{"id":1099,"date":"2026-08-01T09:30:05","date_gmt":"2026-08-01T13:30:05","guid":{"rendered":"https:\/\/www.findstr.com\/?p=1099"},"modified":"2026-08-06T14:35:45","modified_gmt":"2026-08-06T18:35:45","slug":"control-enterprise-ai-token-costs","status":"publish","type":"post","link":"https:\/\/www.findstr.com\/en\/news\/control-enterprise-ai-token-costs\/","title":{"rendered":"How Can Enterprises Control Rising AI Token Costs?"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">Artificial intelligence is moving rapidly from experimentation to everyday enterprise use. More employees are consulting AI models, larger collections of documents are being analyzed, and increasingly autonomous agents are completing multistep tasks.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">As adoption grows, so does token consumption.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A token is a small unit of information processed by an artificial intelligence model. Organizations may be charged for the tokens sent to a model, the tokens generated in its response, and additional services such as web searches, tool usage, caching, storage or agent runtime.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The price of an individual token does not necessarily increase over time. In fact, less expensive models continue to enter the market. However, an organization\u2019s total AI bill can still rise quickly as usage expands across employees, departments, documents, models and automated workflows.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">According to the Findstr design team, the next major enterprise AI challenge will not simply be adoption. It will be making AI usage <strong>visible, connected, structured and financially controlled<\/strong>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>The Answer in Brief<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Enterprises can control AI costs by centralizing access, monitoring token consumption, selecting the right model for each task, reducing unnecessary context, setting usage policies and measuring cost against business outcomes.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Findstr provides a structured enterprise AI environment designed to connect organizational knowledge, centralize access to different AI models and give organizations greater visibility over how artificial intelligence is being used.<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>What Is an AI Token?<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A token is a unit used by artificial intelligence models to process information. A token may represent a word, part of a word, punctuation or another element of the content submitted to the model.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Both the request and the generated answer can consume tokens.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For example, an organization may be billed for:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>input tokens contained in a question, instruction or document;<\/li>\n\n\n\n<li>output tokens generated by the model;<\/li>\n\n\n\n<li>cached tokens used to retrieve previously processed context;<\/li>\n\n\n\n<li>content returned by connected search or retrieval tools;<\/li>\n\n\n\n<li>additional tools or services activated during a request;<\/li>\n\n\n\n<li>the runtime of certain managed AI agents.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">OpenAI states that API usage is priced according to the selected model and the volume of input, cached and output tokens. Anthropic similarly applies model-specific token pricing and may add charges for services such as web searches or managed-agent runtime. Google Cloud publishes separate input, output, caching and batch-processing rates across a wide variety of first-party and partner models.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This means that the cost of a request depends on much more than the length of the final answer.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Why Are Enterprise AI Costs Increasing?<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Enterprise AI costs are increasing primarily because organizations are consuming more artificial intelligence, not necessarily because every token is becoming more expensive.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">AI is increasingly being used to:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>analyze extensive document libraries;<\/li>\n\n\n\n<li>search internal organizational knowledge;<\/li>\n\n\n\n<li>summarize meetings and communications;<\/li>\n\n\n\n<li>produce and revise content;<\/li>\n\n\n\n<li>assist employees and customer-service teams;<\/li>\n\n\n\n<li>compare data from multiple sources;<\/li>\n\n\n\n<li>automate repetitive business processes;<\/li>\n\n\n\n<li>complete multistep agentic workflows.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">A traditional chatbot may answer a question in one or two exchanges. An AI agent may need to develop a plan, retrieve information, call external tools, review the results, correct its work and repeat several steps.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Each step can add more context and more tokens.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">McKinsey reported in July 2026 that agentic tasks can consume roughly 1,000 times more tokens than a single-turn code-reasoning task or a conventional coding conversation. It also noted that poorly managed context can become a recurring cost throughout every stage of a workflow.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A 2026 academic study examining eight frontier models found that token usage for the same agentic task could vary by as much as 30 times between runs. The researchers also found that higher token consumption did not automatically produce higher accuracy.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The signal is clear: <strong>more tokens do not always mean more value<\/strong>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Why Is AI Spending Difficult to Predict?<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">AI expenses are variable because the cost of a task can depend on:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>the model selected;<\/li>\n\n\n\n<li>the length of the request;<\/li>\n\n\n\n<li>the size of the documents provided;<\/li>\n\n\n\n<li>the amount of conversation history retained;<\/li>\n\n\n\n<li>the length of the generated answer;<\/li>\n\n\n\n<li>the number of tools called;<\/li>\n\n\n\n<li>the number of steps completed;<\/li>\n\n\n\n<li>the number of retries or corrections;<\/li>\n\n\n\n<li>the use of search, image, audio or other services;<\/li>\n\n\n\n<li>whether previously processed context can be reused.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Long-context requests may also be billed differently from shorter requests. Google Cloud, for example, publishes different pricing structures for certain models when the context exceeds a specified token threshold.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This converts part of the organization\u2019s technology budget from a predictable subscription expense into a variable operating expense.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Without centralized visibility, separate teams may subscribe to overlapping services, use premium models for simple tasks or repeatedly process the same documents without realizing the cumulative cost.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Seven Ways Enterprises Can Control AI Costs<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>1. Centralize AI Access<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A shared enterprise portal can replace a fragmented collection of personal subscriptions, departmental tools and disconnected interfaces.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Centralization makes it easier to understand:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>who is using AI;<\/li>\n\n\n\n<li>which models are being used;<\/li>\n\n\n\n<li>what types of tasks are being performed;<\/li>\n\n\n\n<li>where demand is increasing;<\/li>\n\n\n\n<li>and where costs may be duplicated.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Instead of searching through the noise of multiple platforms, the organization gains a clearer point of access to its artificial intelligence resources.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>2. Use the Right Model for Each Task<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The most powerful model is not always required.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A smaller or more economical model may be sufficient for classification, extraction, translation, formatting or a straightforward summary. A more advanced model can then be reserved for complex reasoning, strategic analysis or tasks with higher accuracy requirements.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This process is often described as <strong>model routing<\/strong>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Google Cloud\u2019s published pricing illustrates the significant cost differences that can exist between models, including different rates for input, output, caching and batch processing.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Choosing the right model allows organizations to find the appropriate balance between quality, speed and cost.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>3. Monitor Consumption by Team and Use Case<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A total monthly invoice provides limited insight.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Organizations need to connect consumption to its context:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>department;<\/li>\n\n\n\n<li>project;<\/li>\n\n\n\n<li>user group;<\/li>\n\n\n\n<li>model;<\/li>\n\n\n\n<li>assistant;<\/li>\n\n\n\n<li>workflow;<\/li>\n\n\n\n<li>business objective.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">This helps identify where AI creates value and where token consumption may simply be generating additional noise.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>4. Reduce Unnecessary Context<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Longer prompts and larger document collections are not always better.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Context should be selected according to the question being asked. Sending an entire document repository to answer a narrow question can increase costs and reduce the precision of the response.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Anthropic\u2019s documentation provides examples showing how the token volume of retrieved content can rise substantially between a typical webpage, a large documentation page and a research paper. It recommends setting content limits to prevent unexpectedly large retrievals.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A well-structured knowledge environment helps surface the right information rather than repeatedly sending everything.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>5. Reuse Stable Knowledge<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Organizations often transmit the same policies, product information, procedures or instructions with every request.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Caching and structured retrieval can reduce the need to process identical context repeatedly. Many model providers now publish separate rates for cached input because retrieving stable information can be more economical than processing it again from the beginning.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The objective is to help knowledge find its way to the model efficiently.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>6. Establish Usage Rules and Limits<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Enterprises can define:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>which models are available;<\/li>\n\n\n\n<li>which teams may access premium models;<\/li>\n\n\n\n<li>maximum document or context sizes;<\/li>\n\n\n\n<li>monthly budgets;<\/li>\n\n\n\n<li>project-specific limits;<\/li>\n\n\n\n<li>approved AI assistants;<\/li>\n\n\n\n<li>escalation rules for expensive tasks.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Spend controls are already becoming a standard enterprise requirement. OpenAI\u2019s business offering, for example, identifies centralized billing, usage analytics, budgeting and spend controls as organizational features. Anthropic also documents organization-level spend limits for API usage.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>7. Measure Cost per Outcome<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The cost per token is useful, but the more important measurement is often the <strong>cost per valuable result<\/strong>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">An organization should compare AI expenses with outcomes such as:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>employee time saved;<\/li>\n\n\n\n<li>requests resolved;<\/li>\n\n\n\n<li>documents processed;<\/li>\n\n\n\n<li>response time reduced;<\/li>\n\n\n\n<li>content produced;<\/li>\n\n\n\n<li>errors prevented;<\/li>\n\n\n\n<li>opportunities identified;<\/li>\n\n\n\n<li>decisions accelerated.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">A low-cost model that repeatedly produces unusable results may ultimately be more expensive than a stronger model that completes the task correctly.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The right signal is not necessarily the lowest token price. It is the best relationship between cost, quality and organizational value.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>How Findstr Helps Organizations Control AI Costs<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Findstr is a secure enterprise AI platform designed to centralize artificial intelligence, connect organizational knowledge and structure how AI is used across teams.<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Instead of allowing artificial intelligence to remain scattered across individual accounts and disconnected tools, Findstr provides a common environment through which organizations can develop a more controlled approach.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Depending on the organization\u2019s configuration and selected integrations, this approach can help create greater visibility into:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>available AI models;<\/li>\n\n\n\n<li>organizational access;<\/li>\n\n\n\n<li>shared assistants;<\/li>\n\n\n\n<li>connected knowledge sources;<\/li>\n\n\n\n<li>team usage;<\/li>\n\n\n\n<li>approved workflows;<\/li>\n\n\n\n<li>governance rules;<\/li>\n\n\n\n<li>and overall consumption.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Findstr does not aim to slow down AI adoption. It helps organizations bring structure to it.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">By connecting the right model, the right knowledge and the right user, an organization can reduce unnecessary processing and make more informed choices about where advanced artificial intelligence is truly required.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Does Findstr Replace ChatGPT, Claude or Gemini?<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">No.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Findstr is not positioned as a replacement for every artificial intelligence model. It acts as an enterprise layer through which organizations can structure access to selected models, tools and knowledge sources.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The organization can select technologies according to its:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>business requirements;<\/li>\n\n\n\n<li>security policies;<\/li>\n\n\n\n<li>data-governance framework;<\/li>\n\n\n\n<li>performance expectations;<\/li>\n\n\n\n<li>budget;<\/li>\n\n\n\n<li>and preferred technology strategy.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">This model-independent approach also reduces the need to rebuild every employee workflow when a new model becomes available.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>From Token Consumption to Structured Intelligence<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">AI costs should not be managed by preventing employees from using artificial intelligence.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">They should be managed by creating visibility, establishing rules and connecting usage to business value.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">As AI evolves from individual conversations toward autonomous agents and connected workflows, organizations will need to ask more precise questions:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Which task is being performed?<\/li>\n\n\n\n<li>Which knowledge is actually required?<\/li>\n\n\n\n<li>Which model is appropriate?<\/li>\n\n\n\n<li>How many steps should the process include?<\/li>\n\n\n\n<li>What did the result cost?<\/li>\n\n\n\n<li>What value did the organization receive?<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">These questions transform token consumption from an invisible technical measure into a manageable business resource.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Find the signal. Connect the knowledge. Structure the intelligence. Control the cost.<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Discover Findstr and speak with our team about creating a more structured enterprise AI environment.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Frequently Asked Questions<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>What causes AI token costs to increase?<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">AI token costs increase when organizations submit more requests, process larger documents, retain longer context, generate longer answers or deploy agents that complete multiple steps and use external tools. Total spending can therefore rise even when the unit price of certain models decreases.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>What is the difference between input and output tokens?<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Input tokens represent the instructions, questions, documents and contextual information sent to an AI model. Output tokens represent the content generated by the model. Providers frequently charge different rates for input and output tokens.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Can an enterprise set an AI budget?<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Yes. An organization can establish budgets, spending limits, approved models and access policies. Effective controls should be applied by team, project or use case rather than relying only on a single organization-wide monthly limit.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>How can a business reduce token consumption?<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A business can reduce token consumption by selecting only relevant context, limiting unnecessarily long responses, using caching, connecting a structured knowledge base, routing simple tasks to economical models and avoiding repeated processing of the same information.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Is the cheapest AI model always the best option?<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">No. A cheaper model may be appropriate for simple and repetitive tasks, but an advanced model may provide better value for complex work. Organizations should evaluate cost per successful outcome rather than cost per token alone.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>What is AI model routing?<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">AI model routing is the process of sending each request to the model that best matches its complexity, quality requirements, speed and budget. Simple requests may be handled by economical models, while advanced models are reserved for more demanding work.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Why can AI agents cost more than chatbots?<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">AI agents may plan, retrieve information, interact with tools, evaluate results, correct errors and repeat steps. These actions can create substantially more token consumption than a direct chatbot response. Research published in 2026 found that agentic workflows can show very large variations in token usage, even when completing the same task.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>How does Findstr help control enterprise AI costs?<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Findstr helps organizations create a centralized and structured environment for accessing artificial intelligence and connected organizational knowledge. This approach can support greater visibility, consistent governance and more deliberate model selection across teams.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Does Findstr require one specific AI model?<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">No. Findstr is designed as an enterprise AI environment that can structure access to selected models and knowledge sources according to organizational needs, configurations and policies.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Can Findstr help preserve organizational knowledge?<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Yes. Findstr is designed to connect organizational knowledge and make it more accessible within a shared AI environment, reducing the fragmentation created when information and effective practices remain isolated in individual accounts.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Sources<\/strong><\/p>\n\n\n\n<ol start=\"1\" class=\"wp-block-list\">\n<li><strong>OpenAI \u2014 API and business pricing documentation<\/strong>, consulted August 1, 2026. OpenAI documents model-based token pricing as well as enterprise budgeting and spend-control features.<\/li>\n\n\n\n<li><strong>Anthropic \u2014 Claude Platform pricing documentation<\/strong>, consulted August 1, 2026. Anthropic documents input and output pricing, search charges, context-related token usage and managed-agent runtime costs.<\/li>\n\n\n\n<li><strong>Google Cloud \u2014 Generative AI and Agent Platform pricing<\/strong>, consulted August 1, 2026. Google publishes model-specific rates for input, output, caching, batch processing and long-context usage.<\/li>\n\n\n\n<li><strong>McKinsey &amp; Company \u2014 \u201cIs That AI Agent Worth It? Agentic Economics and the Modern Operating Model,\u201d July 13, 2026.<\/strong> The article examines context, refinement, token consumption and cost variability in agentic workflows.<\/li>\n\n\n\n<li><strong>Bai et al. \u2014 \u201cHow Do AI Agents Spend Your Money? Analyzing and Predicting Token Consumption in Agentic Coding Tasks,\u201d April 2026.<\/strong> The study analyzes token consumption across eight frontier models and identifies significant cost variability in agentic tasks.<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"<p>Artificial intelligence is moving rapidly from experimentation to everyday enterprise use. More employees are consulting AI models, larger collections of documents are being analyzed, and increasingly autonomous agents are completing multistep tasks. As adoption grows, so does token consumption. A token is a small unit of information processed by an artificial intelligence model. Organizations may&hellip;<\/p>\n","protected":false},"author":1,"featured_media":1098,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[134],"tags":[],"class_list":["post-1099","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-enterprise-artificial-intelligence"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.findstr.com\/en\/wp-json\/wp\/v2\/posts\/1099","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.findstr.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.findstr.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.findstr.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.findstr.com\/en\/wp-json\/wp\/v2\/comments?post=1099"}],"version-history":[{"count":1,"href":"https:\/\/www.findstr.com\/en\/wp-json\/wp\/v2\/posts\/1099\/revisions"}],"predecessor-version":[{"id":1100,"href":"https:\/\/www.findstr.com\/en\/wp-json\/wp\/v2\/posts\/1099\/revisions\/1100"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.findstr.com\/en\/wp-json\/wp\/v2\/media\/1098"}],"wp:attachment":[{"href":"https:\/\/www.findstr.com\/en\/wp-json\/wp\/v2\/media?parent=1099"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.findstr.com\/en\/wp-json\/wp\/v2\/categories?post=1099"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.findstr.com\/en\/wp-json\/wp\/v2\/tags?post=1099"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}