Fireworks AI + Areebi

Fireworks AI has built its reputation on inference speed - delivering responses from open-weight models like Llama, Mixtral, and their proprietary FireFunction series at latencies that rival or beat the major cloud providers. For development teams, this speed is compelling. For security and compliance teams, it creates a blind spot: fast inference at scale means more data flowing through model APIs with less time for human review. Areebi integrates with Fireworks AI to close this gap, applying real-time DLP scanning, access controls, and audit logging to every call without negating the speed advantage that drew your team to Fireworks in the first place.

What makes Fireworks AI particularly interesting from a governance perspective is its emphasis on structured outputs and function calling. JSON mode and the FireFunction models allow developers to build applications where the LLM generates structured data - API parameters, database queries, configuration objects - that gets executed by downstream systems. This is powerful, but it means ungoverned Fireworks calls can produce structured outputs that contain sensitive data or trigger unintended actions. Areebi inspects not just the natural language prompt and response, but the structured payloads and function call arguments, ensuring governance extends to the full output surface area.

Areebi supports both Fireworks' serverless and dedicated deployment tiers. Administrators configure the Fireworks API connection once in the Areebi admin console, and governance policies propagate to all models and endpoints accessed through that connection. Users interact with Fireworks models through Areebi's workspace interface with no awareness of the governance layer operating behind the scenes - they get the same fast responses, now with enterprise-grade controls attached.

Standard DLP operates on free-text prompts and responses, but Fireworks AI's structured output capabilities demand a more sophisticated approach. When a Fireworks model returns JSON via JSON mode, Areebi's DLP engine parses the structured response and applies detectors to individual fields - catching a Social Security number embedded in a JSON value that might slip past a naive text scanner. Similarly, when FireFunction models generate function call arguments, Areebi inspects each argument value for PII, PHI, and custom-defined sensitive patterns before the function call is returned to the application. This field-level inspection is essential for applications that use LLM outputs to drive automated workflows.

Audit logging for Fireworks AI captures the complete interaction context: the prompt, model selected, inference latency, token consumption, any structured output or function call generated, and the DLP actions applied. For organisations running high-throughput Fireworks workloads, Areebi batches log writes efficiently to avoid becoming a bottleneck. Logs are exportable to your SIEM and are structured for easy querying - filter by user, workspace, model, or DLP event type. For SOC 2 audits, the log format includes all fields required to demonstrate continuous monitoring of AI interactions.

Fireworks AI processes inference requests on their infrastructure, which means data leaves your environment during API calls. For organisations in regulated industries, this data flow must be governed. Areebi's DLP layer ensures that HIPAA-protected health information, financial account numbers, and other regulated data categories are intercepted and redacted before the request reaches Fireworks' servers. The redaction happens at the Areebi layer, meaning Fireworks never receives the sensitive data in the first place - a critical distinction for compliance frameworks that require demonstrable data minimisation.

For organisations using Fireworks' dedicated deployment tier for enhanced isolation, Areebi's governance layer adds the compliance evidence that dedicated infrastructure alone cannot provide. Data residency controls keep inference on Fireworks' infrastructure, but only Areebi provides the audit trail proving what data was sent, what was redacted, and who initiated each call. Combined with workspace isolation and role-based access controls, organisations can segment Fireworks usage by business unit - each with its own compliance posture. Visit the trust centre for Areebi's security documentation, or request a demo to see structured output governance in action. Pricing scales with your Fireworks usage volume.