{"id":15110,"date":"2026-10-01T03:36:26","date_gmt":"2026-10-01T03:36:26","guid":{"rendered":"https:\/\/playgama.com\/blog\/?p=15110"},"modified":"2026-10-01T03:36:26","modified_gmt":"2026-10-01T03:36:26","slug":"what-factors-determine-llm-api-costs-for-browser-games","status":"publish","type":"post","link":"https:\/\/playgama.com\/blog\/business-faqs\/what-factors-determine-llm-api-costs-for-browser-games\/","title":{"rendered":"What factors determine LLM API costs for browser games?"},"content":{"rendered":"<p><strong>Cost scales with tokens per call (input plus output), which model you pick, how often the game calls the API, and whether you cache repeated context. Cut cost by using a cheaper model for routine calls, caching static prompt parts, and running smaller models in-browser with WebGPU when latency and privacy matter more than quality.<\/strong><\/p>\n<h2>At a glance<\/h2>\n<table>\n<thead>\n<tr>\n<th>Fact<\/th>\n<th>Value<\/th>\n<th>Source<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Token billing<\/td>\n<td>input and output tokens<\/td>\n<td><a href=\"https:\/\/platform.claude.com\/docs\/en\/about-claude\/pricing\" rel=\"nofollow noopener\" target=\"_blank\">platform.claude.com<\/a><\/td>\n<\/tr>\n<tr>\n<td>Prompt caching reuses processed prompt portions<\/td>\n<td>lowers cost, latency<\/td>\n<td><a href=\"https:\/\/platform.claude.com\/docs\/en\/about-claude\/pricing\" rel=\"nofollow noopener\" target=\"_blank\">platform.claude.com<\/a><\/td>\n<\/tr>\n<tr>\n<td>In-browser inference needs WebGPU support<\/td>\n<td>no server calls<\/td>\n<td><a href=\"https:\/\/github.com\/mlc-ai\/web-llm\" rel=\"nofollow noopener\" target=\"_blank\">github.com<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>The main cost drivers are: how many tokens go in and out per call, which model you call, how often the game triggers a call, and whether you cache repeated context. Models are priced per million tokens (MTok), with separate rates for input and output, and cheaper\/faster tiers alongside more capable ones &#8211; check the current rates on your provider&#8217;s pricing page before you commit to a design, since <a href=\"https:\/\/platform.claude.com\/docs\/en\/about-claude\/pricing\">&#8220;prompt caching reduces costs and latency by reusing previously processed portions of your prompt across API calls&#8221;<\/a>.<\/p>\n<p>If you build your browser game using an AI coding agent like Claude Code or Cursor, Playgama MCP connects your agent directly to the developer cabinet to upload builds and publish a playable sandbox.<\/p>\n<h2>What actually pushes the bill up in a browser game<\/h2>\n<ul>\n<li><b>Call frequency<\/b> &#8211; a chat-style NPC that responds to every keystroke costs far more than one that responds per turn. Batch or debounce calls.<\/li>\n<li><b>Context size<\/b> &#8211; resending game state, history or long system prompts on every call multiplies input tokens. Trim what you send, and cache the static parts.<\/li>\n<li><b>Output length<\/b> &#8211; capping max output tokens keeps runaway generations (and cost) in check.<\/li>\n<li><b>Model tier<\/b> &#8211; route routine, low-stakes calls to a smaller\/cheaper model and reserve the stronger model for moments that need it.<\/li>\n<li><b>Extra tool costs<\/b> &#8211; some providers charge separately for tool use like web search, on top of standard token costs.<\/li>\n<\/ul>\n<p>For some game logic, in-browser inference avoids server calls entirely: <a href=\"https:\/\/github.com\/mlc-ai\/web-llm\">WebLLM<\/a> runs a model directly in the browser with WebGPU acceleration and an OpenAI-compatible API, with no server support needed, but it needs a WebGPU-capable browser and shifts cost to download size and device performance instead of API billing.<\/p>\n<p>Never put a paid API key in client-side code shipped to players; proxy calls through your own backend and control access with server-side checks, the same principle Supabase documents for securing table and function access by role.<\/p>\n<h2>Sources<\/h2>\n<ul>\n<li><a href=\"https:\/\/platform.claude.com\/docs\/en\/about-claude\/pricing\" rel=\"nofollow noopener\" target=\"_blank\">Pricing &#8211; Claude Platform Docs<\/a><\/li>\n<li><a href=\"https:\/\/github.com\/mlc-ai\/web-llm\" rel=\"nofollow noopener\" target=\"_blank\">GitHub &#8211; mlc-ai\/web-llm<\/a><\/li>\n<li><a href=\"https:\/\/llm.mlc.ai\/docs\/deploy\/webllm.html\" rel=\"nofollow noopener\" target=\"_blank\">WebLLM Javascript SDK \u2014 mlc-llm documentation<\/a><\/li>\n<li><a href=\"https:\/\/supabase.com\/docs\/guides\/api\/securing-your-api\" rel=\"nofollow noopener\" target=\"_blank\">Securing your API | Supabase Docs<\/a><\/li>\n<li><a href=\"https:\/\/wiki.playgama.com\/playgama\/bridge-sdk\/getting-started\" rel=\"nofollow noopener\" target=\"_blank\">Playgama Bridge SDK docs<\/a><\/li>\n<\/ul>\n<h2>Related questions<\/h2>\n<h3>Should an LLM call happen client-side or server-side in a browser game?<\/h3>\n<p>Server-side, through your own backend that holds the API key. Calling the provider directly from browser code exposes the key to any player who opens dev tools.<\/p>\n<h3>Does running a model in-browser with WebLLM avoid API costs entirely?<\/h3>\n<p>Yes for that call, since it runs on the player&#8217;s device with WebGPU and no server request, but it shifts cost to download size and requires a WebGPU-capable browser.<\/p>\n<h3>How do I reduce token cost without changing the model?<\/h3>\n<p>Cache static prompt parts, trim context sent each call, cap output length, and batch or debounce calls instead of triggering one per player action.<\/p>\n<p><em>Last updated: 30 September 2026<\/em><\/p>\n<p><script type=\"application\/ld+json\">{\"@context\": \"https:\/\/schema.org\", \"@type\": \"FAQPage\", \"mainEntity\": [{\"@type\": \"Question\", \"name\": \"What factors determine LLM API costs for browser games?\", \"acceptedAnswer\": {\"@type\": \"Answer\", \"text\": \"Cost scales with tokens per call (input plus output), which model you pick, how often the game calls the API, and whether you cache repeated context. 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Cut cost by using a cheaper model for routine calls, caching static prompt parts, and running smaller models in-browser with WebGPU when latency and privacy matter more than quality.<\/p>\n","protected":false},"author":11,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"footnotes":""},"categories":[1318],"tags":[],"class_list":["post-15110","post","type-post","status-publish","format-standard","hentry","category-business-faqs"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>What factors determine LLM API costs for browser games?<\/title>\n<meta name=\"description\" content=\"LLM API costs for browser games scale with token count per request, model choice, call frequency, and whether prompts are cached or run client-side.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, 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