Best AI Infrastructure Platforms for Salesforce

Find and compare the best AI Infrastructure platforms for Salesforce in 2024

Use the comparison tool below to compare the top AI Infrastructure platforms for Salesforce on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.

  • 1
    Klu Reviews
    Klu.ai, a Generative AI Platform, simplifies the design, deployment, and optimization of AI applications. Klu integrates your Large Language Models and incorporates data from diverse sources to give your applications unique context. Klu accelerates the building of applications using language models such as Anthropic Claude (Azure OpenAI), GPT-4 (Google's GPT-4), and over 15 others. It allows rapid prompt/model experiments, data collection and user feedback and model fine tuning while cost-effectively optimising performance. Ship prompt generation, chat experiences and workflows in minutes. Klu offers SDKs for all capabilities and an API-first strategy to enable developer productivity. Klu automatically provides abstractions to common LLM/GenAI usage cases, such as: LLM connectors and vector storage, prompt templates, observability and evaluation/testing tools.
  • 2
    Google Cloud TPU Reviews

    Google Cloud TPU

    Google

    $0.97 per chip-hour
    Machine learning has led to business and research breakthroughs in everything from network security to medical diagnosis. To make similar breakthroughs possible, we created the Tensor Processing unit (TPU). Cloud TPU is a custom-designed machine learning ASIC which powers Google products such as Translate, Photos and Search, Assistant, Assistant, and Gmail. Here are some ways you can use the TPU and machine-learning to accelerate your company's success, especially when it comes to scale. Cloud TPU is designed for cutting-edge machine learning models and AI services on Google Cloud. Its custom high-speed network provides over 100 petaflops performance in a single pod. This is enough computational power to transform any business or create the next breakthrough in research. It is similar to compiling code to train machine learning models. You need to update frequently and you want to do it as efficiently as possible. As apps are built, deployed, and improved, ML models must be trained repeatedly.
  • 3
    VectorShift Reviews
    Create, design, prototype and deploy custom AI workflows. Enhance customer engagement and team/personal productivity. Create and embed your website in just minutes. Connect your chatbot to your knowledge base. Instantly summarize and answer questions about audio, video, and website files. Create marketing copy, personalized emails, call summaries and graphics at large scale. Save time with a library of prebuilt pipelines, such as those for chatbots or document search. Share your pipelines to help the marketplace grow. Your data will not be stored on model providers' servers due to our zero-day retention policy and secure infrastructure. Our partnership begins with a free diagnostic, where we assess if your organization is AI-ready. We then create a roadmap to create a turnkey solution that fits into your processes.
  • 4
    Lemma Reviews
    Distributed workflows for production and prototype that are event-driven and span AI models, databases, APIs, ETL systems and applications. All on one platform. Reduce operational overheads and infrastructure complexity to enable a faster time-to-value for your organization. Focus on investing in proprietary logical and accelerating feature deliveries without wasting time with platform and architecture choices that slow down development and execution. Revolutionize emergency response through real-time transcription, keyword identification and keyphrase recognition, and integrated connectivity with external systems. Connect the physical and digital realms and optimize maintenance by monitoring sensors, creating a triage for operator review after an alert and creating service tickets on your work order platform. By generating responses based on data from various platforms, you can apply past experience to current problems in new ways.
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