Trusted by teams where AI spend is mission-critical
Connect in minutes
One export. Full LLM visibility.
Point your LiteLLM proxy at DoiT and spend data flows in automatically. Token usage, provider costs, and per-key attribution arrive as clean, structured records instead of raw logs. No agents, no changes to your application code. You're looking at unified reports within hours of connecting.
What you get
Built for the realities of running an AI gateway
The things FinOps and engineering leaders actually ask us for when they route LLM traffic through LiteLLM.
Unified AI cost reporting
Slice LLM spend by provider, model, key, or team without building custom ingestion pipelines.
Real-time anomalies
Get alerted on token spend spikes in minutes, not days.
Model cost comparison
See what each model actually costs per workload, so you can route requests to cheaper options.
Budget tracking
Track burn against budgets across keys, teams, and orgs.
AI spend next to cloud spend
View LiteLLM costs alongside Google Cloud, AWS, and Kubernetes in the same reports, dashboards, and alerts your team already uses.
Chargeback and showback
Bill teams and business units for exactly the tokens they use.
LiteLLM tracks what you spent. Cloud Intelligence™ helps you do something about it.
Beyond LiteLLM's usage dashboard
Provider and model rollups
Consolidated views across 140+ providers, with drilldown into any key, team, or model.
Real-time anomaly alerts
Machine-learning detection on provider, model, and team dimensions, routed to Slack or email.
Token spend forecasting
Project LLM spend against actual usage trends before a pilot becomes a production-sized bill.
Attribution hygiene
Find unattributed spend, enforce allocation rules, and split shared model costs the way finance expects.
Full-stack cost context
Put inference spend next to the Kubernetes and cloud infrastructure that serves it, in one report.
Forward Deployed Engineers
World-class cloud architects who work as an extension of your team to implement optimizations.
Attribute + LiteLLM
Map LLM spend to the customers and features that drive it
Attribute covers AI inference routed through your LiteLLM gateway, mapping costs to customers and workloads without relying on a tagging program. Best for: SaaS teams routing LLM calls through LiteLLM that want per-customer AI cost attribution in one view.
Gateway request coverage
Attribute costs on every request routed through the LiteLLM proxy.
Provider attribution
Cover OpenAI, Anthropic, Google, Mistral, and 140+ providers.
Self-hosted model coverage
Attribute costs for self-hosted models proxied through LiteLLM.
Agent and tool coverage
Capture spend per key, agent, tool, and MCP server.
Model-level costs
Break down spend across GPT, Claude, Gemini, Llama, and other models.
Customer and workload views
Show which customer and workload is responsible for each cost.
Inference anomalies
Detect unusual token spend across models, keys, and teams.
Fast-growing companies run on Cloud Intelligence™
Avg. savings within first 90 days
Avg implementation time
“DoiT's focus on reliability, mixed with the system's flexibility, helps us safely optimize our Amazon EKS workloads with zero-touch from our engineers.”
Oren Ashkenazy
Director of DevOps and Cloud at Fiverr
Ready to connect your LiteLLM gateway?
Put your token spend in context.
Frequently asked
questions
How do I get better visibility into LLM costs across multiple providers?
Route your LLM traffic through LiteLLM and connect it to Cloud Intelligence™ once. Spend across 140+ providers lands in a single view, so you can slice costs by provider, model, key, or team. No spreadsheets, no manual rollups.
What's the best way to integrate LiteLLM spend data with Cloud Intelligence™?
Export spend data from your LiteLLM proxy to DoiT. The platform handles ingestion and normalization, turning raw token usage into clean, structured cost records. Most teams are live within a day.
How can I see which models, keys, or teams drive most of my spend?
Reports let you drill from top-level AI spend down to a specific model, virtual key, or team. Filter by provider, org, or time period without writing SQL or parsing gateway logs.
How can I monitor LLM cost anomalies in real time?
Anomaly detection runs continuously across provider, model, and team dimensions. When a runaway agent or a leaked key starts burning tokens, you get a Slack or email alert with the likely cause, long before the invoice arrives.
How is Cloud Intelligence™ different from LiteLLM's built-in spend tracking?
LiteLLM's tracking is excellent at the gateway level: per-key spend, budgets, and rate limits. Cloud Intelligence™ takes that data out of its silo, putting it alongside your cloud and Kubernetes spend with anomaly detection, forecasting, chargeback, and forward deployed engineers who help you act on it.
Can I charge AI costs back to teams or customers?
Yes. Because LiteLLM attributes every request to a key, team, or org, Cloud Intelligence™ can roll that up into showback and chargeback reports finance actually trusts, and Attribute can map inference costs down to individual customers.
Is my data secure when I connect LiteLLM?
Cloud Intelligence™ ingests spend and usage metadata only, never your prompts or completions. Access is read-only, and the platform is SOC 2 Type II certified.