Polza.ai or provod.ai — comparison 2026

Polza.ai VS provod.ai

Categories LLM aggregators and APIs LLM aggregators and APIs

Pricing and payment

Pricing model Pay as you go Pay as you go
Starting price Pay as you go Pay as you go
Free access No No
Pricing plans
  • Polza AI API — Pay as you go
  • Тарификация по использованию — Pay as you go
Regional payment options Pay directly on the website Pay directly on the website
Pricing last changed

Ratings and reviews

Editorial assessment Good specialized service Good product
Functionality Good Good
Price and value Good Good
Ease of use Good Good
Reliability and support Good Average
Innovation Average Average
User rating +4 +5
User reviews 3

Data freshness

Service status Online, checked Online, checked
Last full review of the listing
Checked

Values come from catalog data: collected pricing, editorial assessments, reviews and the verification history. How we assess tools

What they share and how they differ

Both have

AI model aggregatorAI gatewayLLM API

Only Polza.ai

AI model APIsVPN-free AI access

Only provod.ai

Chat with LLMTeam AI workspaceAI service payments
Polza.ai Polza.ai
Good specialized service +4

Pros

  • Unified access to hundreds of models from OpenAI, Anthropic Claude, Google Gemini, DeepSeek, and others
  • Drop-in compatibility with the OpenAI Chat Completions API standard and major SDKs
  • Automated provider monitoring with fallback routing during upstream outages
  • Transparent pay-as-you-go billing with a single account balance

Cons

  • Request latency and time-to-first-token are strictly tethered to upstream provider data centers
  • Cloud-only infrastructure without confirmed support for on-premise isolated deployments
  • Asynchronous video generation jobs remain vulnerable to external queue delays
provod.ai provod.ai
Good product +5

Pros

  • Native support for both OpenAI Chat Completions and Anthropic Messages endpoints without protocol wrappers
  • Seamless drop-in compatibility for coding CLI utilities and IDE extensions
  • Automated masking of personally identifiable information (names, emails, and phone numbers)
  • Wide-ranging model lineup covering text, vision, and video generation through a single ruble balance

Cons

  • No dedicated endpoints for standalone vector embeddings or direct speech-to-text transcription
  • High-load and multi-step agent requests place a temporary hold on the account balance until generation finishes
  • Request retries on failure are restricted to before output streaming begins, preventing mid-generation recovery