LLM Model Pricing, Deprecation & Migration Intelligence

Use this Actor to analyze llm model pricing, deprecation and migration and return decision-ready structured signals. It is designed for repeatable human, API, Apify AI, and MCP-driven workflows.

Track model pricing, deprecations, context and availability changes across AI providers, then rank migration risk, cost impact and replacement priorities.

Why use this Actor

AI teams need one recurring view of model price changes, deprecation deadlines and replacement risk before production workloads break or costs drift upward. This Actor sits above raw extraction: supply a current dataset, optionally add a previous snapshot, and receive an aggregated report built for recurring monitoring and AI-agent workflows.

Key features

• Unifies pricing drift and deprecation evidence instead of treating them as separate feeds.

• Turns model changes into migration-priority and cost-impact signals.

• Supports recurring portfolio snapshots across multiple providers.

• Reads inline JSON rows or Apify Dataset IDs with limited READ permission.

• Writes one auditable report to the default Dataset and INTELLIGENCE_REPORT.

Input

Provide currentItems directly or select an Apify Dataset with currentDatasetId. For change intelligence, add the prior period with previousItems or previousDatasetId. maxItems caps dataset loading. Optional Gen2 fields can provide a previous analysis and user-supplied economic assumptions.

Output

The Actor writes one decision-ready report to the default Dataset and to INTELLIGENCE_REPORT in the key-value store. The report includes counts, ranked signals, baseline evidence, confidence, regression state, an executive decision, recommended action, and the domain-specific portfolio score.

Example

Use the prefilled example or replace currentItems with rows from an upstream Actor. On recurring runs, provide the prior period in previousItems or previousDatasetId. The Actor normalizes common aliases, compares snapshots, ranks the strongest entity changes and emits agentAction.

Use cases

• AI platform engineering.
• LLM FinOps.
• model migration planning.
• agent infrastructure monitoring.

Pricing

One primary pay-per-event outcome: one decision-ready intelligence report. Base price USD 0.014 before Apify tier discounts. The 256 MB data-first architecture is designed for strong unit economics.

Limitations

• Analyzes supplied public or appropriately licensed data and does not bypass restricted sources.
• Scores are decision-support signals, not predictions or guarantees.
• Keep stable identifiers across snapshots for best change detection.
• Competitor evidence is refreshed before publication because the Store changes continuously.

Workflow

upstream dataset -> current snapshot -> optional previous snapshot -> normalization -> entity aggregation -> change scoring -> ranked signals -> agentAction.