Why Inference Provider Choice Matters for Finance
The choice of LLM inference provider is a strategic decision for financial institutions. Unlike model selection — which determines what the AI can do — inference provider selection determines how it operates within your compliance, cost, and performance requirements. A model that performs well on benchmarks may be unusable if its inference provider cannot meet your data residency, latency, or security requirements.
Data Residency and Compliance
Financial customer data processed by AI models must remain within approved jurisdictions. AWS Bedrock operates in 30+ AWS regions worldwide including US East, EU Frankfurt, and Asia Pacific, making it the most geographically distributed option. Azure OpenAI offers EU data residency with dedicated regions in France, Netherlands, and Switzerland. Google Vertex AI provides strong APAC coverage with regions in Tokyo, Singapore, and Seoul. For institutions needing complete data sovereignty, Go Abacus offers on-premise inference appliances that never send data externally.
Cost at Scale
Inference costs vary dramatically between providers. AWS Bedrock charges $3 per million input tokens for Claude Sonnet while Together AI offers Llama 3.3 70B at $0.18 per million input tokens — a 16x difference. For financial institutions processing millions of tokens daily in document analysis pipelines, this cost differential can translate to hundreds of thousands of dollars annually. The cheapest option is not always the best — regulated institutions must factor compliance cost into the total cost equation.
Latency for Trading
For latency-sensitive trading applications, inference speed is critical. Groq delivers 500-1500 tokens per second through its LPU hardware, approximately 10x faster than GPU-based inference from AWS or Azure. Cerebras has demonstrated comparable speeds following its 2026 Nasdaq IPO. For algorithmic trading strategies that depend on real-time market analysis, sub-100ms inference latency can be a competitive advantage.
Multi-Model Strategy
Leading financial institutions are adopting multi-model strategies — using different inference providers for different use cases. A typical pattern uses Azure OpenAI for regulated customer-facing applications, Together AI for internal research and analysis, and Groq for real-time trading signals. This approach optimizes for cost, compliance, and performance simultaneously.
Three Types of Inference Provider
LLM inference providers fall into four categories, each with different tradeoffs for financial services:
- Managed cloud inference: AWS Bedrock, Azure OpenAI, Google Vertex AI — fully managed, SOC 2 certified, VPC deployment available. Best for regulated institutions that need compliance certifications and existing cloud integrations.
- Independent API providers: Groq, Together AI, Fireworks AI, Cerebras — specialized hardware for fast inference, lower costs, but fewer compliance certifications. Best for non-regulated analysis and internal tools.
- API aggregators: OpenRouter — provides access to multiple models through a single API. Best for development and testing but may lack the compliance guarantees needed for production regulated workloads.
- On-premise appliances: Go Abacus — dedicated hardware deployed within your data center. Best for institutions with strict data sovereignty requirements that cannot use any external API.
For Regulated Financial Institutions
Regulated financial institutions should prioritize providers that offer SOC 2 Type II certification, VPC or private link deployment, comprehensive audit logging, and contractual data residency guarantees. AWS Bedrock and Azure OpenAI currently meet all these requirements across US and EU regions. Google Vertex AI is a strong option for APAC-based institutions. For the strictest requirements, Go Abacus on-premise appliances provide complete data sovereignty with SR 11-7 model risk management compliance.
Decision Framework
- US-regulated bank: AWS Bedrock — best VPC deployment, SOC 2 Type II, broadest US region coverage.
- EU-regulated institution: Azure OpenAI or Mistral AI — EU data residency, GDPR-compliant, strong Microsoft 365 integration.
- Low-latency trading: Groq or Cerebras — LPU hardware delivers 500-1500 tok/s, 10x faster than GPU alternatives.
- Cost-sensitive analysis: Together AI or Fireworks AI — open-source inference at 10-15x lower cost than frontier models.
- Maximum data sovereignty: Go Abacus — on-premise appliance, no external data transfer, SR 11-7 compliant.
Getting Started
Explore our inference platform comparisons to evaluate providers side by side on pricing, supported models, regions, and compliance certifications.