Security operations generate unstructured, high-volume data. SIEM alerts, firewall logs, vulnerability scans, and threat intelligence reports often exceed hundreds of thousands of tokens. Feeding that context into an LLM is necessary for accurate triage, yet token-based pricing creates a direct conflict between depth of analysis and cost. Oxlo.ai removes that conflict with flat per-request pricing, so security teams can send full log dumps and long threat intel documents without watching the meter run. The platform offers 45+ models, from the 1M context window of DeepSeek V4 Flash to the deep reasoning of DeepSeek R1 671B MoE, all through a fully OpenAI-compatible API with no cold starts on popular models.
The Security Context Challenge: Volume and Noise
Typical security data is noisy. A single incident can produce megabytes of JSON logs, PCAP transcripts, and email threads. Token-based inference penalizes you for every byte of that context, which forces teams to preprocess, summarize, or truncate inputs before they reach the model. That preprocessing step is where indicators get lost. Oxlo.ai eliminates the cost penalty for long inputs, letting you pass raw logs directly to models with extended context windows such as DeepSeek V4 Flash and Kimi K2.6.
Enforce Structured Outputs with JSON Mode
Security automation requires deterministic output. You cannot afford a hallucinated severity level or a malformed IP address in a downstream SOAR playbook. Oxlo.ai supports JSON mode across its LLMs, so you can constrain the model to return machine-readable objects
