Model naming
Ollama uses a name:tag format (e.g., llama3.1:8b, deepseek-r1:14b, qwen2.5:32b). Different tags for the same base model (e.g., llama3.1:8b vs llama3.1:8b-instruct-q4_K_M) appear as separate models in Coolhand. Standardize tags across your team to keep analytics meaningful.
Structured outputs
Supported via the format parameter. Pass a JSON schema to enforce structured output.
Docs: https://ollama.com/blog/structured-outputs
Caching
Not supported. Cached Input Tokens and Cache Creation Tokens will always be blank.
Batch processing
No batch API. A single Ollama instance handles concurrent requests in parallel via the OLLAMA_NUM_PARALLEL environment variable (default: 1). Raise it to process multiple requests simultaneously. Requests beyond the parallel limit are queued server-side up to OLLAMA_MAX_QUEUE (default: 512); Ollama returns HTTP 503 only when that queue is full. For higher throughput, increase OLLAMA_NUM_PARALLEL or run multiple instances behind a load balancer.
Latency
Reflects your local hardware and network, not a cloud provider's infrastructure. Expect more variability than hosted APIs, especially on consumer GPUs or CPU inference.
Reasoning tokens
Not supported. Reasoning Tokens will always be blank for Ollama logs, even for models that perform internal reasoning (e.g., DeepSeek-R1). Thinking content may appear in the Thinking Response field, but the token count is not broken out separately.
Cost tracking
Ollama has no per-token pricing — costs are your own compute. Coolhand captures token counts, but there is no dollar-cost mapping for Ollama logs.