Uniphore raises $260M from Nvidia, AMD, Snowflake, Databricks, and more

Conversational automation platform Team Uniphore has raised $260 million in its Series F funding round from NVIDIA, AMD, Snowflake, and Databricks, along with existing investors New Enterprise Associates (NEA) and March Capital. The platform is used by more than 2,000 enterprises, including several Fortune 500 companies. Uniphore’s business model focuses on subscription-based software and AI deployment services for enterprises.

A group of investors including BlackRock, Nvidia, Microsoft, and Elon Musk's xAI has agreed to acquire Aligned Data Centers in a $40 billion deal. 🏬 Aligned operates nearly 80 data centers across the Americas, providing critical infrastructure for AI computing. This acquisition, the first by the Artificial Intelligence Infrastructure Partnership (AIP), underscores the intense race to expand AI infrastructure capacity amid surging demand for advanced computing power. The deal is expected to close in early 2026.

AI workloads are changing how organizations think about infrastructure, and GPU elasticity is becoming a defining challenge for platform teams everywhere. At KubeCon + CloudNativeCon North America 2025, Lukas Gentele will share how vCluster is helping teams bring Kubernetes-native autoscaling to any environment, from hyperscalers to neoclouds and bare metal. We’ve been working on integrating Karpenter, ClusterAPI, Terraform/OpenTofu, KubeVirt, and NVIDIA BCM to deliver dynamic GPU scaling that reduces idle time, simplifies platform operations, and enables consistent AI infrastructure across hybrid and multi-cloud setups. 🗓️ Monday, November 10 📍 Building B | Level 4 | Rooms B401–402

Uniphore garnered $260M in Series F funding from NVIDIA, AMD, Snowflake, and Databricks, keeping its valuation at $2.5 billion since its $400M raise in February 2022. The funds will boost platform capabilities, partnerships, and global expansion across the Americas, EMEA, and Asia. Uniphore's strategy includes acquisitions like Orby AI and Autonom8 to deepen automation. Founded in 2008, its Business AI Cloud serves 2,000+ enterprises, delivering efficiency improvements like 31% self-service rate increases and 500 hours monthly saved in support. Uniphore competes with Observe.AI, Kore.ai, and Cognigy. Read Full Article: https://lnkd.in/gi3hEWMv #AI #Funding #Automation #DeccanFounders #AMD #NVIDIA #Snowflake #Databricks

📈 Palantir Pops on Nvidia Partnership Palantir (PLTR +3.56%) is trading higher after announcing a new partnership with Nvidia — another boost for the company as it continues expanding its AI reach. The stock also recently hit a new all-time high, driven by rising demand for its AI platforms across government and enterprise clients. PLTR keeps stacking wins in the AI ecosystem. 🔥

There’s a newbie in town! Meet Cerebras: the new AI chip competitor to Nvidia that just raised $1.1 billion and is now valued at $8.1 billion. They're expanding to target cutting-edge AI workloads and are preparing for an IPO. Follow us for more tech and market updates! #modInsight #AIForBusiness #SmartOps #SmartWorkflow #WorkSmarter

The most exciting part is what it represents for mainstream compute. Cobalt 100 brings Arm performance into the same infrastructure where .NET, Python, and ONNX workloads already run today. That's HUGE. Developers can ship AI, API, and background services on the same silicon that's optimized for efficiency and throughput without rewriting the stack. Arm is no longer an alternative, it's the baseline. #azure #arm #cobalt100 #arm64 #dotnet #aiinfrastructure

Performance 🤝 Cost Efficiency 🤝 Scale These are the fundamental pillars of creating an infrastructure that can meet the needs of advancing GenAI workloads. This is exactly what Arm is doing - Arm Neoverse architecture is providing real-world results and demonstrating just how exciting this next era of AI innovation will be. Just look at this example demonstrating how Arm Neoverse-powered Microsoft Cobalt 100 processors and Arm’s ONNX Runtime optimizations deliver significant performance gains for running LLMs! https://okt.to/gWbqI8

This partnership is a special one. Key technical details shared in this article, along with real use cases that will benefit from it.

Last night I had the privilege of moderating a panel on "The Future of AI and Kubernetes" at KubeCon Atlanta. With AI workloads exploding in complexity and scale, we dove deep into how they're reshaping Kubernetes ecosystems. Our panelists, included Joshua Bucknor from JPMorganChase, Everett Lacey from the NVIDIA DGX SuperPOD team and Fabian Kramm CTO and Co-Founder of vCluster, brought invaluable insights. A quick recap of our conversation: 🔹 Challenging Kubernetes Norms: We explored surprising ways AI workloads upend traditional assumptions, like the need for dynamic resource allocation beyond static pods, handling massive data pipelines that strain etcd, and ensuring fault-tolerant orchestration for long-running training jobs. 🔹 GPU Scheduling Pain Points & Innovations: The conversation highlighted current bottlenecks in GPU workloads on Kubernetes, such as inefficient bin-packing and NUMA-aware scheduling. Exciting innovations like DRA and it's effect on optimizing sparse GPU resources. 🔹 Resource Sharing & Multi-Tenancy: With AI chips being premium assets, organizations are leaning into namespace isolation, quota enforcement via Kubernetes Resource Quotas, and custom schedulers. Joshua shared how JPMC tackles multi-tenancy in large-scale clusters to balance security and efficiency. Everett discussed customer demands for Kubernetes atop DGX SuperPODs, including hybrid multi-tenant setups. 🔹 Fractioning GPUs & The VM vs. Bare Metal Debate: We debated the shift from VMs for traditional workloads to bare-metal for GPUs, driven by performance needs in AI. 🔹 Advice for Newcomers: If you're just starting with AI on Kubernetes, the consensus? Prioritize observability early, integrate Prometheus and Grafana to monitor GPU utilization and avoid under-optimized clusters. Grateful for the engaging dialogue and fresh perspectives! If you're running AI on K8s, what's your biggest challenge right now? Drop a comment below, let's connect and discuss. #AI #Kubernetes #GPUs #CloudComputing #TechLeadership