AI Cost Discovery Wizard
5 quick steps. Tell us your cloud, your AI stack, and (optionally) paste a recent bill. We'll return a prioritized playbook of cost wins specific to your setup.
1 Cloud
2 AI Services
3 Workload
4 Stack & Pains
5 Bill (optional)
Where are you running AI today?
Picks your optimization branch. Multi-cloud? Select Multi-cloud and we'll cover the intersection.
✓
☁️
AWS
Bedrock, SageMaker, Lambda + external APIs, or EC2 self-host
✓
⛅
Microsoft Azure
Azure OpenAI, Azure ML, AI Foundry, or AKS self-host
✓
🌤️
Google Cloud
Vertex AI, Gemini API direct, or GKE self-host
✓
🌐
Multi-cloud
Workloads split across AWS + Azure + GCP
✓
🏢
On-prem / Other
On-prem GPUs, Oracle, IBM, or direct API only
Which AI services are you using?
Select all that apply. We'll tune the playbook to each.
Which models are you running? (optional, select any that fit)
What's the workload look like?
Helps us size recommendations to volume.
✓
Chatbot / Support
User-facing conversational agent
✓
RAG (Retrieval)
LLM grounded on your docs/data
✓
Agentic / Tool-use
Multi-turn tool-calling workflows
✓
Batch / Async
Offline enrichment, classification, summarization
✓
Voice / Realtime
STT + LLM + TTS pipelines
✓
Code Generation
Copilot-style, PR review, doc-gen
✓
Content Generation
Marketing copy, SEO, creative
✓
Vision / Image
OCR, image understanding, generation
Your stack & where it hurts
Feeds both the playbook selection and the gotcha list.
Bill higher than expected
Unpredictable / spiky spend
Latency too high for users
Unsure which model to use
RAG retrieval quality poor
Not using prompt caching
No observability / blind on cost-per-request
No evals — afraid to change models
Compliance / data residency pressure
Scaling bottleneck as volume grows
Vendor lock-in concern
No specific requirement
SOC 2
HIPAA
GDPR / EU data residency
FedRAMP / GovCloud
PCI DSS
ISO 27001
Paste a recent bill (optional)
We parse service names + dollar amounts and flag where AI vs infra sits. Nothing leaves our server — parsing is local to the request.
Best signal comes from a full monthly statement. Partial data still works — we only use matched lines.
Building your playbook…