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 In the rapidly evolving landscape of artificial intelligence, understanding how to effectively monetize AI products has become crucial for businesses. This comprehensive guide delves into the economics and pricing strategies for GenAI development, offering valuable insights for companies looking to capitalize on this transformative technology.
1. The AI Monetization Challenge The primary challenges in implementing GenAI models revolve around two key factors: value and cost. While the potential value of AI solutions can be immense, quantifying and communicating this value to customers remains a significant hurdle. 1.1 Value Proposition When the value of AI is clear, the results can be staggering. For instance, Klarna's AI assistant, powered by OpenAI, demonstrated remarkable success within just one month of its global launch: - 2.3 million conversations handled, equivalent to two-thirds of Klarna's customer service chats - Workload equivalent to 700 full-time agents - Customer satisfaction scores on par with human agents - Estimated $40 million USD profit improvement for Klarna in 2024 1.2 Cost Considerations The costs associated with developing and implementing GenAI models can be substantial: - Training Llama 3.1: Approximately $1 billion - Training GPT-4: Around $100 million - Training BloombergGPT: Roughly $10 million - Custom GPT-4 model training: $2-3 million These figures highlight the significant investment required for AI development, emphasizing the need for careful cost management and strategic pricing. 2. The 5-Step Product Monetization Framework To effectively monetize AI products, a structured approach is essential. The following 5-step framework provides a comprehensive guide for pricing any software product, including AI-powered solutions: 1. Value Understanding 2. Packaging Decisions 3. Pricing Metric Decisions 4. Price Point Selection 5. Pricing Model Selection 2.1 Packaging Options When introducing a new AI product, companies must consider various packaging options along a spectrum from inflexible to highly flexible: - One-size-fits-all - Good/Better/Best - Add-ons - Usage-based The choice of packaging strategy depends on factors such as market positioning, customer needs, and product complexity. 2.2 Pricing Metric Selection Selecting the appropriate pricing metric for AI products involves considering seven key factors: 1. Customer risk perception 2. Mental anchors 3. Alignment with value 4. Consumption pattern 5. Cost patterns 6. Competitive action 7. Implementability For generative content AI products, pricing based on credit or token bundles of consumption per user is the most common metric. Enterprise SaaS with AI add-ons often employ hybrid metrics, combining per-user platform pricing with consumption-based add-ons. 3. GenAI Costs: A Deeper Dive Understanding the various cost factors associated with implementing GenAI models is crucial for effective monetization. These factors include: - Performance - Data costs - Infrastructure - Integration - Scalability - Support - Licensing - Latency - Security - Compliance - Talent 4. Implementing GenAI Models: Open vs. Closed Source When implementing GenAI models, companies have three main options: 1. Use closed-source models (e.g., GPT-4, Claude 3.5 Sonnet) 2. Leverage open-source models (e.g., Llama 3.1, Mixtral 8x22B) 3. Train their own custom model Each approach has its advantages and disadvantages: 4.1 Closed Source - Pros: Effortless integration, no infrastructure management - Cons: Potential lack of domain knowledge, customization difficulties 4.2 Open Source - Pros: Freedom to use any model and cloud, complete control over model and data - Cons: Requires specialized AI/ML talent, longer implementation time 4.3 Custom Model - Pros: Full control over training data, high data privacy and security - Cons: Most time-consuming to implement, requires significant resources 5. Recent Trends in GenAI Development Several notable trends have emerged in the GenAI landscape: 1. The performance gap between closed and open-source LLMs has decreased significantly in the past two years. 2. Custom open-source models now surpass GPT-4 across 31 use cases. 3. The speed difference between closed and open-source LLMs is now negligible. 4. The cost of tokens has reduced by 240x over two years, with inference costs dropping from ~$50 to $0.50 per 1M tokens. These trends indicate that open-source solutions are becoming increasingly competitive with closed-source options, potentially offering substantial cost savings for businesses. 6. Key Takeaways for Monetizing GenAI 1. AI product costs and value have high variance, making both development cost and pricing strategy crucial for success. 2. Packaging and pricing metric decisions are pivotal for AI products – choose wisely based on your specific use case and target market. 3. Closed-source APIs like GPT-4 offer effortless integration and faster time to market. 4. Open-source models like Llama 3.1 provide more control and can be a better long-term investment in GenAI. 5. The performance of open-source models is now comparable to closed-source APIs, with customized open-source models potentially outperforming them. 6. GenAI models will continue to become cheaper, better, smaller, faster, and easier to develop over time. By carefully considering these factors and staying informed about the latest developments in GenAI, businesses can develop effective monetization strategies that maximize the value of their AI investments while managing costs and meeting customer needs. As the AI landscape continues to evolve, companies that successfully navigate the complexities of GenAI monetization will be well-positioned to capitalize on this transformative technology and gain a competitive edge in their respective markets.  When hiring AI engineers to build Generative AI (GenAI) products during the evolution of a startup from seed-stage to PMF (Product-Market Fit) stage to Growth stage, it's important to consider strategies that align with the company's evolving needs and budget constraints. Here are some strategies to consider at each stage:
Seed Stage 1. Focus on Versatility: At this stage, hire AI engineers who are generalists and can wear multiple hats. They should have a broad understanding of AI technologies and be capable of handling various tasks, from data preprocessing to model development. 2. Leverage Freelancers and Contractors: Consider hiring freelance AI specialists or contractors for short-term projects to manage costs. This approach provides flexibility and allows you to access specialized skills without long-term commitments. 3. Upskill Existing Team Members: If you already have a technical team, consider upskilling them in AI technologies. This can be more cost-effective than hiring new talent and helps retain institutional knowledge. PMF Stage 1. Hire for Specialized Skills: As you approach product-market fit, start hiring AI engineers with specialized skills relevant to your GenAI product, such as expertise in natural language processing or computer vision. 2. Build a Strong Employer Brand: Establish a strong brand as an employer to attract top talent. Highlight your mission, values, and the impact of your GenAI product to appeal to candidates who share your vision. 3. Offer Competitive Compensation: While budget constraints are still a consideration, offering competitive salaries and benefits can help attract and retain skilled AI engineers in a competitive market. 4. Implement Knowledge-Sharing Practices: Encourage mentoring and knowledge-sharing initiatives within your team to enhance skill development and foster collaboration. Growth Stage 1. Scale the Team: As your startup grows, scale your AI team to meet increasing demands. Hire senior AI engineers and data scientists who can lead projects and mentor junior team members. 2. Invest in Continuous Learning: Provide opportunities for ongoing learning and development to keep your team updated with the latest AI advancements. This investment helps maintain a competitive edge and fosters employee satisfaction. 3. Optimize Recruitment Processes: Streamline your hiring process to efficiently identify and onboard top talent. Use AI tools to assist in candidate screening and reduce bias in hiring decisions. 4. Foster a Collaborative Culture: Create a work environment that encourages innovation, creativity, and collaboration. This helps retain talent and enhances team productivity. By adapting your hiring strategies to the specific needs and constraints of each stage, you can effectively build a strong AI team that supports the development and scaling of your GenAI products.  Vector databases have recently gained prominence with the rise of large language models and generative AI. A vector database is a data store for unstructured text in the form of vector embeddings for various AI models and applications. Embeddings are a high dimensional vector representation of text that conveys rich semantic information and represent an efficient way of capturing unstructured data like text.
The rising popularity of large language models like GPT-4, Gemini, Claude-2, Llama-2, Mixtral and others have fuelled tremendous interest in generative AI across the industry to build applications based on these models. Vector databases are specialized for handling vector data that is used to train or fine-tune these foundational models for domain and company specific use cases. Unlike traditional scalar-based databases, vector databases offer optimized storage and querying capabilities for vector embeddings. Although several vector databases are now available in the market like Pinecone, Chroma, Qdrant amongst others, deciding which vector database to choose for enterprise use cases is not a straightforward decision. In this article, you will learn how to decide which vector database to choose for your organization based on criteria like performance, reliability, scalability, cost-efficiency, developer experience, security, technical support amongst others. Key Considerations In this section, you will learn in detail about each of the key factors that should be considered to make your final selection of a vector database. These include data and use case characteristics, performance, functionality, enterprise-readiness, developer experience, and future roadmap. 1. Data and Use Case It is important to work backwards from the specific business use case that you are planning to solve by leveraging organizational data and the latest techniques from the field of generative AI. For instance, if your business objective is to build an enterprise knowledge management chatbot like McKinsey’s Lilli, you will need to organize and prepare all the in-house text data such as documents, emails, chat messages etc. The use case defines several aspects of the data, including its size, frequency, data type, growth in the volume of data over time, data freshness and consequently the nature of the underlying vector embeddings to be stored in the vector database. These vectors may be sparse, dense, and also span multiple modalities depending on the use case. Additionally, careful planning and scoping of the use case also helps you understand other crucial aspects such as the number of users, the number of queries per day, the peak number of queries at any given instant, as well as the query patterns of the users. Vector databases utilize indexing and vector search powered by k-nearest neighbors (kNN) or approximate nearest neighbor (ANN) algorithms. This empowers a vector db to perform similarity search and identify the most similar vectors in the database. This capability underlies enterprise use cases based on natural language processing such as question-answering, document analysis, recommender systems, image and voice recognition etc. 2. Performance 2.1 Query latency and query per second (QPS) The primary performance metrics of a vector db are the query latency, i.e., the time it takes to run a query and get the result and the query per second that defines the throughput in terms of the number of queries processed in a second. These parameters are critical for ensuring a seamless user experience for several applications that require real-time results such as chatbots. Typical QPS values range from ~50-300 and the average query latency from 25-100 ms depending on the underlying hardware. 2.2 Scalability Scalability measures the ability of the vector database to grow and expand further to support the requirements of its customers. The scale can be measured in terms of the number of embeddings that can be supported and in terms of horizontal scaling of existing resources and vertical scaling of additional servers. Typically, most existing vector db companies provide scale-out capabilities up to a billion vectors without any performance degradation. If the resources can scale automatically, then you can be rest assured that your application will always be up and running. 2.3 Accuracy A vector database is as good as its accuracy of retrieving the right set of results based on the user queries. Here, the choice of vector search algorithms to identify data sources with similar embeddings as the embedding of the user query is pivotal. There are several different algorithms used for powering vector search such as kNN, ANN, FAISS, NGT. These algorithms generate approximate results and the best vector databases provide a good trade-off between speed and accuracy. 3. Functionality 3.1 Filtering on metadata In practice, filtering vector search results based on the metadata helps reduce the search space, thus providing for faster and more accurate search results. Typical metadata includes information like dates, versions, tags and the ability of a vector database to store multiple metadata fields allows for a better search experience. 3.2 Integrations Integrating a vector database into the existing data and engineering infrastructure in your organization is critical to faster adoption and lesser time to value. The ability of vector databases to seamlessly integrate with essential infrastructure elements like the cloud infrastructure, underlying large language models, databases etc. is a key factor to consider. 3.3 Cost-efficiency While performance metrics and functionality are core to a technology, the cost should be reasonable and fit your budget. The pricing of vector databases is a function of the number of ‘write’ operations such as update and delete and the number of queries. Other factors that affect the cost include the dimensionality of the embedding, the number of vectors stored in the database, and the size of the metadata. Depending on your use case and requirements, it is essential to estimate the overall cost of running your application at scale on a monthly or quarterly basis and evaluate the overall costs relative to your budget and the expected revenue from running the AI applications. 4. Enterprise-readiness 4.1 Security and compliance For most enterprise companies, it is imperative that any external vendor they employ meets strict security and compliance requirements. These requirements include SOC2, GDPR, HIPAA, ISO compliance and others, depending on the domain in which the company operates. The data privacy and security standards have gone up in the light of recent cybersecurity attacks and breaches of customer data, and you should ensure that any vector db vendor meets your specific security and compliance requirements. 4.2 Cloud setup Several modern companies have undergone digital transformation and house their entire data and infrastructure in the cloud vs on-premise. You may choose to manage and maintain your infrastructure via a self-hosted setup or go for a fully managed SaaS platform. The benefit of a fully managed system is that it automates clusters with minimal requirements for you to provision and scale clusters or take care of operational issues. 4.3 Availability Availability, i.e. the ability of your vector db to run without any interruptions, issues or downtime is essential to not adversely impact user experience. Most vector database providers vouch for specific SLAs which should meet the requirements for your applications. Typical values include 99.9% for uptime SLA and a few hours to a few business days for response time SLA depending on the severity of the production issue. 4.4 Technical support More often than not, you might be stuck facing some issues with your vector db and need some hands-on support from the vendor to help troubleshoot the issue. Does the company provide you with a dedicated team who can be available at a short notice to get on a call and figure out how to solve the problem? The quality of responsiveness and customer support experience provided by a vector db company is valuable and helps you develop a stronger sense of trust in the company. 4.5 Open source vs Closed source Some vector db companies are closed source and operate under a proprietary license such as Pinecone. At the same time, there are a host of vector db companies that are open source under the Apache 2.0 license such as Qdrant or Chroma while also offering a fully managed service. This can also influence your choice of the vector db provider. 5. Developer experience 5.1 Community Software and AI engineers are the core professionals who will work on the vector db and integrate it in the company’s infrastructure and deploy your generative AI application to production. Therefore, the quality of experience that developers have with a vector db solution is integral in shaping your final decision. Having an open-source community on Slack or Discord helps build more engagement and trust with developers than commercial vendor support. It provides your developers an opportunity to learn from developers at other companies as well and discuss and solve issues by leveraging the wisdom of the community. 5.2 Onboarding Onboarding a new technology is challenging as it determines the time your developer team takes to properly understand the product, integrate it, troubleshoot any issues, and become an expert in using the vector database. The availability of APIs and SDKs as well as clear product demos and documentation goes a long way in reducing the barriers to understanding a new vector database so that your developers can build with speed and confidence. 5.3 Time to value Similar to the time to onboard a new vector db, another important factor is the time to business value. If a vector db provider vouches for a fast deployment of a production-ready application, then you can realize value sooner, and meet your business goals faster as well. A long gestation time from onboarding to business value is a deterrent for many fast-moving companies and startups especially in the current frantic race to adopt and ship generative AI applications. 5.4 Documentation The quality of the vector database’s documentation determines the time to onboard, time to value, and trust in the provider’s expertise and product. Clear instructions with tutorials, examples and case studies help your developers understand and master the vector db faster. 5.5 User education Similar to community-based offerings, expert technical content such as blogs, demos and videos focused on the existing as well as new features are helpful for your team to understand and build faster. In addition to text and video content, other offerings like user testimonials, workshops, conferences also help educate your team and build more trust in the vector db provider. 6. Future roadmap A final factor to consider is the product roadmap of the vector database provider. Vector databases are an emerging technology that will need to continuously evolve alongside the advances in generative AI models, chip design and hardware, and novel enterprise use cases across domains. Therefore, the vector db vendor should show the potential for evaluating long-term and future industry trends such as sophisticated vectorization techniques for a wider variety of data types, hybrid databases, optimized hardware accelerators for AI applications such as GPUs and TPUs, distributed vector dbs, real-time and streaming data based applications, as well as industry-specific solutions that might require advance data privacy and security. Conclusion Vector databases are an essential ingredient for modern generative AI applications built on unstructured data such as text. Their popularity has increased in parallel to the developments in the generative AI field such as large language models, large image models etc. to serve as the underlying database for handling high-dimensional data stored as vector embeddings. In this article, you learned about several important pillars to help your decision making about the choice of the vector database. These factors include data and use case considerations, performance-based requirements such as query speed and scalability, functionality requirements such integrations and cost-efficiency, enterprise-readiness including security and compliance, and developer experience including community and documentation. Several vector database companies have emerged to build this foundational infrastructure. There is no single ‘best’ vendor of vector db and the ultimate choice is highly contingent on your organization’s business goals. Therefore, a data-driven approach guided by the factors listed in this article will help you select the most optimal vector db for your organization.  Figure 1 - Mixture of Experts layer. 1. Introduction Mistral is a pioneering French AI startup that launched their own foundational large language model, called Mistral 7B in September 2023. As of the date of launch, it was the best 7 billion parameter language model, outperforming even larger language models like Llama 2 of size 13 billion parameters across multiple benchmarks. In addition to its performance, Mistral 7B is also popular as the model is open-sourced under the Apache 2.0 license with the model weights available for download. Mixtral 8x7B (hereafter, referred to as “Mixtral”) is the latest model released by Mistral in January 2024 and represents a significant extension of their prior work on Mistral 7B. It is a 7B Sparse Mixture of Experts (SMoE) language model with stronger capabilities than Mistral 7B. It uses 13B active parameters during inference out of a total of 47B parameters, and supports multiple languages, code, and 32k context window. In this blog, you will learn about the details of the Mixtral language model architecture, its performance on various standard benchmarks vis-a-vis state-of-the-art large language models like Llama 1 and 2 and GPT3.5, as well as potential use cases and applications. 2. Mixtral Mixtral is a mixture-of-experts network, similar to [GPT4]. While GPT4 is said to constitute 8 expert models of 222B parameters each, Mixtral is a mixture of 8 experts of 7B parameters each. Thus, Mixtral only requires a subset of the total parameters during decoding, thus allowing faster inference speed at low batch sizes and higher throughput at large batch sizes. 2.1 Sparse Mixture of Experts Figure 1 illustrates the Mixture of Experts (MoE) layer. Mixtral has 8 experts, and each input token is routed to two experts with different sets of weights. The final output is a weighted sum of the outputs of the expert networks, where the weights are determined by the output of the gating network. The number of experts (n) and the top K experts are hyperparameters that are set to 8 and 2 respectively. The number of experts, n determines the total or sparse parameter count while K determines the number of active parameters used for processing each input token. The MoE layer is applied independently per input token in lieu of the feed-forward sub-block of the original Transformer architecture. Each MoE layer can be run independently on a single GPU using a model parallelism distributed training strategy. 2.2 Mistral 7B Mixtral’s core architecture is similar to Mistral 7B, and therefore, a review of its architecture is relevant for a more comprehensive understanding of Mixtral. Mistral 7B is based on the Transformer architecture. In comparison to Llama, it has a few novel features that contribute to it surpassing Llama 2 (13B) on various benchmarks. 2.2.1 Grouped-Query Attention Grouped-Query Attention (GQA) is an extension of multi-query attention, which uses multiple query heads but single key and value heads. Popular language models like PaLM employ multi-query attention. GQA represents an interpolation between multi-head and multi-query attention with single key and value heads per subgroup of query heads. As shown in figure 2, GQA divides query heads into G groups, each of which shares a single key and query head. It is different to multi-query attention which shares single key and value heads across all query heads. GQA is an important feature as it significantly accelerates the speed of inference and also reduces the memory requirements during decoding. This enables the models to scale to higher batch sizes and higher throughput, which is a critical requirement for real-time AI applications. 2.2.2 Sliding Window Attention Sliding window attention (SQA), introduced in the Longformer architecture exploits the stacked layers of a Transformer to attend to information beyond the typical window size. SWA is designed to attend to a much longer sequence of tokens than vanilla attention, and also offers significant reductions in computational cost. The combination of GQA and SWA collectively enhance the performance of Mistral 7B and therefore Mixtral relative to other language models like the Llama series.  Figure 2. A comparison of the configuration of key, value and query heads for GQA vs. multi-head and multi-query attention. 3. Performance 3.1 Standard benchmarks The authors of Mixtral benchmarked the performance of the model on a range of standard benchmarks and evaluated the accuracy of Mixtral versus leading language models like Llama 1, Llama 2, and GPT3.5 as shown in figure 3, table 1, and table 2. In summary, Mixtral is better than much larger language models with up to 70B parameters like Llama 2 70B while only using 13B (~18.5%) of the active parameters during inference. Mixtral’s performance is especially superior in tasks focused on mathematics, code generation, as well as multilingual comprehension. 3.2 Multilingual understanding Table 3 shows the performance of Mixtral versus Llama models on multilingual benchmarks. As Mixtral was pretrained with a significantly higher proportion of multilingual data, it is able to outperform Llama 2 70B on multilingual tasks in French, German, Spanish, and Italian while being comparable in English. 3.3 Long-range performance As shown in figure 4, the input context length of language models has increased by several orders of magnitude in the last few years - from 512 tokens for the BERT model to 200k tokens for Claude 2. However, most large language models struggle to efficiently use the longer context. Nelson and colleagues showed that current language models do not robustly make use of information in long input contexts, and their performance is typically highest when the relevant information for tasks such as question-answering or key-value retrieval occurs at the beginning or the end of the input context, with significantly degraded performance when the the models need to access information in the middle of long contexts. Mixtral, which has a context size of 32k tokens, overcomes this deficit of large language models and shows 100% retrieval accuracy regardless of the context length or the position of the key to be retrieved in a long context. The perplexity, a metric that captures the capability of a language model to predict the next word given the context, decreases monotonically as the context length increases. Lower perplexity implies higher accuracy, and the Mixtral model is therefore capable of extremely good performance on tasks based on long context lengths as shown in figure 5.  Figure 3. Performance of Mixtral in comparison to Llama 1 and 2 models of different sizes.  Figure 4. Evolution of the context length of large language models.  Figure 5. Long range performance of Mixtral.  Table 1. Mixtral outperforms Llama 2 70B model on almost all benchmarks while using less than 1/5th of the active parameters during inference.  Table 2. Mixtral outperforms or matches the performance of Llama 2 70B and GPT-3.5 on most benchmarks.  Table 3. Mixtral’s performance on multilingual benchmarks for French, German, Spanish and Italian versus Llama 1 and 2 models. 4. Instruction Fine-tuning Instruction tuning refers to the process of further training large language models on a curated dataset containing (instruction, output) pairs of training samples. Instruction tuning is a computationally efficient method for extending the capabilities of large language models in diverse domains without extensive retraining or architectural changes. “Mixtral - Instruct” model was fine-tuned on an instruction dataset followed by Direct Preference Optimization (DPO) on a paired feedback dataset. DPO is a technique to optimize large language models to adhere to human preferences without explicit reward modeling or reinforcement learning. As of January 26, 2024, on the standard LMSys Leaderboard, Mixtral - Instruct continues to be the best performing open-source large language model. This leaderboard is a crowdsourced open platform for evaluating large language models that ranks models following the Elo ranking system in chess. Mixtral - Instruct only ranks below proprietary models like OpenAI’s GPT-4, Google’s Bard and Anthropic’s Claude models, while being a significantly small model. This extremely strong performance of Mixtral - Instruct and with an open-source friendly Apache 2.0 license opens up the possibility for tremendous adoption of Mixtral for both commercial and non-commercial applications. It represents a much more powerful alternative to Llama 2 70B that is already being used as the foundational model for extending large language models to other languages like Hindi or Tamil that are spoken widely but not adequately represented in the training dataset of these large language models.  Figure 6. Mixtral is the best performing open-source large language model on the LMSys Leaderboard. 5. Use Cases
Mixtral represents the numero uno of open-source large language models as it clearly outperforms the previous best open-source model, Llama 2 70B, by a significant margin, while providing for faster and cheaper inference. At the time of writing this article, Mixtral has been available in the open-source for less than two months and we are yet to see many examples of how it is being used in the industry. However, there are some early movers, like the Brave browser that has already incorporated Mixtral in its AI-based browser assistant, Leo. Mixtral is also incorporated by Brave for powering its [programming-related queries in Brave Search. It is only a matter of time before Mixtral witnesses widespread adoption across industry for a variety of use cases and challenges the hegemony of proprietary models like OpenAI’s GPT-4 and the likes. 6. Conclusion Mixtral is a cutting-edge, mixture-of-experts model with state-of-the-art performance among open-source models. It consistently outperforms Llama 2 70B on a variety of benchmarks while having 5x fewer active parameters during inference. It thus allows for a faster, more accurate and cost-effective performance for diverse tasks including mathematics, code generation, as well as multilingual understanding. Mixtral - Instruct also outperforms proprietary models such as Gemini-Pro, Claude-2.1, GPT-3.5 Turbo on human evaluation benchmarks. Mixtral thus represents a powerful alternative to the much larger and more compute intensive Llama 2 70B as the de facto best open-source model, and will facilitate development of new methods and applications benefitting a wide variety of domains and industries. |
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