Together AI Integration Overview
Together AI has established itself as one of the leading platforms for hosting and fine-tuning open-source models, offering inference endpoints for Llama, Mixtral, Qwen, and dozens of other open-weight models at competitive price points. For enterprises evaluating alternatives to closed commercial APIs, Together AI provides the cost savings and model flexibility that open-source promises - but without built-in governance tooling. Areebi bridges this gap by providing a complete governance layer that covers both inference and fine-tuning workflows on Together AI.
Every inference request sent through Areebi to Together AI is scanned by the DLP engine before it leaves your environment. This is particularly important for open-source model deployments because organisations often choose these models specifically to handle sensitive data that they do not want to send to major commercial providers. Areebi ensures that even when using Together AI's more private hosting, the same rigorous data protection policies apply - PII, PHI, financial records, and proprietary information are caught and handled according to your organisation's rules.
Beyond inference, Areebi governs the fine-tuning lifecycle on Together AI. When teams upload training data and launch fine-tuning jobs, Areebi scans the training datasets for sensitive content, logs the job parameters and approvals, and applies access controls to the resulting custom models. This end-to-end governance means compliance teams have full visibility into what data went into a model, who approved the training, and who can access the fine-tuned result - a critical requirement for organisations operating under SOC 2 or sector-specific regulations.
Governance Capabilities for Together AI
Areebi's governance for Together AI covers two distinct workflows: inference and fine-tuning. On the inference side, the platform applies the same controls available for any LLM provider - real-time DLP scanning with 50+ built-in PII detectors, prompt and response audit logging, per-user rate limiting, and cost allocation tagging. For Together AI specifically, administrators can restrict access to approved models from the Together AI catalogue, preventing users from sending data to models that have not been vetted by the security team.
The fine-tuning governance layer is where Areebi's Together AI integration differs meaningfully from standard LLM integrations. Before a fine-tuning job is submitted, Areebi scans the training dataset using the same DLP engine that protects inference prompts. If the dataset contains PII, PHI, or other regulated data, the job can be blocked, flagged for review, or allowed with documented approval - depending on your organisation's policy. This prevents the accidental embedding of sensitive data into model weights, which would be extremely difficult to remediate after training completes. All fine-tuning jobs, including their parameters, datasets, and approvals, are recorded in the audit trail.
Fine-Tuning Workflow Controls
Areebi's fine-tuning controls include dataset scanning, job approval workflows, and access restrictions on resulting models. Administrators define who can initiate fine-tuning jobs, which base models can be used, and what approval process is required before a job launches. Once a fine-tuned model is created, it inherits the governance policies of its base model by default, and administrators can apply additional restrictions - for example, limiting a model fine-tuned on legal documents to only the legal team's workspace. This chain of custody from training data to deployed model is logged end-to-end.
Compliance Considerations
Organisations often select Together AI because open-source models offer more control over data handling than closed commercial APIs. However, "more control" does not automatically mean "compliant." Regulatory frameworks like HIPAA and GDPR require documented controls over how AI systems process sensitive data, regardless of whether the model is open-source or proprietary. Areebi provides those documented controls - DLP enforcement, access management, and comprehensive audit logging - ensuring that Together AI deployments meet the same compliance bar as any other AI provider in your stack.
For organisations fine-tuning models on Together AI with domain-specific data, the compliance picture extends to the training pipeline. Auditors increasingly ask what data was used to train or fine-tune AI models, who approved the training, and what safeguards prevented regulated data from being embedded in model weights. Areebi's fine-tuning governance provides defensible answers to all of these questions, with immutable logs and documented approval chains. Review our trust centre for detailed security documentation, or request a demo to see how Areebi governs Together AI workflows from inference through fine-tuning.