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Empowering Enterprise Generative AI with Flexibility: Navigating the Model Landscape

The world of Generative AI (GenAI) is rapidly evolving, with a wide array of models available for businesses to leverage. These models can be broadly categorized into two types: closed-source (proprietary) and open-source models. Closed-source models, such as OpenAI’s GPT-4o, Anthropic’s Claude 3, or Google’s Gemini 1.5 Pro, are developed and maintained by private and public companies.

GPUs Public Preview: Run AI workloads on H100, A100, L40S, and more

Welcome to day two of Koyeb launch week. Today we're announcing not one, but two major pieces of news: Our lineup ranges from 20GB to 80GB of vRAM with A100 and H100 cards. You can now run high-precision calculations with FP64 instructions support and a gigantic 2TB/s of bandwidth on the H100. With prices ranging from $0.50/hr to $3.30/hr and always billed by the second, you'll be able to run training, fine-tuning, and inference workloads with a card adapted to your needs.

What is a Headless Data Architecture?

The headless data architecture. Is it a fad? Some marketecture? Or something real? In this video, Adam Bellemare takes you through the basics of the headless data architecture and why it’s beginning to emerge as its own respective pattern. Driven by the decoupling of data computation from storage, the headless data architecture provides the basis for a modular data ecosystem. Stream your data for near real-time low latency use cases, or convert it to an Iceberg table for analytical use cases.

Autoscaling GA: Scale Fast, Sleep Well, Don't Break the Bank

We are thrilled to kickstart this first launch week with autoscaling - now generally available! Our goal is to offer a global and serverless experience for your deployments. Autoscaling makes this vision a reality. Say goodbye to overpaying for unused resources and late-night alerts for unhealthy instances or underprovisioned resources! During the autoscaling public preview, we received key feedback around scaling factors.

Streamlit in Snowflake: Improved Customization, Performance and AI Capabilities

Snowflake’s mission is to mobilize the entire world’s data, and there are millions of data scientists and developers who don’t have access to full-stack engineering teams. It’s been our endeavor to bring the power of the AI Data Cloud to every individual developer, data scientist and machine learning engineer, so that they can build and share world-class data apps — all by themselves. Streamlit is an open source library that turns Python scripts into shareable web apps.

How to Turn a REST API Into a Data Stream with Kafka and Flink

In the space of APIs for consuming up-to-date data (say, events or state available within an hour of occurring) many API paradigms exist. There are file- or object-based paradigms, e.g., S3 access. There’s database access, e.g., direct Snowflake access. Last, we have decoupled client-server APIs, e.g., REST APIs, gRPC, webhooks, and streaming APIs.

Generative AI vs Predictive AI: Knowing the Differences

Generative AI has received the lion’s share of the press. With good reason—it’s revolutionizing the way we do work and do business. But it’s not the only game in town. Predictive AI also places a role across enterprise use cases like demand forecasting, maintenance, and customer experience. This blog will discuss these two types of AI: generative AI and predictive AI.

Where Does Data Governance Fit Into Hybrid Cloud?

At a time when artificial intelligence (AI) and tools like generative AI (GenAI) and large language models (LLMs) have exploded in popularity, getting the most out of organizational data is critical to driving business value and carving out a competitive market advantage. To reach that goal, more businesses are turning toward hybrid cloud infrastructure – with data on-premises, in the cloud, or both – as a means to tap into valuable data.

Using Moesif, AWS, and Stripe to Monetize Your AI APIs - Part 1: Integrating The Platforms

As the wave of AI sweeps through the technology landscape, many have hopped on board. Interestingly enough, and often overlooked, is that many AI capabilities are served through APIs. Fancy user interfaces integrate with the actual mechanisms where the magic happens: the APIs. So, when generating revenue through AI platforms, the APIs drive the revenue.