Summary Bullets:

• As telemetry arrives, Observe adds structure that reflects each customer’s business, creating a context graph, which gives an AI agent a roadmap for querying data.

• Querying a structured database is quicker and burns far fewer tokens, which helps address the AI cost concerns that are top of mind among enterprises.

The need to manage costs and recognize a positive ROI are top concerns among organizations rolling out generative and agentic AI. Even the largest enterprises are taking a more careful look at token economics as they seek to scale the use of AI tools throughout their organizations. As employees, developers and agents all gain access to generative AI and use an ever-growing number of tokens, spending is skyrocketing. During the Snowflake World Tour 2026 in New York City in October, Jeremy Burton, former CEO of Observe and currently General Manager of Snowflake’s Observability business, notes that companies let chaos reign while they experimented with AI and now need to rein in token usage. One often-overlooked lever is context. The context graph helps agents investigate operational telemetry more efficiently, which can improve answers and reduce token usage in those workflows. For this reason, the impact of Snowflake’s acquisition of Observe, which helps organizations better structure and understand how data is being used, has the potential to manage overall enterprise AI spending as well.

Snowflake’s acquisition of Observe expands its observability portfolio. As telemetry arrives, Observe adds structure that reflects each customer’s business, creating a context graph that maps semantics and relationships across logs and metrics, which gives an AI agent a roadmap for querying data. An agent could dig through raw data, but querying a structured database is quicker and burns far fewer tokens. As Observe puts it, the company does not have better AI; it has better data. Observe ingests data based on OpenTelemetry open standards and can store it in Apache Iceberg formats, providing greater flexibility and control. It uses object storage, elastic compute, and interoperable standards to meet customers’ requests for flexibility and cost sensitive solutions.

Observe complements Snowflake Cortex AI Gateway, announced in July 2026. The gateway governs how Snowflake and third-party agents access tools, models and data. It routes requests to approved models based on cost, quality, and latency; allocates spend to teams and workloads; sets limits; and records agent actions. Observe Agent Observability goes deeper, tracking which tools agents access and correlating agent activity with backend telemetry. It adds more detail directly from the agents themselves, such as connections to underlying application and infrastructure behavior. Observe stores the telemetry in Snowflake on low-cost object storage. At the scale of telemetry data, scan-based queries using Observe’s architecture are cheaper and faster than using index-based tools. Together, Cortex AI Gateway and Observe provide an end-to-end picture of an agent, from chat client to gateway to back end. Plenty of tools observe the agent itself; Observe provides deeper insight into the surrounding telemetry.

Improved context sits at the center of several trends related to how data is accessed. Software is going headless, as users prefer prompts and APIs to complicated interfaces when interacting with information. Walled gardens are crumbling as APIs, CLIs, and MCP streamline access to information and enable AI agents to drive actions. Evaluations, which interrogate how an agent reached its answer in testing and production, depend on rich telemetry. The next opportunity lies in context graphs. Enterprises that structure their data first can make more efficient use of tokens. With AI bills climbing, vendors that offer solutions that reduce token usage offer a compelling value proposition.

The post Snowflake’s Observe Helps Businesses Rein in Token Usage by Improving Context appeared first on IT Connection.

Summary Bullets:

• Meta will need to shed its consumer-centric image to gain traction in the business market, even if it only targets smaller organizations.

• Convincing organizations that they can trust Meta to secure personal information and corporate intellectual property will take time and effort.

On Monday September 28, 2026, Meta announced plans to enter what is already a crowded enterprise AI market. It will develop Meta Enterprise Platform, a collection of tools and services that will help business customers implement their own AI strategies. Few details were shared other than the platform will incorporate Muse agent, Meta Business Agent, Muse API, Muse Code, and other solutions.

In addition to plans for an enterprise AI platform, Meta revealed it has developed a version of its Muse personal assistant for enterprises. Muse for Small Business, announced one day after Meta Enterprise Platform, expands on the consumer version of the Muse AI agent announced earlier this month. It can access a variety of enterprise applications, including Asana, Box, Canva, Dropbox, Figma, Granola, HighLevel, Intuit QuickBooks, Klaviyo, Lovable, Notion, Shopify, Slack, Stripe, Zoom, and Facebook and Instagram business accounts. A limited amount of Muse usage is available for free; customers that want to consume it at higher volumes can purchase subscription plans based on token usage.

Meta will need to shed its consumer-centric image to gain traction in the business market, even if it only targets smaller organizations. Bringing onboard a strong enterprise focused leader, Chirantan Desai, as the ‘Chief Enterprise Platform Officer’ is a good first step. The industry veteran has extensive experience gained at MongoDB, Cloudflare, and ServiceNow, which are well respected enterprise-focused brands. However, he will be fighting an uphill battle as he seeks to position Meta as a leading provider of enterprise-grade solutions. The Meta (and Facebook) brands are strongly associated with consumer services and devices, further bolstered by the recent buzz around its AI glasses and Meta Quest googles, which though intriguing, have yet to see serious application by the business community.

Furthermore, privacy concerns abound regarding Meta/Facebook. The company built a business model based upon the sharing of information, which is the antithesis of a corporate approach to data management. Convincing organizations of all sizes that Meta has the pedigree and experience to offer secure solutions to which they can entrust the personal information of customers and employees, as well as corporate intellectual property, will take time and effort.

Additionally, Meta is seeking to enter a very crowded market. All major software vendors, such as Oracle, ServiceNow, Microsoft, and Salesforce offer platforms on which their customers can create AI agents. And most claim to integrate with outside data sources. The large hyperscale cloud providers are already major players in the enterprise AI platform space, with Google and AWS positioned as leaders in GlobalData’s latest Cloud-based Enterprise AI Platform Competitive Landscape Assessment report.

Out-of-the-box solutions that can be customized are only a fraction of what is required for a successful enterprise AI strategy. Leaders in the market for enterprise AI platforms need to demonstrate they have the tools to implement responsible AI, including guardrails and harnesses for agentic AI. They will also need to offer observability and orchestration across agents and language models, as well as integration with predictive AI. And as implementations become more complex, the ability to offer professional services, whether in-house or in partnership with consultants and systems integrators, will be critical to helping customers deploy a strategy that focuses on value and business outcomes.

The post Meta Will Need to Shed its Consumer-centric Image to Gain Ground in Enterprise AI appeared first on IT Connection.

Summary Bullets:

• After a period of ‘token maxxing,’ organizations are looking to reign in and better control inference costs.

• Instead, enterprises are now embracing ‘value-maxxing,’ which focuses on outcomes.

Last week, Google announced several enhancements to Gemini Enterprise designed to help enterprises obtain greater and faster ROI on their AI projects. The improvements address one of the biggest frustrations expressed by organizations today, namely that the benefits promised by AI are taking too long to realize. Companies are clamoring for domain specific solutions in order to speed the deployment, reduce the integration complexity, and increase the value obtained from AI projects. Additionally, business leaders are eager for better tools to help them manage AI costs. They are looking for improved visibility on token use and costs, more proactive spending controls, and more flexible payment options.

On Tuesday, August 25th, Google announced Gemini Enterprise for Financial Services and Gemini Enterprise for Legal. The industry-specific solutions include out-of-the box AI capabilities such as specialized agents that provide shortcuts for directing workflows, data connectors, and sector-optimized models. Reusable packages of instructions teach AI agents to perform specialized tasks that are customized to meet company-specific requirements; connectors link agents to internal systems and data while maintaining access controls; pre-built agents are available to deploy out of the box; and software provider partnerships facilitate industry-specific customization and integration, while avoiding vendor lock-in. Though initially rolled out for the financial services and legal industries, Google plans to offer similar solutions for other industries, including healthcare, life sciences, and professional services.

The following day, August 26th, Google revealed expanded tools for managing AI spending. It announced that Google Antigravity, its AI agent development platform, and Android Studio, for building applications, will now be included in Gemini Enterprise subscriptions. Usage across Antigravity, the platform, and the app rolls up into a single view instead of separate license and billing siloes. Furthermore, Google is providing expanded billing flexibility and new cost management tools for agent workloads across Gemini Enterprise. Customers can purchase a mix of per-seat subscriptions along with a new pay-as-you-go option, to help avoid hitting token caps in the middle of a job. Companies that commit to a minimum monthly spend will receive discounts on token costs. To better control spending, Google has rolled out new guardrails that enable administrators to set limits on AI spend by project, help estimate agent runtime costs, and identify anomalies in spending. Project level guardrails can pause an agent when API call limits are reached; a FinOps agent provides spending summaries in natural language.

Google’s announcements directly address concerns many organizations have over the spend on AI inference. After a period of ‘token maxxing’ wherein greater token usage was associated with greater productivity, organizations are looking to rein in and better control inference costs. Despite declining token costs, overall consumption, and therefore spend, are skyrocketing. Thus, the industry is now embracing ‘value-maxxing,’ which focuses on outcomes. It seeks to identify and quantify results, whether they be improved performance, more insightful decisions, or greater efficiency.

Regardless of the jargon of the day, organizations are taking a more analytical and practical approach to cost, latency, and performance optimization. No longer is the fastest or most expensive model considered the best choice for all tasks; organizations are now recognizing that some workflows are served well enough by less intensive reasoning, and that the same level of accuracy is not required for all tasks. At the same time, many are considering open-source strategies, attracted to the potential of lower costs, ability to fine tune models, greater transparency, local deployment options, and the option of leveraging existing infrastructure investments. At the end of the day, the development of appropriate AI strategies relies heavily on a broader understanding of the business and its operating model; professionals that can combine this knowledge with technical AI expertise are invaluable.

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