There are many reasons why an on-premises Gen-AI adoption is the best path for most enterprises:
Your data is secure and private:
All your sensitive data remains on-premises without being sent to the cloud and potentially being used to train someone else’s AI model. You mitigate security, privacy and compliance risks for your organization.
You own your AI models:
When we deploy AI models on your premises, the model is regularly trained (fine-tuned) on your organization’s data and usage patterns over time. Which essentially means that it becomes aware of your organizations style of writing, workflows and pain points etc.
This is now your IP and a valuable asset. You own this asset and not your cloud service provider.
This is more cost effective at scale:
Getting the best results from Gen-AI requires large context data to be repeated provided to the AI models repeatedly – for creating summaries, samples questions, agentic processes etc. for thousands of docs in your organization. Cloud services charge you per input and output token and this cost can get out of control very fast at scale.
Having your own AI-Models can be cost effective for your organization compared to cloud services.
That really depends on what you are looking for. The infrastructure to host the FinBlade AI app can range anywhere from a couple of thousand dollars to hundreds of thousands and beyond.
This largely depends on the number of users, the latency you are willing to tolerate, the size of the data, and whether you want to use server-grade hardware with high availability or consumer-grade GPUs (yes, LLMs can now run on smaller GPUs).
At FinBlade AI, we provide L&D (learning and development) services to all our customers. Multiple workshops are designed to:
Provide an overview of Gen-AI and the power of this technology.
Offer a use-case approach to developing an understanding of how AI can help your organization.
Engage with department heads to understand how AI can help with their workflows and develop use cases with them.
Determine whether FinBlade AI can be implemented out-of-the-box to serve the organization’s use cases or if customization is required.
On-premises deployment of FinBlade AI is free. Customers arrange their own infrastructure during the 3-month trial period. During this time, we highly encourage customers to purchase our L&D package so we can develop use cases and AI workflows together.
FinBlade offers both Serverless & Dedicated LLMaaS offerings, where the serverless offering is provided by our partner, which uses its shared infrastructure and charges “end-users” or “you” as a cloud service. The LLM (and the underlying GPU infrastructure) is shared across other customers, and input/output tokens consumed per user serve as the metric. The Dedicated LLMaaS is where FinBlade/partner offers dedicated GPUs running the LLM (of your choice) 24/7 for a fixed contract period (12 months min). This model provides consistent SLA delivery as the number of concurrent users is known.
FinBlade AI employs a mix of proprietary and open-source models optimized for different use cases. The core models currently in use are GPT-OSS-120B and LLaMA 70B.
Yes. FinBlade securely ingests and preprocesses structured and unstructured data for model fine-tuning. Using Low-Rank Adaptation (LoRA) techniques, it customizes open-source models such as LLaMA or Qwen on your enterprise data. Training durations vary by dataset size and hardware capacity, typically ranging from several days to a few weeks for terabyte-scale inputs. The result is a context-aware AI model fully aligned with your organizational knowledge base.
FinBlade AI supports input and output in over 100 languages, including Arabic, Urdu, Mandarin, French, Spanish, and Turkish. Its multilingual processing pipelines preserve semantic integrity rather than relying on literal translation.
Yes. FinBlade’s upcoming DeepSeek model enables advanced Optical Character Recognition (OCR) capabilities, allowing it to read and interpret diagrams and visual content.
FinBlade is built for data sovereignty in on-prem, private cloud, or hybrid environments. It employs AES-256 encryption, RBAC, MFA, and continuous auditing in compliance with NIST 800-53, ISO 27001, and major cloud security mandates.
FinBlade AI’s proprietary pipeline integrates:
FinBlade AI offers:
FinBlade provides 24/7 technical support (Tier 1 and Tier 2), full access to FinBlade Academy with training videos and documentation, and SLA-backed uptime and response commitments for enterprise clients, managed by a dedicated account team.
FinBlade’s development team actively benchmarks open-source and proprietary AI ecosystems, integrating community innovations and emerging technologies while maintaining strict compliance and sovereignty standards. This ensures a continuously evolving platform that delivers sustainable, secure, and future-ready enterprise intelligence.
In on-premises or sovereign deployments, all data including vector databases, configurations, and model files remains entirely within the client’s control. For SaaS deployments, data portability and migration pathways are guaranteed under FinBlade’s End-User License Agreement (EULA).
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For technical support and assistance, please contact us at support@finblade.ai. Our support team is available 24/7 to address any technical issues or questions you may have.
© 2025 FinBladeAi. All Right Reserved