Use a model API
Best first move for most apps. No GPU setup, scaling, or idle hardware.
- Best for prototypes and variable usage
- You manage prompts and product code
- Tradeoff less infrastructure control
See what runs where, what hardware it needs, and what it can cost — before you deploy anything.
Pick the closest shape. We will separate the app server, the agent runtime, and the model so you do not buy a GPU for the wrong job.
Use an API for the fastest start. Rent serverless GPU for custom weights or bursty jobs. Keep a GPU warm only after the workload proves it needs one.
Open the model hosting guideThere is no universal winner. Traffic shape, control, and engineering time change the answer.
Best first move for most apps. No GPU setup, scaling, or idle hardware.
Bring custom weights and pay for active compute. Cold starts and minimums still matter.
Predictable capacity and maximum control — with a bill that continues while idle.
These are conservative first-test targets for 4-bit weights, short context, and one active sequence — capacity estimates, not performance benchmarks.
Model sizes come from official model cards. Raw weight math and first GPU targets are estimates with headroom. Context, concurrency, runtime, and quantization can require more.
Read the sizing method →Every tool exposes its inputs, claim type, source date, and limitations.
Compare current public list rates across 13 providers.
Open the price board →Change the model, traffic, throughput, and rates. See the crossover.
Run your numbers →Price is only one axis. Compare billing, scale-to-zero, and constraints.
Compare providers →The short visual answer comes first. These longer pages keep the source trail, assumptions, and implementation detail available for search, GEO, and serious buyers.