Corporate Account Executive

Roboflow Ranked #9 AI Product in G2's 2026 Awards. At Roboflow, its mission has always been to democratize computer vision. Roboflow, Inc. believe that giving developers the tools to make the world programmable will solve some of the world's hardest problems. Today, Roboflow, Inc. is proud to share a significant milestone in that journey: After being named the #1 Image Recognition on G2 in 2025, Roboflow is the #9 AI Product on the G2 2026 Best Software Awards list. Being ranked alongside OpenAI, ElevenLabs, Canva, and Notion is a testament to the success of the 1,000,000+ developers who are using Roboflow to bring vision AI into the mainstream. The AI landscape is more crowded than at any point in history with 18,312 total products being evaluated by G2. Vision entering the top 10 is a signal that AI's next major wave of understanding and interacting with the physical world has arrived. Visual AI adoption is growing rapidly. For years, vision AI was sidelined as a future technology reserved for big tech research labs with massive budgets. But that era has officially ended. This shift is critical because, while most AI currently lives in a browser tab, the most valuable AI of the next decade will live in the physical world. As agentic AI and robotics start to take center stage, vision becomes the primary sensory input for every autonomous system. In its 2026 Vision AI Trends report, Roboflow, Inc. analyzed 200,000 real-world projects to show how enterprises are successfully deploying AI to production across 10 global industries. Roboflow's climb into the Top 10 of G2's rankings is another direct reflection of the growing adoption and the real-world value users are finding in vision AI. With over a million vision projects and 250,000 models build with Roboflow, it's clear that the tools are ready for anyone to deploy models that track inventory or identify defects in real time. When you build with Roboflow, you are building on a platform trusted by the world's largest logistics companies such as BNSF, category leading manufacturers like USG, autonomous robotics companies like Peer Robotics, and AI-native healthcare companies like Wellth. A community-driven win. G2 rankings are unique because they aren't decided by a panel of judges; they are determined by the people actually using the software to build the future. Roboflow's ranking is thanks to the researchers, the hobbyists, and the enterprise engineers who have been contributing to the growth of the vision community. Roboflow, Inc. is honored to be your partner in building the foundational vision layer of the AI stack. Ready to see why Roboflow, Inc. is a Top 10 AI product? Start building for free or talk to its sales team to scale your vision AI project today. Cite this post. Use the following entry to cite this post in your research: Trevor Lynn. (Apr 10, 2026). Roboflow Ranked #9 AI Product in G2's 2026 Awards. Roboflow Blog: https://blog.roboflow.com/roboflow-g2-2026-top-ai-software/ Stay Connected Get the Latest in Computer Vision First Trevor Lynn Trevor leads Marketing at Roboflow. He focuses on sharing insights from Roboflow customers to inspire the broader AI community and help advance visual AI. Topics.

Inference 1.0: Foundational Infrastructure for Visual Understanding. Today, Roboflow is announcing Inference 1.0, the most trusted and reliable vision AI inference engine. As vision AI has entered a period of exponential growth, the technology must evolve to continue serving the needs of the ecosystem. Processing visual data like images, videos, and real-time streams at enterprise scale is compute intensive and models need to be optimized to fully utilize the latest GPUs for real-world applications. Inference 1.0 is built specifically to handle these unique requirements. The newest version is a modular execution engine with architectural maturity and high-performance, multi-backend support. This gives enterprises a single engine to deploy vision systems at the edge or in the cloud, grounded in four core primitives high scale visual understanding applications require: * Fast model loading and inference * Optimized CPU and GPU resource utilization * Modularity and extensibility * Separation between serving and model runtime "We upgraded to Roboflow inference 1.0, and it solves a big pain point for us thanks to TRT pre-compilation. Previously, TRT compilation happened on the fly, which blocked real-time processes on edge devices in our factories. Now, 1.0 allows the TRT models to use pre-compiled files, making it much easier to load new models across 40+ total streams. We also saw a 20-30% speedup using the API on our production server running 4 streams." - USG Corporation Over 1 billion inferences a week are already being processed with the new Inference 1.0 engine, supporting applications using models like RF-DETR, Segment Anything models, vision-language models (SmolVLM, Qwen3-VL, PaliGemma, Moondream2), OCR, embedding generation (CLIP, Perception Encoder) and YOLO architectures. Inference 1.0 upgrades and performance benchmarks. The new Inference engine increases throughput significantly across a variety of models on cloud and edge GPUs. The 1.0 release provides a new core execution engine with multi-backend support for ONNX, PyTorch, and TensorRT. This means the server automatically chooses the best runtime for the underlying hardware (e.g., utilizing CUDA/TensorRT on NVIDIA GPUs or optimized ONNX on CPUs) automatically. In addition to supporting different runtimes for general speed enhancements, 1.0 has optimizations that further improve overall latency. Cold-start logic reduces the time it takes to swap or load new models into memory, resulting in faster model loading and dynamic batching and multi-threading handles concurrent requests more effectively, optimizing GPU/CPU utilization during high-throughput scenarios, like processing 30fps video streams from multiple cameras. Beyond performance, the new release makes Inference easier to use in production. Cleaner abstraction between the serving layer (HTTP/REST/gRPC) and the model runtime makes the system more modular and easier to debug or extend. Whether you're deploying to a Jetson at the edge, a local Docker container, or a scalable Cloud cluster (GCP/Azure/AWS), the environment is abstracted away. One engine, deploy anywhere. You can self-host or use Inference 1.0 with Roboflow Cloud services. Inference allows you to use the exact same API and integration logic regardless of where a model or workflow is physically hosted. Use Inference 1.0 in Roboflow Cloud. For teams that prefer not to manage complex infrastructure, Inference 1.0 is available natively within Roboflow Cloud. This provides a fully managed, production-grade inference solution that scales effortlessly with your business needs. Whether you need dedicated deployments for sustained, real-time throughput or batch processing for large-scale asynchronous workloads, Inference 1.0 adapts to your specific use case. Inference is specifically tuned for vision AI, providing an optimized infrastructure that cuts compute costs significantly compared to alternative cloud providers. Beyond compute costs, Roboflow, Inc. handle the server orchestration and scaling spikes so your team doesn't have to manage the underlying infrastructure. Just like with the rest of the Roboflow platform, data privacy is built into the foundation of its cloud and Roboflow, Inc. never store your inference inputs or outputs. Roboflow Cloud is SOC 2 Type II certified and HIPAA compliant, ensuring you can confidently deploy mission-critical applications that meet the highest regulatory standards. Self-host Inference 1.0. Complete backwards compatibility makes Inference 1.0 a drop-in replacement for previous versions (enabled via the USE_INFERENCE_MODELS flag), allowing for easy testing and painless migration for anyone self-hosting Inference. With newly optimized runtimes, you'll achieve significantly better GPU utilization and higher overall output. This means you can process more inferences with less compute, actively driving down your hardware costs. For access to the optimized and pre-compiled TensorRT weights, connect with its Sales team. With the underlying hardware environment hidden behind a clean interface, you can change infrastructure without rewriting your application. If you decide to move your models from a local Docker container to a cloud instance, you don't have to overhaul your codebase. The Inference engine handles the translation to the new hardware in the background. Whether you deploy on edge devices or directly inside your own cloud infrastructure, self-hosting Inference gives you control over data residency. By ensuring your data never leaves your environment, it's easier than ever to meet strict in-house security requirements and adhere to regulatory standards like GDPR, HIPAA, and more. Conclusion. Inference 1.0 is built to power a future where all visual data can be turned into visual understanding. By stripping away hardware dependencies and infrastructure headaches, Roboflow, Inc. is giving enterprises the freedom to focus on what actually matters: solving real-world problems. Whether you're putting a model on a remote drone in the field or analyzing millions of images in the cloud, Inference is the fast, reliable foundation you need to make it happen. Roboflow, Inc. is moving vision AI out of the research lab and into everyday life, and Roboflow, Inc. can't wait to see the incredible things you create. Cite this post. Use the following entry to cite this post in your research: Trevor Lynn. (Mar 12, 2026). Inference 1.0: Foundational Infrastructure for Visual Understanding. Roboflow Blog: https://blog.roboflow.com/inference-1-0/ Stay Connected Get the Latest in Computer Vision First Trevor Lynn Trevor leads Marketing at Roboflow. He focuses on sharing insights from Roboflow customers to inspire the broader AI community and help advance visual AI. Topics.