The amortized hardware cost comparison is the number that finally convinced our team to take local inference seriously for high-volume workloads. But the metric that shifted our thinking more than raw $/month was cost-per-decision vs. cost-per-token.
A lot of inference cost analyses compare token prices in isolation, but in practice many agent tasks require multiple small calls — classification, routing, format validation — where the per-token cost adds up but the reasoning complexity is low. Local models handle these beautifully at near-zero marginal cost, which frees up API budget for the heavy lifting that actually needs frontier capability.
The pattern we run in production: local Ollama for the high-frequency, lower-stakes tasks (routing, extraction, formatting) and API calls for complex reasoning and multi-step planning. Rough split ends up 80/20 local/API by call volume, and roughly inverts on spend. One thing I'd love to see benchmarked: latency under concurrent load. Single-request Ollama benchmarks look great, but contention on consumer hardware with 20+ parallel agent calls is a different story.
For further actions, you may consider blocking this person and/or reporting abuse
We're a place where coders share, stay up-to-date and grow their careers.
The amortized hardware cost comparison is the number that finally convinced our team to take local inference seriously for high-volume workloads. But the metric that shifted our thinking more than raw $/month was cost-per-decision vs. cost-per-token.
A lot of inference cost analyses compare token prices in isolation, but in practice many agent tasks require multiple small calls — classification, routing, format validation — where the per-token cost adds up but the reasoning complexity is low. Local models handle these beautifully at near-zero marginal cost, which frees up API budget for the heavy lifting that actually needs frontier capability.
The pattern we run in production: local Ollama for the high-frequency, lower-stakes tasks (routing, extraction, formatting) and API calls for complex reasoning and multi-step planning. Rough split ends up 80/20 local/API by call volume, and roughly inverts on spend. One thing I'd love to see benchmarked: latency under concurrent load. Single-request Ollama benchmarks look great, but contention on consumer hardware with 20+ parallel agent calls is a different story.