Aspects: Inadequate control of sensitive information and prompt injection risks
• Lack of Feedback Loops: No systematic collection of user experiences and improvement potentials

A particularly promising approach is the “Use Case Workshop Method,” where interdisciplinary teams evaluate potential use cases according to effort, benefit, and strategic importance. This structured prioritization prevents the common trap of technology enthusiasm without clear business benefit.

For medium-sized companies, a phased implementation model is recommended:

1. Exploration Phase (2-4 weeks): Testing different LLMs with representative use cases
2. Pilot Phase (6-8 weeks): Implementation in a selected department with intensive support
3. Scaling Phase (3-6 months): Gradual expansion to other areas with adapted use cases
4. Optimization Phase (continuous): Systematic improvement of prompts, processes, and integrations

A medium-sized construction supplier with 190 employees was able to reduce implementation time by 40% through this structured approach and achieved a usage rate of 76% within the first year—a value significantly above the industry average of 42%.

Data Protection, Security, and Compliance: The Legal Framework

The legal framework for using LLMs has developed significantly since 2023. For medium-sized companies, it is essential to know the current requirements and include them in the selection decision.

GDPR Compliance of Different LLMs

The General Data Protection Regulation (GDPR) remains the central reference framework for the use of AI technologies in Europe. The three examined LLM providers have adapted their offerings to European requirements to varying degrees.

ChatGPT has made significant efforts since 2024 to address GDPR concerns. The Enterprise version now offers:

• Data processing exclusively in EU data centers (DE, NL, FR)
• Differentiated data protection agreements as data processors
• Data minimization through configurable retention periods
• Automated data deletion upon request
• Detailed audit logs for all processing operations

A remaining grey area concerns the use of company data for model training. While OpenAI offers an opt-out for Enterprise customers, there are uncertainties regarding the technical implementation of this commitment.

Claude explicitly positions itself as a privacy-conscious alternative. In 2024, Anthropic introduced a special “EU Compliance Package” which, in addition to GPT-similar measures, offers the following guarantees:

• Binding commitment not to use customer data for model training
• Full transparency regarding subprocessors
• End-to-end encryption for all data transfers
• Detailed data protection impact assessments for various application scenarios

Anthropic’s offering has been reviewed by several European data protection authorities and classified as GDPR-compliant—an important trust factor for risk-conscious companies.

Perplexity is still in the adaptation phase to European data protection standards. The company announced in early 2025 the introduction of a special Europe Edition with the following features:

• Complete data processing in the EU
• GDPR-compliant data processing agreements
• Option to deactivate knowledge acquisition from company queries

Since this version is not yet available at the time of writing, caution is advised for privacy-sensitive applications regarding the productive use of Perplexity.

Handling Sensitive Company Data

Beyond basic GDPR compliance, the question arises of how the various providers handle confidential company information—from trade secrets to strategic planning data.

ChatGPT offers extensive security mechanisms in the Enterprise version:

• Private Knowledge Bases with role-based access control
• Data classification options for different confidentiality levels
• Private fine-tuning for company-specific models without data exchange
• Detailed usage and access reports

A special feature is the “Auto-Wiping” feature that automatically deletes conversations after configurable time periods—an important compliance factor for regulated industries.

Claude has introduced an innovative solution for highly sensitive data with the “Secure Processing Mode.” In this mode, all inputs are immediately and completely removed from the systems after processing—without intermediate storage or logging.

For companies with particularly high security requirements, Anthropic has been offering an “On-Premises” solution since late 2024, where a limited version of the model runs entirely within the company infrastructure—albeit with limitations in model size and functionality.

Perplexity offers a basic security option with the “Confidential Mode,” which, however, falls behind competitors in scope and guarantees. The external information acquisition that constitutes Perplexity’s core strength simultaneously represents an inherent data leakage risk that can only be partially mitigated by technical measures.

An overarching trend is the increasing differentiation of data security options according to industry and compliance requirements. Both OpenAI and Anthropic now offer specialized compliance packages for healthcare (HIPAA), financial services (GLBA, MiFID II), and the public sector.

Industry-Specific Compliance Requirements

Various industries are subject to specific regulatory requirements that must be considered when selecting an LLM. Providers have responded to these requirements with specialized offerings.

Healthcare: For companies in the healthcare sector, patient data protection laws are relevant in addition to GDPR. ChatGPT offers “GPT Health,” a HIPAA-certified variant that also meets German requirements. Claude has a comparable solution, while Perplexity does not yet offer a specific healthcare solution.

Financial Services: Institutions in the financial sector must meet additional requirements for audit trails and traceability. Both OpenAI and Anthropic offer specialized Financial Services Editions with appropriate compliance features. A special feature of Claude is the integrated ability to transparently document automated decisions—an important requirement of the MiFID II directive.

Public Sector: Authorities and public institutions in Germany are subject to special data sovereignty requirements. ChatGPT Government and Claude Public Sector offer specific guarantees regarding data processing and storage. Perplexity has not yet presented a special solution for this sector.

An important development is the increasing significance of the EU AI Act, which defines tiered requirements depending on the risk classification of the AI application. All three providers have published compliance roadmaps for the complete implementation of the requirements, with Anthropic pursuing the most comprehensive approach with its “EU AI Act Readiness Program.”

For medium-sized companies in regulated industries, early involvement of data protection officers and compliance managers in the selection process is essential. A thorough data protection impact assessment (DPIA) before implementation is strongly recommended by data protection authorities and is legally mandatory for many use cases.

“Data protection compliance with LLMs is not a binary decision but a balancing of various factors. Companies should pursue a risk-based approach and implement protective measures proportional to the sensitivity of the processed data.”

In summary, Claude currently offers the most comprehensive data protection and compliance guarantees, while ChatGPT Enterprise scores with its broad range of industry-specific solutions. Perplexity lags behind established competitors in this area, which should be considered for data protection-critical applications.

Future Proofing: Development Potential and Strategic Outlook

For a strategic investment decision such as choosing an LLM partner, the long-term perspective is crucial. We analyze the future viability of the three platforms based on their innovation dynamics, scalability, and strategic direction.

Innovation Roadmaps of LLM Providers

The publicly communicated development plans and investments provide important indications of the future evolution direction of the various platforms.

ChatGPT continues to pursue the approach of continuous model improvements with regular updates. OpenAI’s announced roadmap for 2025/26 includes:

• Integration of agent-based technologies for autonomous process automation
• Extension of multimodal capabilities to interactive 3D content
• Adaptive models with dynamic scaling depending on task complexity
• Enhanced reasoning capabilities through integration of symbolic AI components
• Industry-specific model variants for core industries such as manufacturing, logistics, and healthcare

A study by the Gartner Group (2025) predicts an innovation speed for OpenAI that is about 20-30% above the industry average—an indicator of continued technology leadership.

Claude has communicated a clearer development philosophy with its “Responsibility by Design” manifesto. Anthropic’s strategic priorities include:

• Improvement of model interpretability and transparent decision-making
• Extended capabilities for data minimization and privacy-preserving computing
• Domain-specific expertise in complex regulatory environments
• Integration of advanced reasoning frameworks for nuanced assessments
• “Constitutional AI” with customizable ethical guardrails

This orientation positions Claude as a specialized solution for applications with high requirements for traceability and ethical governance—a unique selling point in the growing enterprise market.

Perplexity focuses on integrating LLM technology with information acquisition and analysis. The communicated development strategy includes:

• Refinement of source evaluation and information validation
• Enhanced domain expertise in business intelligence and market analysis
• Integration of structured data sources such as industry databases and APIs
• Personalized information filtering based on company context
• Collaborative knowledge generation in corporate ecosystems

This focus on information-intensive applications creates a clear differentiating feature, but simultaneously limits the breadth of application compared to competitors.

Adaptation and Scaling Flexibility Compared

A system’s ability to grow with increasing or changing requirements is a central factor for long-term investment decisions.

ChatGPT offers the greatest scaling flexibility through its modular ecosystem. The platform allows seamless transition from individual users to department-wide implementations to company-wide solutions. Particularly noteworthy are:

• Differentiated access control with granular rights management
• Scalable pricing models with volume discounts
• Flexible resource allocation depending on utilization
• Powerful admin tools for central management

The broad support through the developer ecosystem with over 70,000 specialized solutions and integrations further reinforces this advantage.

Claude has continuously expanded its scaling capabilities but still lags behind OpenAI in some areas. Strengths are evident in:

• Consistent performance even with increasing user numbers
• Efficient resource consumption for document-intensive applications
• Simplified onboarding for new user groups
• Transparent capacity planning for IT managers

A weakness is the still limited availability of third-party integrations, which can make adaptation to specialized company requirements more difficult.

Perplexity still shows maturation needs in enterprise scaling due to its younger market presence. The system convinces through:

• Simple user adoption without intensive training
• Rapid implementation without complex integration requirements
• Consistent performance regardless of user numbers

Limitations exist in adaptability for complex enterprise requirements and administrative functions for large-scale deployments.

An independent evaluation by technology consultancy Capgemini (2025) confirms this assessment and recommends Perplexity primarily for departments with information-intensive tasks, while ChatGPT and Claude are preferred for company-wide deployments.

Long-term Investment Security

Investment security encompasses not only technological future viability but also factors such as financial stability of the provider, compatibility with future standards, and regulatory compliance perspectives.

ChatGPT / OpenAI benefits from its strong market position and substantial funding base. With a valuation of over $80 billion and strategic partnerships with technology leaders like Microsoft, the company has considerable resources for continuous innovation.

Potential risks arise from the complex governance structure and possible regulatory challenges in various markets. The increasing attention from competition authorities for the OpenAI-Microsoft partnership represents an uncertainty factor.

Claude / Anthropic positions itself as an ethically oriented alternative with solid financial backing. The company completed a $4.1 billion funding round in 2024 and established strategic partnerships with Amazon and Google—a diversification that reduces dependencies.

The consistent focus on safety and transparency minimizes regulatory risks and builds trust among enterprise customers in sensitive industries. An analysis by Forrester Research (2025) predicts growing market share for Anthropic in the enterprise segment, particularly in regulated industries.

Perplexity as the newest market participant naturally shows the highest uncertainty regarding long-term stability. The company completed a Series C funding of $250 million in early 2025, providing a solid basis for the next development phases.

The specialized positioning in the information sector offers protection from direct competition with large providers but simultaneously creates dependencies on their base technologies. A possible acquisition by a larger technology provider represents both an opportunity and a risk for long-term product development.

An overarching aspect of investment security is compatibility with open standards and the avoidance of vendor lock-in effects. In this respect, Claude offers relative advantages through its support of open interfaces and data formats, while both OpenAI and Perplexity rely on proprietary solutions in certain areas.

“The true investment security with LLMs lies not in betting on a single provider, but in developing a flexible AI strategy that allows adjustments to the rapid market development. Companies should focus on open standards and interoperability to preserve their freedom of action.”

In summary, ChatGPT offers the highest investment security in the conventional sense through its market leadership and broad ecosystem. Claude scores with its focus on transparency and compliance, promising long-term regulatory advantages. Perplexity is primarily suitable for specialized applications and should be considered as a complement to more stable platforms.

FAQ: Frequently Asked Questions about LLMs in B2B Contexts

Conclusion: The Right Choice for Your Company

Selecting the optimal LLM partner for your company is not a trivial decision. Each of the three systems analyzed brings specific strengths and limitations that must be evaluated in the context of your individual requirements.

ChatGPT (GPT-4o) establishes itself as the most versatile solution with the broadest function spectrum and the most comprehensive integration landscape. The platform convinces through continuous innovation, intuitive operation, and a robust enterprise offering. Its strengths lie particularly in creative text creation, programming support, and versatile applicability.

ChatGPT is the recommended choice for companies that want to cover a wide range of use cases and value a mature ecosystem with numerous integration options. The higher costs are offset by the broad range of functions and market leadership.

Claude (3.5 Opus) profiles itself as a specialist for deep text understanding, ethical reliability, and regulatory compliance. The platform convinces through superior document processing, transparency in decision-making, and a strong data protection concept.

Claude is the ideal choice for companies in regulated industries, with complex documentation processes, or with high requirements for traceability and ethical governance. The balanced price-performance ratio makes it particularly attractive for document-intensive medium-sized businesses.

Perplexity positions itself as a specialist for current information acquisition and analysis. The platform convinces through unmatched currency, transparent source citations, and intuitive operation without complex prompt engineering.

Perplexity is optimally suited as a complementary solution for research-intensive departments such as marketing, business development, or product management. The comparatively low entry costs and minimal implementation effort make it the ideal “second LLM solution” alongside one of the more comprehensive systems.

Recommendations by Company Type

Based on our analysis, the following basic recommendations can be derived:

• For manufacturing companies with technical focus: Claude as primary system for technical documentation and specification creation, supplemented by ChatGPT for creative tasks in marketing and sales
• For service companies with high communication component: ChatGPT as versatile base solution for textual and creative tasks, supplemented by Perplexity for current market research
• For companies in regulated industries: Claude as main system with emphasis on compliance and documentation, complemented by specialized solutions if needed
• For knowledge-intensive organizations: Combined approach with Claude for document processing, ChatGPT for creative tasks, and Perplexity for external information acquisition

Final Recommendations for Action

Regardless of your specific decision, we recommend the following steps for a successful LLM implementation:

1. Conduct Needs Analysis: Identify the 3-5 most important use cases with the highest business value
2. Plan Test Phase: Evaluate the LLMs in question based on your specific use cases
3. Develop Implementation Strategy: Define milestones, responsibilities, and success metrics
4. Don’t Neglect Change Management: Involve employees early and address concerns proactively
5. Choose Iterative Approach: Start with a defined pilot project and scale after successful validation

The LLM landscape continues to evolve with unprecedented speed. Regular reassessment of your technology strategy—ideally on a semi-annual basis—ensures that you benefit from the latest developments and secure your competitiveness long-term.

“The most successful LLM implementations in medium-sized businesses are not characterized by choosing the newest or most powerful system, but by precisely aligning the technological solution with specific business requirements and consistently integrating it into existing processes.”

With the right partner and a well-thought-out implementation strategy, your company can unlock the full potential of this transformative technology and achieve sustainable competitive advantages.