As generative AI reshapes how finance and procurement teams operate, one thing has become clear: no single approach or foundational model is best for every task.
At Previse, we’re building a platform of intelligent agents that accelerate procure-to-pay and payments workflows, from invoice parsing to payment term validation and supplier matching. These agents rely on large language models (LLMs) to transform noisy, unstructured data into smart decisions. But in a rapidly evolving market, committing to a single GenAI provider can quickly become a strategic bottleneck. What works today may not work tomorrow; In the GenAI landscape, yesterday’s best model can be today’s bottleneck, more costly, slower, and not fit for purpose.
That’s why our philosophy is simple: the right model, for the right task, at the right time.
Directly integrating with individual LLMs is complex, fragmented, and time-consuming. Rather than building bespoke integrations for each provider, we chose to integrate with a platform that provides access to an ecosystem of foundation models, allowing us to dynamically select the best model for each task without the integration overhead. We evaluated three of the most prominent Managed AI/ML platforms that provide access to foundation models and tools for building, deploying, and scaling generative AI applications: Snowflake Cortex AI, Amazon Bedrock, and Google Vertex AI. Our aim was to understand their relative strengths and trade-offs. Below is a summary of what we’ve learned and why it matters for product and data leaders building next-gen finance automation tools.
When evaluating AI infrastructure, it’s easy to focus on the most visible players, ChatGPT, Claude, and other well-known tools built on large language models. These tools are powerful, but they’re also positioned primarily as end-user SaaS products rather than as a natural fit for API access within an enterprise ecosystem.
At Previse, we’re building embedded solutions that operate within financial workflows, where data governance, infrastructure alignment, and orchestration flexibility are just as important as the accuracy of the underlying model. That’s why we’ve chosen to focus this evaluation on Snowflake Cortex AI, Amazon Bedrock, and Google Vertex AI:
While we continue to use OpenAI and Anthropic models via direct API when appropriate, our core platform is built to integrate with infrastructure-native tools that support scaled, governed AI execution.
Finance, procurement, vendor and accounting datasets are rich and varied. Extracting supplier data from invoices is a different problem from detecting duplicate payments. Analysing line items on an invoice to catalogue them against multi-dimensional spend category taxonomies requires different LLM attributes than constructing real-time supplier responses. Even within a single workflow, the tasks may benefit from different model types (e.g., Claude for summarisation, Gemini for classification) and may include “traditional” Machine Learning algorithms.
Our architecture is built to support model diversity, and we continue to invest in expanding its flexibility and performance. That meant evaluating platforms not just for language ability, but for:
Snowflake Cortex AI is purpose-built for teams that already operate within the Snowflake ecosystem. It brings generative AI capabilities directly to where data lives, minimising latency and reducing the complexity of data movement. By offering support for SQL-driven tasks and models optimised for summarisation and data classification, it enables data teams to embed LLM functionality directly into analytical workflows.
A natural fit when your data already lives in Snowflake and you want minimal movement, but less ideal for complex, multi-step orchestration.
Amazon Bedrock offers a serverless, managed approach to accessing leading LLMs via a unified API. It’s built for flexibility, allowing teams to experiment with, deploy, and switch between a variety of models like Claude and LlamaTitan etc with minimal friction. This is particularly valuable in payment environments where different models may outperform others depending on the structure and quality of the data.
Ideal for teams already within AWS, offering a wide selection of models, a unified API, and favourable pricing that aligns with existing investments.
Google Vertex AI stands out for its robust support of proprietary and open-source models combined with mature tooling for training, tuning, and compliance. Its enterprise-grade architecture and customizable deployment workflows make it ideal for high-assurance finance applications where full control over data handling and model execution is non-negotiable.
Great when control, governance, and breadth of models are key, and a strong option for product teams building production-grade decision flows.
If you’re building solutions in payments or procurement, here’s why Managed AI/ML platform evaluation isn’t just a technical decision:
| Platform | Strengths | Challenges | Best fit scenario |
| Snowflake Cortex | Embedded in Snowflake, SQL-first, rich model set | Limited orchestration, early-stage tooling | Teams that are already invested in Snowflake’s data ecosystem |
| Amazon Bedrock | Unified API, serverless, encryption & residency controls | Debugging abstraction, batch inference setup complexity | AWS-native teams seeking broad model access |
| Google Vertex AI | Model diversity, training tools, strong governance & compliance features | Higher setup complexity, batch mode needed for high concurrency | Enterprise teams prioritising control and flexibility |
Our platform is designed to make GenAI modular, observable, and replaceable. We utilise abstraction layers that enable us to run the same job through different providers for benchmarking purposes. We track token usage, cost, latency, and quality outcomes, so we’re not guessing what “best” means. We have invested in infrastructure that enables us to scale what’s working and replace what isn’t, allowing us to develop alongside the GenAI landscape.
As new models launch, we’re ready to evaluate them. As use cases evolve, our AI agents evolve with them.
That’s what it means to build AI-native infrastructure for the finance industry.
Want to go deeper? We’d be happy to share more on how we evaluate and deploy models in real business contexts. Reach out to schedule a session with our team.
