AIOps / LLMOps for AI Products
Executive decisions, economics and FinOps for the production AI loop
Slide contents
1. AIOps / LLMOps for AI Products
Executive decisions, economics and FinOps for the production AI loop
2. Alexander Polomodov
Technical Director & Fellow, T-Technologies
Architecture and engineering practices
AI adoption at development scale
Focus: managed production AI
3. The platform exists. Now manage it
Executives choose behavior, launch path and control
Vocabulary is known — Gateway, context, evals and guardrails.
Focus on decisions — SLA, autonomy and quality cost.
Money anchor — Support bot tests TCO/FinOps.
4. Basic components are assumed
AIOps / LLMOps starts after the platform appears
Skip the platform map
Choose service by scenario
Count solved-task cost
Focus: AI product manageability
5. 01. From basics to governance
Do not repeat the AI platform map; translate it into executive decisions
6. After launch, AI becomes a service
7. The manager chooses the frame
8. AIOps is a decision loop
9. 02. Manager decisions
Scenario, autonomy, error cost, SLA, quality budget and ownership
10. Scaling needs decisions first
Otherwise AI grows faster than control
Value — Value and scenario — Value, baseline and SLA.
Risk — Risk and control — Allowed autonomy and human-in-the-loop.
Cost — Economics and ownership — Quality cost; product, data, risk and bill owners.
11. Error cost defines autonomy
12. AI SLA is broader than API uptime
13. Evaluation starts from product
The same stack can be right or wrong
Value: process and baseline
Risk: error and approval
Economics: unit cost
Operability: release and rollback
Explain the decision, not only the stack
14. 03. Options and trade-offs
API-first, managed platform, rented GPU, owned self-host and hybrid
15. AI runtime is a portfolio
16. Assistant and agent are different products
17. You need routing rules
18. Choice is a portfolio decision
API-first validates the launch
Rented GPU gives transitional control
Owned self-host needs stable load
Hybrid fits different scenarios
Runtime cannot be chosen without scenarios and financial horizon
19. 04. Money case
Support bot: load, tokens, GPUs, team and the management decision
20. Calculate the support bot
200,000 monthly dialogs · 10 messages · 5-second SLA
What we calculate — Load, tokens, peak, GPU, CAPEX/OPEX, team.
What we compare — API-first, rented GPU, own self-host, hybrid.
What we decide — Year-one path and review metrics.
21. Case parameters set the decision scale
22. Translate parameters into RPS and parallelism
23. GPT-5.2 API gives a clear starting OPEX
24. API-first buys speed
The risk is silent unit-economics growth
Why launch is convenient
Near-zero CAPEX
Faster production launch
Thinner surrounding platform
What must be controlled
Output, cache, retries
Provider limits and degradation
25. Size self-host from SLA and peaks
26. GPU rental trades CAPEX for OPEX
27. Self-host changes the problem
28. Strategy comparison shows the year-one decision
29. The decision must include a review point
30. The case decision
Launch API-first unless owned perimeter is mandatory.
Track unit economics immediately.
Set review triggers.
Self-host must pass calculation, SLA and operations readiness.
The first decision is speed with control, not eternal architecture
31. 05. Manageability and FinOps
Dashboard, cost allocation, unit economics review and a 120-day plan
32. FinOps starts with bill ownership
33. Owned hardware makes the bill platform-wide
34. After CAPEX, operations begin
35. Shared platforms need clear allocation drivers
36. The dashboard connects quality, risk and money
37. 120 days: scenario to scale
38. No measurement, no management
Without evals, quality looks higher.
Without fallback, risk grows.
Without unit economics, economics break.
Without ownership, disputes begin.
Managed AI starts with manager decisions
39. References
AIOps, risk and economics
OpenAI pricing and docs.
OWASP, NIST, Langfuse.
Habr TCO, FinOps Foundation.
40. Feedback form for day two
Feedback form for day two