Show HN: Tracking GenAI cost and endpoint fragility so app teams don't have to
LLMIntel is a demo dashboard for monitoring GenAI model usage costs, endpoint health, and optimization opportunities. It provides views for model status, cost analysis, usage trends, at-risk spend, and tag breakdowns, helping teams take action before model deprecation or cost spikes.
A sample LLMIntel dashboard with a week of realistic traffic across three apps.
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You’re viewing demo data — this is what LLMIntel looks like with a week of real traffic. Sign up and your own data appears minutes after your first instrumented call.
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Overview
6
Models you run
1
Need attention (≤90d / deprecated)
$0.05–5.32
Input $/1M spread
$0.4–16
Output $/1M spread
Lifecycle health
ModelStateRetirement
meta.llama3-1-405b-instruct-v1:0retiringin 21 days2026-07-28
Cost analysis
Pricing known for 6 of 6 models you run. Cheapest by input: gpt-5-nano-2025-08-07 at $0.05/1M.
Want cheaper/faster on-par alternatives for these models? See optimization →
Cost & usage
Times in UTC · 13 months of history on your plan · sample data — send telemetry to see your own, range-filtered
Filters
ProviderModelTag
Spend trend
Stack by
$1,284.06
Tracked spend · last 30 days ▲ +9.9%
412,000
Requests ▲ +7.5%
96M
Total tokens ▲ +11%
$13.32
Blended rate · $/1M tokens
−$48.74
Caching savings (est.)· 27M cached
$212.40
At-risk spend · retiring ≤90d
Cost is frozen at ingest — priced from each model's rates at call time, so past spend never shifts. Window: last 30 days.
Telemetry up to Jul 7, 16:41 UTC· 8 min ago· Catalog checked 42 min ago
Responses getting longer
These models are returning more output per input token than their 30-day baseline — the usual cause of silent cost creep. Check prompts, max_tokens, and whether a cheaper or more concise model would do.
ModelNowBaselineDriftRequestsSpend
claude-sonnet-4-5-202509290.3×0.2×+38%101,040$620.86
At-risk spend
Dollars flowing through models that retire within 90 days. Migrate before the provider 4xxs you — see recommended replacements on each model page.
ModelRetirementSpend
meta.llama3-1-405b-instruct-v1:0in 21 days2026-07-28$212.40
Spend by source
Environment › Application — expand any row to drill in
Group byFilter tag
Environment › ApplicationRequestsTokensBlended $/1MCost/reqSpendShare
prod379,00086M$13.16$0.0030$1,131.6688%
Checkout Assistant261,00058M$12.77$0.0028$742.1158%
Support Copilot118,00028M$13.96$0.0033$389.5530%
staging33,00010M$14.65$0.0046$152.4012%
Unassignedno app mapping33,00010M$14.65$0.0046$152.4012%
How is this allocated?
Choose the root dimension with Group by; the tree nests it over the next dimension in the rotation Environment → Application → API key. An application comes from the app tag your agent sends or the API key's app mapping; calls with neither show as Unassigned (map keys under Applications or send an app tag). Unattributed keys are usage recorded before per-key attribution shipped. Nothing is ever smeared across buckets, and the tree follows the active filters (including the tag filter above). Share is relative to the total spend in view.
Custom tag breakdowns
grouped by tag key
Tag: feature4 values
featureRequestsTokensBlended $/1MCost/reqSpendShare
checkout214,00048M$12.51$0.0028$604.3247%
search121,00028M$14.54$0.0033$401.1831%
summarize54,00013M$14.68$0.0035$187.9615%
Untaggedno feature tag23,0007.7M$11.77$0.0039$90.607%
Tag: team3 values
teamRequestsTokensBlended $/1MCost/reqSpendShare
growth268,00061M$12.75$0.0029$771.2460%
platform144,00033M$13.70$0.0031$452.2235%
Untaggedno team tag04.3M$14.09—$60.605%
Spend by model
ModelProviderRequestsInputOutputBlended $/1MCost/reqTokens/reqOut:inSpend
claude-sonnet-4-5-20250929Anthropic168,40031M
14M cached
8.5M$15.64$0.00372360.3×$620.86
gpt-5-2025-08-07OpenAI96,80022M
9.0M cached
6.9M$9.98$0.00303030.3×$292.40
gpt-5-mini-2025-08-07OpenAI74,30012M
3.6M cached
3.4M$2.09$0.00042090.3×$32.40
gpt-5-nano-2025-08-07OpenAI41,9005.6M1.5M$1.13$0.00021690.3×$8.00
gpt-4o (2024-08-06)Azure AI Foundry22,6002.9M900k$31.05$0.00521680.3×$118.00
meta.llama3-1-405b-instruct-v1:0AWS Bedrock8,000780k220k$212.40$0.02661250.3×$212.40
Optimization
Optimization
Analysis: Artificial Analysis · advisory
gpt-5-2025-08-07
GPT-5 mini
input −80% · output −80% · same capability class
Compare
meta.llama3-1-405b-instruct-v1:0
Claude Sonnet 4.5switch to anthropic
current model retiring ≤90d · higher capability class · context 2.5×
Compare