BettoAI FinOpsThe evidence layer for AI spend

Find the AI decisions costing you money.

Betto tells your engineering team what to change next, why it matters, and the evidence behind every recommendation.

For engineering teams running AI in production.
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AI architecture is becoming unit economics.

Models change. Pricing changes. Best practices change. Your team keeps shipping. Betto continuously finds the decisions worth revisiting before inefficiency compounds.

Questions Betto Answers
Which model should handle the task?

Recommendations, not dashboards.

Betto continuously surfaces the highest-impact optimization opportunities across your AI stack.

betto.dev/pr-checks/6
Betto PR check for a commit configuring high-capacity defaults for agent workflows: three high-priority cost risks in the changed code.
Agent loop control · Priority high

Unbounded agent loop enabled by the high-capacity-default profile.

Estimated monthly savings$3,337
What to change
Set agentLoop.maxSteps to a finite default, require a non-never stop condition, and add a runtime hard cap that aborts the loop after a configurable timeout.
Why it matters
Unbounded iterations mean unbounded model invocations. A single task can produce an arbitrarily large number of billable calls.
What supports it

examples/orbit-cost-review/runtime-config.json:2

COMMIT 8df8ab8FILES REVIEWED 2 of 2CONFIDENCE Medium

Delivered in your pull request, before merge.

Your tools tell you what happened.
Betto tells you what to do next.

Model policy

This workflow is spending ~$8,400/month on a premium model for a task that appears suitable for a smaller model.

What to change

Run this evaluation set against three lower-cost candidates before changing the default.

Prompt caching

This 11K-token system prompt is repeated across 74% of requests with almost no cache utilization.

What to change

Restructure the prompt around a stable cached prefix to materially reduce input cost.

Retrieval

This RAG workflow retrieves 20 chunks by default, but successful runs consistently rely on fewer than 6.

What to change

Test a lower top-k with a relevance threshold.

Betto uses those signals as evidence and combines them with continuously updated optimization intelligence.

AI best practices expire fast.

New models launch. Pricing changes. Better patterns emerge. Betto continuously tracks models, benchmarks, provider capabilities, and optimization techniques, then applies what matters to your environment. So your optimization strategy evolves as AI does.

Betto works with the stack you already have.

OpenAIGitHubAnthropicGoogle Vertex AIAzure AI FoundryAWS BedrockMistral AILangSmithDatadog

The integrations provide the evidence. Betto turns it into recommendations.

Questions we get asked.

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