Where I'd Look at Pricing First
I wouldn't start with Enterprise or Team plans. For a use case like this, that's usually not the first decision point but rather the next stage, once you have a clear monthly volume, SLA, and negotiating power for discounts.
If the task involves a Claude agent within AWS that receives data from the backend, performs financial data scoring, and occasionally calls other APIs, I'm left with two realistic options: the direct Anthropic API and Claude via AWS Bedrock.
I dug into the current pricing for March 2026, and the picture is quite straightforward: on-demand rates for the direct API and Bedrock for the Sonnet class are roughly the same. We're talking about $3 per 1M input tokens and $15 per 1M output tokens. So, the common misconception that "everything on Bedrock is suddenly more expensive just because it's AWS" doesn't usually apply here.
And this is where it gets interesting. The economics shift not based on the base token price, but on the usage modes.
Why Bedrock Suddenly Looks Smarter for Scoring
In financial scoring, you almost always have a repeating request structure: a system prompt, evaluation rules, response schema, constraints, and JSON format. What changes are the client's data, transactions, and document excerpts. This is a perfect scenario for prompt caching.
In Bedrock, caching isn't just a gimmick. If you have the same large prompt prefix running over and over, reading from the cache is significantly cheaper than reprocessing the full input every time. At high volumes, this is no longer a "nice bonus" but a tangible line item in your savings.
The second advantage of Bedrock that I like for this use case is asynchronous or batch processing. If the scoring doesn't need a response in seconds and you can process requests in batches, AWS offers a discount of up to 50% compared to on-demand. For nightly runs, portfolio recalculations, anti-fraud queues, and bulk scoring, this is an almost obvious choice.
To put it simply: it's best to treat real-time scoring as a premium path, and anything that can tolerate a delay should be pushed to batch processing. This is what a healthy AI architecture usually looks like, one where the LLM bill doesn't start to infuriate the CFO.
When the Direct API is Also a Viable Option
I wouldn't write off the direct Anthropic API. It's a perfectly fine choice if AWS isn't your central platform, if you need more direct access to Anthropic features without waiting for them to appear in Bedrock, or if you've already built your own gateway for external models.
But if you're already living inside the AWS ecosystem, the direct API often brings extra overhead: separate authorization, network configuration, a proxy layer, egress control, call auditing, and additional places where you can accidentally complicate your life. It works, of course. It's just that the AI solution's architecture becomes less tidy.
This is especially noticeable in regulated finance. Bedrock integrates more seamlessly with IAM, VPC, CloudWatch, data guardrails, and the overall security framework. I wouldn't pay extra for this, but in this case, there's often no premium on the token price.
What I Would Do in Practice
If a project like this came to me at Nahornyi AI Lab, I would build the initial production pipeline on Bedrock and immediately split the traffic into two classes.
- Urgent scoring where a fast response is needed: on-demand inference.
- Mass and non-urgent recalculations: batch or async with a 50% discount.
- Repetitive system instructions and templates: via prompt caching.
Plus, I would keep a very close eye on output tokens. In these systems, they often inflate the budget more than the input. If the agent is verbose, loves detailed reasoning, and provides long explanations, the bill skyrockets faster than you'd think.
That's why for financial scoring, I almost always force responses into structured JSON with short label fields, a score, confidence, and length-limited reasons. It's simply cheaper and better for downstream AI-powered automation.
My short conclusion is this: if you're already on AWS and need a Claude agent as an API endpoint for scoring, Bedrock usually looks like the most practical option in terms of cost and operations. Enterprise plans make sense to discuss later, once you have a confirmed volume and a clear load model.
This analysis was prepared by me, Vadym Nahornyi of Nahornyi AI Lab. I personally design and build these kinds of systems: AI implementation, AI integration with backends, routing batch and real-time workloads, and controlling LLM costs in production.
If you'd like, I can help you break down your specific use case by the numbers: what the token economics will look like, where to implement caching, and where AI automation through batch processing is the better choice. Get in touch, and we'll discuss your project at Nahornyi AI Lab.