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LLM routing and cost control, every inference measured.

Kroolo AI sends each request to the model that fits the job, keeps usage inside limits, and shows what every team and workflow spends. Frontier quality where it matters, speed and savings everywhere else.

Inference consoleLive
48.2k
Requests today
1.4s
Median latency
62%
Of monthly budget
RequestRouted toTimeCost
Extract fields from invoiceFastClaude Haiku0.6s$0.002
Draft customer replyBalancedClaude Sonnet1.8s$0.011
Plan a multi-team programFrontierClaude Opus4.2s$0.094
Classify support ticketFastGemini Flash0.4s$0.001
Smart routing

The right model, every time.

Kroolo reads each request, judges how demanding it is, and picks a model tier. Teams just ask. Routing happens in the background.

Match effort to task
Simple jobs never pay for a frontier model.
Use the best of every vendor
Claude, GPT and Gemini sit behind one layer, so you are never tied to one provider.
Respect policy first
Routing only considers models your governance policies allow for that data.
Fall back gracefully
Slow or unavailable models trigger automatic failover.
RoutingAutomatic
Summarise a threadFast tier
Short, routine task
Claude HaikuGPT LunaGemini Flash
Draft a proposalBalanced tier
Quality writing, moderate context
Claude SonnetGPT Sol
Analyse a 200-page contractFrontier tier
Deep reasoning, long context
Claude OpusGPT TerraGemini Pro
If a model is slow or unavailable, the request moves to the next one in line.
Inference management

Control the whole pipeline.

Everything between a request and a response is configurable and observable, so AI runs the way your operations team expects.

Model catalog
Choose which models are available, per workspace and per type of data.
Pipeline
$1.42M
+18%
Win rate
31%
+4 pts
Fallback chains
Define the order of alternatives so work continues through outages and slowdowns.
NameValue
Quote #1042$8,400Sent
Quote #1043$21,900Approved
Quote #1044$5,150Draft
Rate limits and quotas
Protect shared capacity with limits per team, workflow or agent.
Timesheet, this week62h
Website relaunch32h
Client onboarding21h
Internal ops9h
Latency targets
Set response-time goals and route to faster models when they are at risk.
GR1Auth context enforcedCritical
GR2Source citation requiredHigh
GR3PII masked in outputMedium
Caching
Reuse results for repeated requests to cut cost and wait time.
Open tasks by ownerThis year
Ann
Clive
Kate
Ravi
Health monitoring
Watch error rates, latency and model availability in one view.
NameValue
Meridian Health$212,750Won
Northgate Corp$25,006Lost
Crestline Foods$76,963Won
Cost allocationThis month
By teamBy workflowBy agentBy modelBy project
$11,220AI spend this month
Engineering$4,120
Support$2,860
Sales$2,170
Marketing$1,430
People Ops$640
Export for showback and chargeback
Cost allocation

Know who spent what, and why.

Every request carries its team, workflow, agent and model, so AI spend is never one opaque line on an invoice.

Attribute to owners
Break usage down by team, project, workflow, agent and model.
Spot what drives cost
See which workflows and models use the most, and where routing saves.
Close the loop with finance
Export usage for showback or chargeback in the format finance uses.
Budgets that act

Limits that respond, not just report.

A budget is more useful when it does something. Set thresholds per team and decide what happens as spend approaches them.

Allocate
Give each team, workspace or agent its own allowance.
Alert
Notify owners at the thresholds you choose, before a surprise.
Act
Downshift to faster models, slow non-critical work, or ask for approval.
BudgetsAlert sent
Support team · monthly budget
$3,000 allowance
Used so far86%
At 70%Notify the team lead
At 85%Shift routine work to fast models
At 100%Pause non-critical agents, ask finance
Observability

See every request, and why it went there.

Each inference is logged with its model, size, speed, cost and the reason it was routed that way. Debug, tune and explain with real data.

Route reasons
Understand why a request used a given model or fell back.
Connect your dataDone
Build the viewDone
Share with the teamNext
Speed and size
Track tokens and response time per workflow.
Pipeline
$1.42M
+18%
Win rate
31%
+4 pts
Governance linked
Every record ties back to the policy and audit trail.
Access, enforced at retrieval
Finance teamRead
Sales leadsRead, edit
ContractorsNo access
Request logLast hour
TimeWorkflowModelTokensTimeCostWhy
10:12Weekly status draftClaude Sonnet1.9k1.7s$0.012Balanced fit
10:13Ticket classificationGemini Flash0.4k0.4s$0.001Simple task
10:15Risk analysis, 14 programsClaude Opus38k5.1s$0.142Long context
10:16Invoice extractionClaude Haiku1.1k0.6s$0.002Simple task
10:18Contract reviewGemini Pro61k6.3s$0.168Fallback from Opus

FAQs on AI Inference

What is AI inference?
Inference is the step where a model produces a response to a request. Every answer, summary and agent action in Kroolo AI is one or more inference calls.
How does smart routing work?
Kroolo reads the request, judges how demanding it is, and picks a model tier within what your policies allow. If the chosen model is slow or unavailable, the request moves to an alternative.
Can we see what AI costs by team?
Yes. Usage is attributed to team, workflow, agent and model, and can be exported for showback or chargeback.
Can we set limits on AI spend?
Yes. Budgets can be set per team, with alerts and automatic actions, such as moving routine work to faster models, as spend nears the limit.
Which models are supported?
Kroolo uses leading models from Anthropic, OpenAI and Google behind one layer. See the Model Hub for the current tiers.

See smart routing in action.

Bring a real workflow. We will show which model handles each step and what it costs.

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