Bedrock LLM Token Tracking

@ZohaibRoy

⭐⭐⭐⭐⭐ Track Token Usage and Costs for AWS Bedrock LLMs Efficiently ❇️ Hi My Friend, I hope you are doing well. I've reviewed your project requirements and see you are looking for a solution to track token usage and costs at user, application, and agent levels in your office. You don't need to look any further; Zohaib is here to help you! My team has successfully completed 50+ similar projects for tracking and monitoring usage. I will create a straightforward method to count token usage and push the metrics to Datadog for observability, all within your budget. ➡️ Why Me? I can easily handle your token tracking project as I have 5 years of experience in cloud services, AWS, and data monitoring. My expertise includes API integration, data analysis, and observability solutions. Additionally, I have a strong grip on AWS services and monitoring tools, ensuring a comprehensive approach to your project. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. Looking forward to discussing this with you in chat. ➡️ Skills & Experience: ✅ AWS Bedrock ✅ API Integration ✅ Data Tracking ✅ User Monitoring ✅ Cost Analysis ✅ Datadog Integration ✅ CLI Tools ✅ Token Management ✅ Cloud Services ✅ Application Monitoring ✅ Data Visualization ✅ Agent Configuration Waiting for your response! Best Regards, Zohaib

@corpmember29

Hello, {{{ I HAVE CREATED SIMILAR BEFORE AND I CAN SHOW YOU }}}} I have carefully reviewed your AWS Bedrock token tracking requirements and understand the goal of building a unified usage and cost observability layer across CLI, API, and agent-based access to Claude models. With 10+ years of experience in backend systems, cloud integrations, and observability pipelines, I have worked extensively with AWS services, API instrumentation, usage metering systems, and distributed logging/metrics solutions like Datadog. I can design a centralized tracking layer that captures token usage at request level, normalizes it across different access paths, and attributes it to user, application, and agent identities. The solution will typically involve intercepting Bedrock requests via a shared middleware or proxy layer (or SDK wrapper where applicable), extracting token usage metadata from response payloads, and enriching it with identity context (user/app/agent). This data can then be aggregated into a structured event stream and pushed to Datadog for dashboards, alerting, and quota enforcement. I will ensure the design supports cost attribution, rate limiting, and usage governance, so you can both monitor and restrict access based on defined thresholds. The system will be lightweight, scalable, and compatible with existing AWS infrastructure. I WILL PROVIDE 2 YEARS OF FREE ONGOING SUPPORT AND COMPLETE SOURCE CODE. I eagerly await your positive response. Thanks Christina

@intelliresponse

Hello, Hello! I understand you need a centralized usage-tracking layer for AWS Bedrock Claude access, covering CLI, API, and agent-based usage, with visibility by user, application, and agent. The key requirement is not only counting input/output tokens and estimating cost, but also enforcing limits and exporting metrics to Datadog for monitoring. I have experience building API middleware, cloud cost-tracking workflows, and Python/Node.js observability integrations. I would approach this by creating a unified proxy or wrapper around Bedrock calls, capturing metadata, token counts, model IDs, users, apps, and agents, then calculating cost, storing logs, applying quota rules, and pushing metrics/events to Datadog. I can deliver a clean, scalable solution with dashboards, alerts, and clear documentation for office-wide adoption. Best regards.

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@Herculesz

Hi, you’re not asking for a chatbot feature here—you need a reliable attribution layer around Bedrock usage, and that’s a backend governance problem more than an LLM problem. The real engineering risk is inconsistent identity and event capture across CLI, API, and agent paths, because cost tracking breaks down fast if requests are not normalized before they hit Bedrock. I’ve built production AI systems where the important part is not model access itself, but the control plane around it: usage accounting, workflow tracing, and operational visibility. With AWS Bedrock and Claude, I usually structure this as a thin metering layer plus a normalized event schema for user, app, agent, model, token counts, and computed cost. The closest match here is Custom Feature Development & Integration, where I stepped into an existing codebase, mapped the live architecture, and added targeted functionality cleanly. HubSpot CRM Implementation & Interactive Senior Living Website is also relevant because it required accurate attribution and reporting across multiple interaction paths. I typically design this by separating invocation capture, usage aggregation, and enforcement. That lets you push stable metrics into Datadog while keeping restriction logic independent from reporting. If some calls bypass a central wrapper today, I’d identify those gaps first and close them before promising accurate per-user numbers. Thanks, Hercules

@Aaleen110

I understand you need to track Bedrock LLM token usage and cost at user, application, and agent levels to restrict office usage, pushing metrics to Datadog. I've previously implemented a similar system for a fintech company, successfully attributing API calls to specific internal teams and flagging overages, reducing their cloud spend by 15%. My solution involves a Python Lambda function triggered by CloudTrail logs or API Gateway access logs, parsing event details to identify user/application/agent origins and Anthropic Claude token counts. This data will be aggregated and formatted before being sent via Datadog's HTTP logs intake API. The entire pipeline will be orchestrated using AWS services like Lambda, CloudWatch Events, and IAM for secure access. What is the expected volume of LLM requests per day? Ready to start as soon as you confirm scope.

@mohjib092

Hi! Are you looking for a reliable AWS Bedrock & Datadog expert who can build a centralized LLM gateway to accurately track, attribute, and control token usage and cost across users, apps, CLI, and agents in real time?

@amgadmourad18

Hi , I'm a professional Full stack web developer and AI specialist . am ready to create you full dashboard that will track and monitor the usage of tokens and their costs moreover i can visualize it in charts so it is better to understand . thanks

@sf264

Hello, I understand your requirements. Please message me to discuss the project in more detail. Best regards, Fahad.

@juanpablomacf

Hello, I’m Juan Pablo. I can build a clean, reliable token‑tracking layer for your Bedrock‑based Claude usage so you can see cost and consumption per user, per application and per agent, no matter whether they call the model through CLI, API or internal tools. The key is to intercept every Bedrock invocation at a single choke‑point and log the request/response metadata before the model returns output. From there, usage can be aggregated and pushed to Datadog for dashboards and alerts. My approach is to create a lightweight usage‑metering wrapper that your team calls instead of hitting Bedrock directly. It records the Bedrock invocation, captures token counts from the response metadata, tags the event with user, app and agent identifiers, and writes the metrics to a small PostgreSQL or DynamoDB table. Datadog then scrapes or receives these metrics for observability and enforcement. This gives you a unified view even if people use CLI scripts, internal agents or custom apps. I’ve built similar governance layers for companies using Bedrock and OpenAI where the goal was to track spend, enforce limits and generate per‑user cost reports. If you want insight into LLM usage tracking or how I design AI governance layers I can expand. I’m ready to help you implement a simple, scalable tracking system.

@sharmin283

As a seasoned full-stack developer with over a decade of experience, I have encountered numerous situations demanding the precise tracking and management of usage, along with sophisticated metrics calculation. My diverse tech stack, Laravel, MySQL, and PostgreSQL among others, prepares me to handle such tasks adeptly. Thus, I can analyze your AWS bedrock LLMs' usage via different channels and provide you with a meticulously curated report at the user, application, and agent levels. A significant part of my contingency agility is being AI-enabled. Utilizing modern AI tools for rapid engineering and AI-assisted site building throughout development keeps me ahead of the game. That should come in valuable for this project that requires real-time tracking using Datadog for effective observability. In addition to my technical adroitness, I also bring a strong sense of structure and efficiency to project management. A commitment to timeliness is one of my major value propositions; something that I especially focus on while working with businesses whose systems need extensive monitoring and maintenance like yours. So, if you're looking for an developer with comprehensive skills, broad technological understanding and extreme dedication to collecting accurate metrics on time then look no further - I am your perfect fit.

@riogushiken1114

Hi there, I reviewed your Bedrock LLM Token Tracking project carefully, and I can help you implement user-, application-, and agent-level token/cost tracking for AWS Bedrock Claude usage across CLI, API, and agents. Why I’m a good fit: • Strong Python and AWS Bedrock integration experience, including Claude invocation flows • Experience building observability pipelines with usage metrics, cost attribution, and Datadog reporting • Practical approach to enforcing limits by user, app, or agent without disrupting existing workflows I have experience with AWS SDK/Boto3, Bedrock runtime logging, middleware/wrappers, API instrumentation, CloudWatch, and Datadog metrics, including similar work around LLM usage governance and cost visibility. My approach: • Clean tracking layer for all access paths • Accurate token/cost calculation and tagging • Reliable delivery with clear communication I can start immediately and would be happy to discuss the project in more detail. Best regards,

@kend27

Hi, Two architectural paths for this. One is dramatically cleaner. Option A: LiteLLM Proxy. OpenAI-compatible proxy in front of Bedrock. Each user/app/agent gets a virtual key. Proxy enforces per-key budgets and rate limits, captures tokens per call, ships to Datadog via the built-in callback. ~3 days setup. Downside: CLI/SDK callers point at the proxy instead of Bedrock directly. Option B: CloudTrail + Bedrock Invocation Logging. Enable invocation logging to CloudWatch, parse tokens and IAM identity in a Lambda, push to Datadog. Works with whatever access pattern users have, but attribution needs distinct IAM credentials per user/app/agent. No native enforcement; budgets are alerting-only. For "track and restrict" together, default to A. Looking forward to hearing from you. Best, Ken

@johnedwardl

Hi, I will implement a robust solution to track token usage and associated costs at the user, application, and agent levels for your AWS Bedrock LLMs. Leveraging your existing access to Anthropic Claude, I'll design a system that seamlessly aggregates usage data from CLI, API, and agent interactions. My experience with AWS services and observability tools, including Datadog, ensures I can create an efficient architecture that captures detailed metrics without impacting performance. This will involve setting up a monitoring mechanism that records token usage in real-time and correlates it with user activities, providing clear insights into usage patterns. To ensure effective tracking and enforcement, I recommend discussing your current access controls and any specific thresholds you'd like to implement. Additionally, understanding the data retention requirements for your observability metrics will help tailor the solution. Once implemented, you’ll have the clarity needed to manage token usage effectively in your office. Thank you.

@engruhulajom

Dear Client, I’m a Full-Stack Developer with 10+ years of experience building cloud-native systems, AWS solutions, observability platforms, and usage-tracking frameworks. I specialize in designing scalable monitoring and governance solutions that provide accurate cost attribution, reporting, and access control across enterprise environments. I understand you need to track Anthropic Claude token usage through AWS Bedrock at user, application, and agent levels, regardless of whether requests originate from APIs, CLI tools, or agent workflows. I can implement a centralized tracking layer that captures request metadata, token consumption, and model costs, stores usage records, enforces quotas and limits, and exports metrics to Datadog for dashboards and alerts. The solution will provide clear visibility, accountability, and cost management across your organization. Experienced with AWS Bedrock, Anthropic Claude, Python, APIs, Datadog, CloudWatch, Lambda, IAM, monitoring, and cost analytics. I’m available to discuss architecture and implementation details. Best regards, Md Ruhul Ajom

@Vsion2

Hi, I'm Alema, Python developer with 10 years of experience. My main passion and specialization is working with data extraction. I have worked with many sites and wrote a lot of bots which can extract data in any required format, like Excel, CSV, Json or save data to a DB. You can see completed projects: https://www.freelancer.com/projects/excel/Looker-Studio-Performance-Dashboard/proposals https://www.freelancer.com/projects/french-translator/ation-Agent-Polyvalent/reviews https://www.freelancer.com/projects/ubuntu/Ubuntu-Price-Tracking-Suite-Deployment/proposals https://www.freelancer.com/projects/documentation/Python-Security-Audit-Report/proposals https://www.freelancer.com/projects/beautifulsoup/Data-from-https-www-gsmarena/proposals If you're looking for a reliable Python backend developer to help with your project, feel free to reach out. Your faithfully. Eng. Alema Akter

@ivicab6

Hi, I've developed systems to track token usage and costs for multiple APIs and agents, ensuring clear visibility and control. With experience handling similar setups using AWS Bedrock and integrating with tools like Datadog, I can help you set up a straightforward solution. We can start with a small test task to align our approach before scaling up. Best Regards, Ivica

@SameerK22

Hi! I understand that your team is using AWS Bedrock (Claude) across multiple entry points like CLI, APIs, and agents, which is making it difficult to accurately track token usage, cost, and ownership at a user, application, and agent level. My approach would be to introduce a centralized LLM gateway that all requests pass through, where we can capture user identity, log requests, extract token usage from Bedrock responses, and calculate cost in real time. This gateway would also push structured metrics into Datadog for observability and enforce basic quotas or rate limits to control spending. I specialize in building production-grade AI infrastructure using FastAPI, RAG systems, and LLM integrations, with a focus on turning fragmented AI usage into fully observable and manageable systems. Feel free to message or let's schedule a quick 10-minute introduction call to discuss further. Best Regards, Sameer

@MIndlabsAi6

Hey there, I'm Vishal Maharaj, a Python and AI expert with 25 years of experience based in Perth, Australia. I am highly passionate about taking on your project involving tracking Bedrock LLM token usage and cost at different levels within your office setup. My approach would be to develop a custom solution that accurately tracks token usage and costs at the user, application, and agent levels, integrating with Datadog for enhanced observability. I would love to discuss this project further with you. Please feel free to initiate a chat to explore how we can collaborate effectively. Cheers, Vishal Maharaj