Cartesia · FinOps Profile

Cartesia Ai Finops

FinOps view of Cartesia spend. Cartesia bills a monthly subscription per tier (Free/Pro/Startup/Scale/Enterprise) that includes a pooled monthly credit allowance consumed by TTS and STT generation, plus a separate prepaid Voice Agents balance metered per minute for calls and telephony and a one-time credit charge for professional voice cloning. Concurrency ceilings rather than per-request rate limits are the operative capacity constraint, so cost and throughput scale together per plan.

Cartesia Ai Finops is the FinOps profile for Cartesia on the APIs.io network, aligned with the FinOps Foundation Framework.

It defines 6 billable meters, billed in USD, on a monthly cycle, and pricing category usage-based.

The profile maps 8 FOCUS columns for cost-allocation reporting.

Tagged areas include AI, Voice AI, Text to Speech, Speech to Text, and FinOps.

Category: AI and Machine Learning Pricing: Usage-Based Billing: Monthly FOCUS v1.3
AIVoice AIText to SpeechSpeech to TextFinOpsCost ManagementFOCUS

Framework Alignment

Framework
Data Spec

Charge Categories

UsagePurchaseSubscriptionAdjustment

FOCUS Columns

BillingCurrency
USD
ChargeCategory
Usage
InvoiceIssuerName
Cartesia
PricingCategory
Usage-Based
ProviderName
Cartesia
PublisherName
Cartesia
ServiceCategory
AI and Machine Learning
ServiceName
Cartesia

Meters

tts_credits
Unit: credits
Credits consumed by Text-to-Speech generation (REST bytes/SSE and WebSocket), drawn from the plan's monthly credit pool.
stt_credits
Unit: credits
Credits consumed by Speech-to-Text transcription (batch REST and WebSocket), drawn from the plan's monthly credit pool.
agent_call_minutes
Unit: minutes
Voice Agent call minutes billed against the prepaid agents balance at $0.06/minute.
telephony_minutes
Unit: minutes
Cartesia-managed telephony number minutes billed at $0.014/minute.
voice_clone_operations
Unit: credits
One-time professional voice cloning operations billed at 225 credits each.
dataset_storage
Unit: bytes
Files and audio stored in Datasets for fine-tuning and voice creation.

Sources

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