Demo •

LLM Cost Analytics Dashboard (Demo) — Built for Devs & Tech Managers

See where your LLM budget goes—and how to cut it. This demo shows cost, efficiency, and developer time saved with DoCoreAI’s temperature tuning and prompt health—without sending your prompt content to our servers.

Open the Demo Try the ROI Calculator Book a 15-min Walkthrough
Sample Data
No prompt content stored
Install via PyPI in minutes
18–32% reduced LLM spend (typical)
12–25 hrs developer time saved / month
MoM track ROI across teams
LLM Cost & ROI Dashboard — token spend, time saved, prompt health
Live-style Demo

Preview the Management Dashboard

Below are sample charts with a simple Before/After toggle to show impact from temperature tuning and prompt health. Click “View insight” on any card for an executive summary.

Cost Savings (vs. baseline)
Savings
Mini chart: cost trend ↓ after optimization Cost savings chart over days
Sample data • Cost saved.
Est. 0–5% reduction pre-tuning
Before
After
Developer Time Saved
Productivity
Fewer retries & faster outputs Developer Time Saved bar chart over days
Sample data • Daily hours saved.
~3–6 hrs saved / engineer / mo
Before
After
ROI Index
Finance
Blended savings + time − infra ROI index over time showing uplift after optimization
Sample data • ROI Index (token savings + time value)
ROI trending flat
Before
After
Token Waste
Efficiency
Over-generation & unused tokens Total tokens vs bloated tokens over time
Sample data • Bloated tokens
High over-generation (>20%)
Before
After
Prompt Health Score
Quality
Structure • Clarity • Determinism Average bloat score trend
Sample data • Prompt health proxy
Inconsistent outputs (score 58)
Before
After
Time Saved by Role
Teams
Mini chart: model trade-offs (cost vs. success) Time saved by role bar chart
Sample data • Time saved by role.
Unknown distribution across teams
Before
After
Education

What You’ll Learn From the Dashboard

What is an LLM cost analytics dashboard?

An executive-friendly view of spend, efficiency, and outcome quality across teams. It centralizes telemetry—without storing your prompts—to guide cost cuts and stability improvements.

Read the primer →

Analyze GPT prompt efficiency

Measure over-generation, retries, and determinism to reduce token waste and engineering time. See how “prompt health” translates to lower costs and faster delivery.

Explore the guide →

Under the hood

How DoCoreAI Works (No Prompt Content Stored)

Client-side optimization

DoCoreAI’s client inspects each request locally and adjusts temperature and related parameters. Your original prompts and responses stay on your machine.

Server-side analytics

Only telemetry—timings, token counts, success rates—flows to the dashboard. This is enough to compute cost, efficiency, and ROI without seeing your content.

Prompts & outputs flow only between your app and LLM provider. DoCoreAI receives telemetry only (counts, timings, success) to compute cost, efficiency and ROI—no prompt content stored.
Your app / dev machine • Prompts & outputs stay on device • DoCoreAI client tunes params (temperature, max_tokens, stop) LLM provider(s) OpenAI, Anthropic, Groq, etc. DoCoreAI Cloud • Telemetry ingest (counts, timings, success) • Metrics store → dashboards & ROI content response (content) telemetry only Content path (app ↔ LLM) — never sent to DoCoreAI Telemetry only (counts, timings, success, model id) Prompts and outputs flow between your app and LLM provider. Only telemetry (no content) is sent to DoCoreAI for analytics.

Security →   Privacy →

Integrations

Integrations & Setup (Zero-code Drop-in)

  • Works with leading LLM providers.
  • Install via PyPI and keep your code unchanged.
  • Role-based prompts supported; telemetry auto-captured.

PyPI →   Docs →

OpenAI Supported
Anthropic (Coming soon) Coming soon
Groq Supported
Azure OpenAI Coming soon
Google Vertex AI In Testing
Mistral Coming soon 

pip install docoreai
Trust

Security & Governance

Your data governance matters. DoCoreAI avoids storing prompt content and provides visibility for finance and engineering leadership.

  • No prompt content leaves your environment; only telemetry is collected.
  • RBAC-ready dashboards and team scoping.
  • Audit-friendly summaries for monthly reviews.
FAQ

Questions Managers Ask

Does DoCoreAI store our prompts or outputs?

No. The client runs locally and only sends telemetry (counts, durations, success signals) for analytics.

How quickly can we see savings?

Teams typically see early gains within the first week as high-variance prompts are tuned.

Which models are supported?

Works with OpenAI and modern LLM APIs via Groq; your team can choose the models that fit cost and quality needs.

Developer vs. SaaS edition—what’s the difference?

Developer edition demonstrates temperature tuning logic locally; SaaS adds dashboards, team-level telemetry, and leadership reporting.

How do you calculate ROI?

Blends direct cost cuts (token spend) with developer time saved and infra overheads.

Ready to See Your Own Numbers?

Run DoCoreAI for a week and compare Before/After results across teams.

Last updated: August 11, 2025 • This is a public demo. Your full dashboard is private to your workspace. Learn how we protect data.

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