Enterprise Account Executive

PointFive Raises $60M to tackle Enterprise AI costs. Key insights. * PointFive raised a $60 million Series B led by Accel, bringing total funding to $96 million for its cloud and AI cost management platform. * Founders Arvatz, Ben-David, and Hozez previously co-founded IntSights, acquired by Rapid7 for $350 million in 2021. * CEO Arvatz reports AI spending inside enterprises is increasing fivefold, driven by vendors shifting to consumption-based, token-measured pricing. Why this matters. Enterprises are facing AI budget runaway as consumption-based, token-measured pricing replaces predictable fixed contracts, and PointFive's $60 million raise signals that top-tier investors now treat AI cost management as a distinct, fundable infrastructure layer rather than a dashboard feature. The IntSights founding team's track record of a $350 million exit to Rapid7 lends real credibility to their ability to sell into and retain large enterprise accounts. Accel leading the round alongside Index Ventures and Salesforce Ventures suggests that both pure-play venture and strategic enterprise software money now see AI spend accountability as a standalone market. Summary. PointFive has closed a $60 million Series B led by Accel, with Index Ventures, Salesforce Ventures, Entrée Capital, Perpetual Growth, Vesey Ventures, and Sheva Ventures joining, bringing total funding to $96 million. Founded in 2023 by Alon Arvatz (CEO), Gal Ben-David (CPO), and Amir Hozez (CTO), all Unit 8200 graduates who previously built IntSights (acquired by Rapid7 for $350 million in 2021), the company now employs 100+ people across Tel Aviv, London, and the U.S. Essentially: (PointFive, Accel, Index Ventures) are betting that fivefold AI spend growth, combined with a shift to token-based consumption pricing, creates structural demand for dedicated cost management tooling. - CEO Arvatz: "Organizations are not ready for the pace of AI adoption...AI spending inside companies is increasing fivefold." - Vendors are moving from fixed subscriptions to consumption-based pricing measured in tokens, causing invoices to rise sharply and unpredictably. - PointFive's cloud and AI cost management platform targets this new cost visibility gap as a dedicated infrastructure layer. Budget accountability for AI workloads is now a category-defining enterprise problem that traditional cloud cost tools weren't built to address. Potential risks and opportunities. Risks. * Major cloud providers could build native AI spend dashboards directly into their existing cost consoles, commoditizing PointFive's core offering before the $96 million in total funding converts to durable recurring revenue. * Accel and Index Ventures as institutional lead investors will expect a clear growth trajectory toward a Series C or exit; if enterprise AI spending normalizes after the current fivefold surge, PointFive's growth narrative weakens at a critical fundraising moment. * Scaling 100+ employees across Tel Aviv, London, and the U.S. while adding implementation services alongside the SaaS platform increases cost structure complexity and could strain runway if enterprise deal cycles run long. Opportunities. * PointFive's cloud and AI cost management platform is positioned ahead of FinOps incumbents still retooling for token-based consumption pricing, giving it a first-mover window while the category is still being defined. * Salesforce Ventures' participation as an investor opens a potential distribution channel through Salesforce's existing enterprise customer relationships, accelerating go-to-market beyond direct sales. * The fivefold AI spending growth CEO Arvatz cites is a greenfield signal for the broader FinOps and cloud cost management ecosystem; PointFive's raise validates the category as fundable at scale and is likely to accelerate competitive investment. What Aiweekly don't know yet. * Post-money valuation was not disclosed in the article, despite $500 million figures appearing in pre-publication reporting. * Named enterprise customers and quantified cost savings are absent from the article, leaving the platform's demonstrated ROI unverified in public reporting. * Whether PointFive has expanded or plans to expand beyond cloud cost management into adjacent AI efficiency categories is not addressed. Originally reported by calcalistech.com Original headline: PointFive Raises $60M Series B at $500M Valuation to Build AI Efficiency OS as Enterprise AI Spending Rises Fivefold

NYSE content update: PointFive raises $60 million to redefine efficiency. NYSE issues a pre-market daily advisory direct from the trading floor. NEW YORK, June 8, 2026 /PRNewswire/ - The New York Stock Exchange (NYSE) provides a daily pre-market update directly from the NYSE Trading Floor. Access today's NYSE Pre-market update for market insights before trading begins. Ashley Mastronardi delivers the pre-market update on June 8th * Equities are higher Monday morning, even as tensions in the Middle East remained elevated over the weekend. * PointFive's Co-Founder and CPO Gal Ben David will join NYSE Live to disclose how the company will allocate $60 million in fresh capital. * Man Group's CEO Robyn Grew will join Taking Stock to discuss the firm's first two active ETFs, which recently began trading on NYSE Arca. * SEC Chairman Paul Atkins and NYSE Head of U.S. Listings Tara Dziedzic will speak at NIRI 2026 in Chicago, connecting key decision-makers in the IR community Opening Bell Colgate-Palmolive (NYSE: CL) celebrates its 220th anniversary of founding Closing Bell Man Group ETFs celebrates the launch of its first two active ETFs For market insights, IPO activity, and today's opening bell, download the NYSE TV App: TV.NYSE.com FONTE New York Stock Exchange

Why production AI costs spiral and how PointFive's DeepWaste AI targets the whole stack. Thu March 5, 2026 On February 27, 2026, PointFive introduced DeepWaste(TM) AI as a standalone module built to continuously optimize production AI across LLM services, GPU infrastructure, and AI data platforms across major providers. What changes in production is not just volume, but complexity. AI workloads become a web of interconnected decisions, how a request is routed, which model is selected, how tokens are allocated, when caching is applied, whether retries are happening quietly in the background, and how GPU resources are provisioned. Add data platform orchestration to the mix, and the same "AI" outcome can be achieved through very different, and differently priced, execution paths. Where inefficiency actually lives. PointFive frames inefficiency as a stack issue: model selection, token consumption, routing logic, caching behavior, GPU utilization, retry patterns, and data platform orchestration all shape AI cost and performance. These drivers often interact. A routing choice can increase token usage. A caching gap can turn repeat usage into repeat spend. A retry loop can inflate costs while also hurting latency. A GPU fleet can be oversized for peak load while staying underutilized at steady state. Even when teams are careful, the system can drift as workloads evolve. DeepWaste AI is positioned as the tool that reads these layers as one execution stack. PointFive argues that traditional cloud optimization tools weren't built to analyze AI-specific behavior across the stack, which leaves teams with fragmented visibility: one view for cloud spend, another for model usage, and yet another for infrastructure telemetry. What DeepWaste AI connects to. DeepWaste AI provides native, agentless connectivity across: * AWS (Bedrock, SageMaker, and AI managed services) * Azure (Azure OpenAI, Azure ML, Cognitive Services) * GCP (Vertex AI and AI services) * OpenAI and Anthropic direct APIs This matters in production because organizations frequently operate across clouds, and teams often mix provider-managed services with direct API usage. PointFive's approach is to normalize the signals that describe how AI services run so inefficiency can be detected consistently. Full-Stack means GPUs and data platforms, too. PointFive emphasizes that DeepWaste AI is not limited to inference-only visibility. On the GPU side, DeepWaste AI continuously identifies underutilized or idle GPUs, instance-type mismatches, OS and driver misconfigurations, and hardware-to-workload misalignment. These issues are often invisible if teams only look at aggregate spending; they show up in how resources are configured and how workloads actually behave. DeepWaste AI also extends into AI data platforms via native support for Snowflake and Databricks. The stated goal is end-to-end coverage from data ingestion through inference, tying upstream platform orchestration to downstream execution and costs. Agentless by default, with controls for deeper analysis. DeepWaste AI connects directly to cloud APIs, LLM service metrics, GPU telemetry, and billing systems without agents, instrumentation, or code changes. By default, it operates using metadata, billing signals, performance metrics, and resource configuration data, without requiring access to raw inference logs. PointFive positions this as privacy-preserving and designed to minimize data access requirements. For organizations that want more depth, optional inference-level analysis can be enabled to evaluate prompt architecture and orchestration logic. The company states customers control how deep the analysis goes and that optimization adapts accordingly. The four-layer detection model. DeepWaste AI structures and enriches invocations with task classification, routing context, cost attribution, and infrastructure alignment signals, then detects inefficiency across four layers: * Model & Routing Intelligence (model-task mismatch, downgrade opportunities, batch vs. real-time misalignment, benchmarking outliers) * Token & Prompt Economics (prompt bloat, context window overprovisioning, output inflation from misconfigured max_tokens, parameter-task misalignment, structural token waste) * Caching & Reuse Optimization (duplicate inference detection, underused caching, cache miss rate inefficiencies) * Infrastructure & Operational Leakage (idle GPUs, instance mismatch, driver-level throughput limits, retry-driven cost inflation, latency outliers, provisioning misalignment) PointFive's claim is that these detections are grounded in unified workload signals rather than surface-level billing anomalies. Turning findings into action. DeepWaste AI attaches quantified savings estimates and clear implementation guidance to findings. Recommendations are prioritized by financial impact and mapped to engineering and FinOps workflows so teams can evaluate projected savings before acting and track improvements over time. PointFive describes this as shifting from reactive monitoring to continuous optimization across models, infrastructure, and data platforms. Why full-stack optimization matters. "AI workloads introduce a new category of operational complexity," said Alon Arvatz, CEO of PointFive. "DeepWaste AI gives organizations the intelligence required to scale AI efficiently, across models, infrastructure, and data platforms, without sacrificing control." DeepWaste AI is now available to PointFive customers.