[ MANIAC ]
high throughput
task specialized
background agents
Outperform Opus 4.6 on niche repetitive tasks.
1/50 the cost of large frontier models, so your agents can run 24/7.
[ CALCULATOR ]
Same Budget.
50x More Throughput.
See what happens when you swap GPT-5.2 for Maniac-optimized models — same quality, a fraction of the cost.
[ WHAT BECOMES POSSIBLE ]
Stop Sampling.
Process Everything.
Frontier models are too expensive to run on every input. At 1/50 the cost, your agents can cover 100% of your data — not just a slice.
Extract structured data from every PDF, contract, and invoice — not just a sample. At 1/50 the cost, exhaustive processing becomes the default.
Frontier models force you to prioritize which leads get scored. Maniac lets you score all of them, continuously — so nothing slips through.
Stop randomly sampling support tickets for quality. Monitor every conversation, classify every message, flag every issue — in real time.
Run churn models on your entire user base every day instead of monthly batches. Catch at-risk users 30x sooner.
How it Works
Three steps. No AI team required. A few engineering hours to get started.
Point your agents at Maniac
Swap your API endpoint. Maniac exposes an OpenAI-compatible interface—your existing code, SDKs, and frameworks work unchanged.
Follow the setup guide →We optimize automatically
Maniac captures production traffic, builds domain-specific training sets, and runs continuous experiments. Winners are promoted automatically.
Ship frontier quality at 1/50 cost
Optimized models go live through seamless routing. Your agents get frontier-quality responses. Models only get better over time.
Engineering Blog
Deep dives on model optimization, agent throughput, and the economics of running intelligence at scale — plus updates from the Maniac team.
Autonomously Beating GPT-5.2 and Gemini 3 Pro in Prediction Accuracy, with 30x Cheaper Inference for Commerce AI
Our autonomous pipeline took production traffic hooks as input and output frontier-beating Small Language Models — no ML team required. Here's how it works, and why it generalizes to any predictive task.
Limitations of Together and Fireworks finetuning (and why autonomous finetuning can win)
Managed finetuning reduces setup time, but it can bottleneck iteration and portability. Here’s what breaks in practice—and how autonomous finetuning can lower total cost, including inference.
[ GET STARTED ]
Start shipping
in minutes
OpenAI-compatible API. No infrastructure changes. Start free, scale to millions of agent calls.
$ pip install maniac $ maniac init --container my-extraction-agent ✓ Container created: my-extraction-agent ✓ Initial model: openai/gpt-5 ✓ Endpoint: https://api.maniac.ai/v1 $ maniac status ┌─────────────────────────────────────────────┐ │ Container: my-extraction-agent │ │ Status: ● active │ │ Model: maniac-opt-v3 (promoted) │ │ Quality: 99.1% vs opus 4.6 │ │ Cost: $0.20 / 1M tokens │ │ Calls: 2.4M today │ └─────────────────────────────────────────────┘