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Adola

OpenAI-compatible API for GPT-OSS, Qwen, Llama and DeepSeek with per-request cost receipts

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paidUsage-based only — no subscription, seat fee or monthly minimum, but billing must be enabled before requests are served. Rates start at $0.11 per 1M input tokens and $0.26 per 1M output tokens. Published per-model rates include GPT-OSS 120B at $0.14 input / $0.57 output per 1M tokens (131K context) and Qwen3 30B at $0.11 input / $0.47 output per 1M tokens, each with a $0.001 successful-request minimum; Llama 3.3 70B and DeepSeek V4 Flash are also offered. Streaming, explicit model selection, project keys, usage controls and per-request cost receipts carry no platform fee.View full pricing →

About Adola

Adola is an OpenAI-compatible gateway for open-weight models, aimed at teams who want to run GPT-OSS, Qwen, Llama or DeepSeek without swapping out the client library they already ship. You keep your existing OpenAI client, change the model ID per request, and get streaming responses plus a per-request cost receipt back. The pitch that distinguishes it from a general router is explicitness: Adola runs the exact model you asked for or returns a clear error, never silently substituting a cheaper or differently-quantised variant when capacity is tight — which is the failure mode that makes multi-provider routers hard to benchmark against. Pricing is straightforwardly usage-based with no subscription, seat fee or monthly minimum; each of the four supported models publishes its own input and output rate and a per-successful-request minimum, so you know the cost of a call before your application makes it. The dashboard tracks tokens, latency and spend in one place, and project keys with usage controls let you cap a workload rather than discover the overrun on the invoice. The catalogue is small by design — four models with four clear prices rather than a hundred with variable availability — which is a real limitation if you need frontier closed models, and a real advantage if your problem is that a router's routing is the thing you cannot debug.

Key Features

OpenAI-compatible chat completions — keep your existing client
Explicit model selection with no silent substitution
Streaming responses across all supported models
Per-request cost receipts in the dashboard
Project keys and usage controls for capping workloads
No subscription, seat fee or monthly minimum

Adola Pros & Cons

Pros

  • +No-silent-substitution guarantee makes benchmarks reproducible
  • +Per-request receipts remove the usual inference-cost guesswork
  • +Zero platform fee on top of published model rates

⚠️ Cons

  • Only four models — no frontier closed models at all
  • Billing must be attached before any request is served, so no free evaluation tier
  • Small catalogue means you may still need a second provider

Tags

llm-apiopen-weightsopenai-compatibleinferencestreaminggateway
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