CommPulse

Sat through our annual IR tabletop yesterday. Consultant reads a scenario, we all sit around a table, discuss calmly, agree on a plan, write it down, done. Two hours, nobody's blood pressure moved. I've been on the other side of an actual ransomware incident, and nobody was calm. Legal wasn't reachable at 2am, the exec wanted answers before we had them, and half the "plan" from the last tabletop was irrelevant because reality didn't match the script. If your tabletop feels like a calm meeting and not remotely like the real thing, is it actually testing anything? Or is it just a compliance box that happens to have a meeting attached to it? submitted by /u/VegetableFault5149 to r/sysadmin [link] [comments]

When AI costs rise and you can't tie that cost to a specific result, project, or team, the safe move becomes turning capabilities off. It makes sense, but it also defeats the point of adopting AI. We just soft-launched Agent Observe in Capital One Slingshot to attribute Snowflake GenAI spend by agent, model, user, etc. using read-only metadata. Early pilot, write-up here . submitted by /u/noasync to r/FinOps [link] [comments]

FOCUS 1.3 solved a real problem: every cloud provider used to bill in its own format, so cross cloud cost comparison meant building custom normalization pipelines just to ask basic questions. Now there’s a common schema. That part is genuinely good. But adoption of the schema is not the same as fixing FinOps. I keep seeing teams roll out FOCUS, get their data normalized, and still can’t answer the question that actually matters to leadership: who owns this cost, and why did it move. The reason is that FOCUS standardizes the shape of usage and cost data. It says nothing about your tagging discipline, your allocation model, or who is accountable when an engineering team spins up something that triples a bill overnight. Those three things are where the actual FinOps work lives, and they’re organizational problems, not schema problems. You can have perfectly FOCUS compliant data and still have zero cost accountability, because accountability comes from tagging governance and process, not from the spec. The teams that get real value from FOCUS are the ones who treat it as the foundation for building an allocation and accountability model, not as the finish line. If your rollout stopped at “we ingest FOCUS data now,” you’ve done the easy 20 percent. I went deep enough on this that I ended up writing a book on it, “Cloud Money,” working through the FOCUS spec at a practitioner level alongside cost allocation strategy and accountability models. Not trying to sell it here, just flagging it in case anyone wants the longer version of this argument. Happy to talk through the tagging governance side in the comments if people have specific setups they’re stuck on. submitted by /u/aliihashmiii to r/FinOps [link] [comments]

I am working on Azure Data Explorer to optimise its cost usage for my clients, currently I am suggesting downsizing the engine instance SKU type based on the following metrics : CPU utilization < 45% Cache utilization factor < 55% IngestionUtilization and StreamingIngestUtilization < 45% If these holds downsize to the very next lower sku type. But the blocker i am having is that , there's this limit of 50% ram/node for a query , so this will break and cause my queries to fail , is this for real or only latency will increase. Can anyone help me out with that. submitted by /u/sirius_black19 to r/AZURE [link] [comments]

I've been building Cognocient for a few months now, a proxy that sits between your app and OpenAI/Anthropic/Gemini and tells you what each feature actually costs, before the call goes out instead of after you get the bill. For a while the only way in was a 10 day trial with full access, then you had to pick a paid plan. That made sense for teams evaluating it for real budgets. It made no sense for someone who just wants to drop it into a side project and see what their AI calls actually cost. So I added a free tier that doesn't expire. What's in it: one provider connection, the real time proxy and attribution dashboard by feature and model, one budget with alert level enforcement, 7 day retention. Capped at $50/mo of tracked spend, after that the proxy keeps forwarding your calls (I will not break your app over a free tier limit) but stops logging new attribution until the next cycle. Setup is one line, you swap your base\_url for the Cognocient proxy URL and nothing else in your code changes. If you're already tracking spend some other way I'd genuinely like to know what you're using, half the reason I built this is I couldn't find anything that did pre-call enforcement instead of after the fact dashboards. [cognocient.com]( http://cognocient.com ) if you want to poke at it. submitted by /u/MaverikSh to r/FinOps [link] [comments]

Does anyone have any direct experience of the typical costs coming in for the new harness ? Microsoft are making a number of general statements about "long running, multi-step jobs" but still no clear breakdowns of what a particular businss workflow (e.g. receive email from customer, look up record in salesforce, determine sentiment, notify account manager if needed, arrange customer manager meeting, send confirmation". might cost. Because testing is now no longer free, its hard to run through these models and look at typical charge cards without some good examples. Under the old model we had clear credit values for doing certain actions. submitted by /u/imnotonetogossipbut1 to r/FinOps [link] [comments]

submitted by /u/WancloudsInc to r/FinOps [link] [comments]

Hey hi everyone, I am just trying to understand how engineers/SREs who dealt with real production latency incidents investigate it Lets say you have the following - Logs - Recent deployment information - Application health - Database metrics - External dependency health/metrics - Infrastructure metrics You just encountered the incident, you dont know the root cause. You are uncertain about the truth. From here how do real engineers go about reasoning to find the root cause - Do you follow a standard sequence of investigative steps - How do you determine what investigative step to take next under uncertainty to narrow down the possibilities for the root cause - Have u ever encountered with incident where initial information was misleading, how did you navigate from there - Is there any situation where you have lot of information but struggled to form a proper hypothesis - Have you tried any AI investigative tools that help you in achieving this I just wanted to understand how do real engineers reason through the uncertainty to find the root cause. What are the biggest pain points submitted by /u/HawtBeagle to r/devops [link] [comments]

An 80 percent price cut makes a nice chart. It does not make an 80 percent cheaper task. LLM cost optimization starts with the task ledger, not the price sheet. The useful unit is an accepted task: work the team is willing to ship. Each row needs model tier, uncached input, cache traffic, output, retries, review time, and a final accepted or rejected flag. I keep the accounting wrapper fixed by routing Sol, Terra, and Luna through ZenMux's multi-model API gateway and changing only the model slug at one endpoint. That still leaves provider behavior, workload, acceptance rate, and retries. A cheap model can get expensive when review or reruns creep in. Until the ledger has those rows, the July 30 headline is just a new line in the price sheet. submitted by /u/Dramatic_Spirit_8436 to r/FinOps [link] [comments]

I assume everyone has been following the AI build out. Memory prices have risen sharply. GPUs, CPUs, memory shortage is hitting the consumer market. Apple announced price increases in hardware, which is quite rare for Apple. We run on Hetzner and we reserved several Hetzner instances a few months back. The renewal prices have risen since we reserved them. We also run on AWS, but in much smaller numbers. We haven't seen any major changes in our AWS bills thus far. But for folks who are operating much larger accounts, I'm trying to figure out is when these will hit AWS/GCP/Azure list prices. I personally think its a matter of "when", as opposed to "if". If you renewed a savings plan, RI or CUD recently, was the effective rate worse than the term it replaced. Has an account team given anyone a heads-up? I work at Readyset which is a caching solution for databases, so we have an obvious interest where instance costs go. I'm trying to work out whether this is a real 2026 budget line or mostly bare metal hosts that have less pricing cushion than the hyperscalers. submitted by /u/Master-Bass-1905 to r/FinOps [link] [comments]

looking at a pretty big gpu bill right now. the normal cloud cost tools can obviously tell me what the instances cost. but im trying to get more granular. cost by workload. team. job. maybe even gpu utilization vs what were actually paying for. are there any finops platforms that do this well? what are you guys using? submitted by /u/eastwill54 to r/FinOps [link] [comments]

I am hoping to get an outsiders perspective, and thank anyone in advance for taking the time to respond. I feel like there could be other young professionals in my situation that this will hopefully help. Background Non-technical, non-accounting degree with further analytics qualifications UK based with ~5 years of experience both as a consultant and in-house and have actioned millions in savings initiatives working with engineers etc. I am the first FinOps hire that my current company has ever made. Exposed to multiple clouds (private and public), industries and technologies. A range of vendor certifications (AWS, Azure etc.) I genuinely love the work I do, it is the perfect intersection between technology and finance that scratches a very specific itch. But given my background I feel like I can see an upper limit in terms of career trajectory and am wondering which path I should take and what are the steps required to avoid getting stuck as an analyst. Problem I can talk the talk with engineers but I do not currently possess the technical skills to go down an engineering/architecture path, although "cloud architecture" is probably my favourite part of the job even if I feel like I am just successfully guessing most of the time. The imposter syndrome that comes with that is less than ideal. My lack of accounting/finance background makes me think that I would struggle to be taken seriously in a more senior role. I would be curious to get your guys' take on this: Am I just overthinking and experience will make up for my slightly non-conventional background? Do you have any recommendations for financial or technical qualifications (outside of the usual vendor certs)? This is a multi-year strategy. Given my lack of engineering background, am I better off leaning into finance/accounting? Cheers! submitted by /u/EconThrowA to r/FinOps [link] [comments]

☁️ AWS Daily Digest · July 29, 2026 Auto-generated · Groq (Llama 3.3 70B) · Free & Open-Source 7 highlights · ~2 min read · Quick AI briefing per item 1. Amazon EKS Provisioned Control Plane now delivers faster pod autoscaling Compute  ·  AWS What's New Amazon EKS Provisioned Control Plane now delivers faster pod autoscaling by increasing Horizontal Pod Autoscaler sync concurrency. This benefits customers with large-scale workloads that require rapid scaling in response to changing demand. The update reduces the time it takes for workloads to scale, enabling faster responsiveness. → Read full article 2. AWS Console Home now supports the Cost and Usage widget in the AWS European Sovereign Cloud (Germany) Region FinOps  ·  AWS What's New AWS Console Home now supports the Cost and Usage widget in the AWS European Sovereign Cloud (Germany) Region, allowing customers to track costs and identify savings opportunities. This benefits customers in the region who want to optimize their spend and improve financial management. → Read full article 3. AWS DataSync Enhanced mode now supports Amazon EFS and Amazon FSx for Lustre Storage  ·  AWS What's New AWS DataSync Enhanced mode now supports Amazon EFS and Amazon FSx for Lustre as source or destination locations, simplifying large-scale migrations and high-performance computing workflows. This benefits customers who need to transfer large amounts of data to or from these locations. The capability is available in all AWS Regions where AWS DataSync is supported. → Read full article 4. AWS DataSync Enhanced mode adds HDFS, Azure Blob, and object storage locations with Hyper-V agent support Storage  ·  AWS What's New AWS DataSync Enhanced mode adds support for HDFS, Azure Blob, and object storage locations with Hyper-V agent support, enabling secure and high-speed data transfers. This benefits customers who need to migrate data from these locations to AWS. Enhanced mode provides parallelism, unlimited file counts, and detailed metrics for these transfers. → Read full article 5. Introducing self-managed Amazon S3 buckets for AWS Lambda function code Compute  ·  AWS Compute Blog AWS Lambda now supports self-managed Amazon S3 buckets for function code, eliminating the 75 GB code storage limit and giving customers full security control. This benefits customers who manage large-scale Lambda functions and need more storage and security flexibility. → Read full article 6. Introducing modularized kernel cryptography in Amazon Linux Security  ·  AWS Compute Blog Amazon Linux introduces modularized kernel cryptography, separating FIPS 140-3 cryptographic components into an independent kernel module for easier certification and reuse. This benefits customers who require FIPS compliance and need to simplify their compliance workflows. The modular approach reduces the need for repeated kernel re-certification. → Read full article 7. Eliminating Java cold starts with AWS Lambda Managed Instances Compute  ·  AWS Compute Blog AWS Lambda Managed Instances eliminate Java cold starts, providing consistent and predictable performance for latency-sensitive applications. This benefits customers with production services that have strict p99 SLA requirements and cannot tolerate cold start penalties. → Read full article #aws #cloud #compute #storage

UBS put out numbers showing the big three cloud providers are spending about 102% of their cloud revenue on capex, $4.1 trillion is projected through 2028. that spend lands somewhere, and what i'm watching is whether it hits non-AI workloads. OVHcloud already raised prices citing the memory shortage, some servers up as much as 87%, because AI is eating the same memory and power regular nodes run on. the thing is it won't show up as a line item. it's compute and memory quietly drifting up with no change in usage, and if your cost tooling only watches your own consumption, it won't flag a provider-side price move. so you can't really tell whether your usage went up or their prices did. anyone renewed an RI or savings plan lately and had the rate come back worse than the term it replaced? submitted by /u/CryOwn50 to r/FinOps [link] [comments]

Declared self-promotion per the sidebar: my project, launched this week. https://llmcostkit.com Free, client-side calculator answering the two questions FinOps keeps getting asked about AI spend. First, what do LLM API tokens cost against a real workload: 24 current models across OpenAI, Anthropic, Google, xAI, DeepSeek, Mistral, Meta-hosted Llama and Perplexity, with prompt caching and batch discount modelling, projected from your requests/day and token sizes. Second, what do the per-seat tools cost: M365 Copilot, GitHub Copilot, Cursor, Kiro, ChatGPT Business and Claude Team, against your seat count. Prices were verified against vendor pages on 12 Aug 2026 and the page says so on every table, because half the calculators out there quietly serve stale numbers. Filter to the models you actually use and it gives you one answer line. CSV export, shareable scenario links. No signup, no tracking, no cookies. Installs as a web app and works offline. One deliberate omission, explained on the page: Databricks Mosaic AI, because DBU-based pricing has no honest single number, so there's a custom-rate row instead of a made-up one. Transparency: the site sells a paid kit for teams (unit economics model, allocation and tagging policy templates, CUR/Azure/BigQuery starter queries, chargeback model, maturity assessment). The calculator is free regardless and doesn't nag you about it. What's missing that would make this genuinely useful for your practice? PTU and provisioned-throughput break-even modelling and self-hosted GPU comparison are top of my list. I'll build the most-asked. submitted by /u/TeacherInevitable408 to r/FinOps [link] [comments]

Authored by Trevor Parsons Today, we're announcing new pricing for Bronto. TL;DR: $0.10 per GB ingested, $1 per TB searched — for any signal: logs, traces, or metrics. No per-host or per-metric charges. Retained for 12 months, always hot, with sub-second search. It's designed to be disruptive, simple, predictable, and to align cost with customer value. It's over 100x more efficient than legacy pricing. We're addressing a foundational issue our industry has failed to tackle: current pricing models aren't fit for purpose. They're out of whack from an overall cost perspective, don't align cost with value, and are designed to favor the vendor, not the customer. They're also hard to understand and wildly unpredictable, with teams regularly getting hit with nasty overage surprises. Cost has been the biggest issue for observability customers for the past 15+ years , and vendors continue to willfully ignore it. Observability costs routinely run at 20–30% of total infra spend. That leads to a familiar set of workarounds: dropping high-volume logs, cutting retention to 3, 7, or 15 days, sampling, archiving and rehydrating, building pipelines just to throw data away before it lands, or rolling your own observability on open source and inheriting all the management overhead. One example that stuck with us: a company resized its hosts onto bigger AWS instances just to fight per-host pricing — even though that wasn't the right architecture for their system. They changed their infrastructure to suit their observability bill. That's how distorted this pricing has become. Instead of tackling the problem head-on, vendors keep adding "features" on top of datastores that aren't fit for purpose. The latest wave is AI agents and automated workflows. Those capabilities are genuinely powerful and will help teams manage complex systems, reduce MTTR, and improve root-cause analysis — but if you build AI capabilities on top of fundamentally broken foundations, the cost problem only gets worse, especially as AI workloads drive even higher data volumes. Legacy Pricing Is Broken There are two core problems with legacy pricing. First , it's roughly 10x too expensive no matter how you slice it. Vendors charge dollars per GB ingested and stored, when per-GB pricing needs to be at the level of cents per GB — so teams stop architecting around the cost. Observability spend should drop from ~30% of infra spend to under 5%, low enough that you stop engineering around it. On top of that, dollar-per-GB pricing is typically for only days of hot retention, meaning you're paying dearly for access to a sliver of your data — again, at least an order of magnitude off for an AI world where historical analysis over months or years should be the norm. Second , it's a value problem. You pay to ingest and store, so the vendor gets paid whether or not you ever get value from the data. You may never log in, search, or add an alert — the vendor still gets paid. In logging especially, people describe their provider as an expensive datastore they never actually use. The model was built for the vendor, not the customer. Enter Bronto — Pricing Built for the Customer, Not the Vendor Built on BrontoDB, our custom-built observability datastore, Bronto drives massive efficiency in ingesting, storing, and analyzing observability data. Bronto pricing: cents per GB, not dollars per GB. $0.10 per GB ingested — any signal, logs, traces, or metrics. No per-host or per-metric charges. Retained for 12 months, always hot, with sub-second search. $1 per TB searched — 5x cheaper than scanning the same data on AWS Athena, which runs $5 per TB. If you ingest data and never search it, you pay very little. You pay more only when you get more value by searching across more data — incentives that line up with yours, not against them. For enterprise plans, typical DevOps usage at scale comes in under a combined ingest-and-search cost of about $0.20 per GB (roughly $0.10 for ingest, ~$0.10 for search at the $1/TB rate). A free trial lets you verify exactly where you'd land with your own data. 100x–1000x More Efficient Bronto's entry plan is built for startups, solo builders, and teams building something new: $25/month for 1TB ingested with 20x search, at 12-month retention — roughly $0.025 per GB for any signal, with no per-host costs. Datadog runs about $2.60 per GB for 30-day retention. A team ingesting 1TB/month might pay around $2,600 with Datadog for 30 days of retention versus $25 with Bronto — 100x cheaper, with over 10x the retention on top, which works out to roughly 1000x more efficient . That's before even factoring in cost explosions from things like high-cardinality metrics. At larger volumes, Bronto's $0.10/GB ingested plus $1/TB searched comes out to around $0.20 per GB all-in for a typical DevOps profile, with 12 months of retention — versus Datadog's $2.60 per GB at 30 days (assuming ~1KB events and 100% log indexing). Roughly 100x more efficient. Simple. Predictable. No surprises. Simple — one per-GB cost for any signal ingested, one per-TB cost for what you search, with 12-month retention by default. Predictable — entry plans have generous built-in headroom; larger plans get a usage dashboard or direct support from the team. No surprises — built-in usage tracking and alerting mean no end-of-month shocks, and data never stops flowing when you hit a limit — you'll just get a heads-up. What Bronto Delivers All your data in one place, with full coverage and no blind spots. Always-hot data at 12-month default retention. Seamless cross-correlation across logs, traces, and metrics without hopping between separate datastores (think Prometheus, Tempo, and Loki, each limited and disjointed in its own way). And AI on top, via Bronto's MCP server , Bronto Vibe , or Bronto's built-in investigation capabilities . Try Bronto Today Spin up a free trial and run the numbers against your own data, or read the full details at bronto.io/pricing .

Our AWS rep hooked us up with CloudKeeper. They pitched us their AZ product and said we could get a 2% discount off our AWS bill and free support, plus some finops tools, the product doesn’t cost us anything, they don’t have to take over our AWS root account and we can leave with 60 days notice. Said they make their money on bulk AWS discounts that get that they share with us. It’s not a huge amount of savings, but there doesn’t seem to be a downside. Anybody have any experience with them. I’ve been in the business for a long time and there is usually a gotcha in there somewhere. submitted by /u/DReddit111 to r/FinOps [link] [comments]

Seen a few "why is our Datadog bill suddenly insane" threads here, so figured I'd write up the actual mechanisms instead of just "check your log volume." Custom metric cardinality — you're billed per unique tag-combination, not per metric. One new tag with enough distinct values can 10-40x your time-series count on a metric that "didn't change." Log indexing — ingestion and indexing are separate line items, and a debug-level log bump or a retry loop that starts logging every attempt goes straight into the expensive tier unless you've got exclusion filters actively scoping it. APM span indexing — same deal as logs. A sampling-rate change or a new hot endpoint can push you from a small sampled fraction to near-full indexing without anyone touching a Datadog setting directly. Host-tier boundaries — per-host pricing has step jumps. Autoscaling that crosses a tier threshold during a traffic spike can produce a bill jump way out of proportion to actual growth. None of these are Datadog doing anything wrong technically — they're just invisible until the invoice shows up. Happy to dig into specifics if anyone's dealing with a spike right now — drop what you're seeing in the comments and I'll take a look. submitted by /u/Nylor_Tech to r/devops [link] [comments]

Every FinOps conversation about AI cost I have run into loops back to cost per token. It is the number vendors publish, so it feels concrete. It is also the wrong number to argue about. At the AI deployments I have worked on close enough to see the real numbers, the token bill was rarely more than a third of the actual TCO. The rest sat in three places nobody was tracking as tightly. GPU underutilization at inference is the first one. Reserved capacity sitting at single-digit average utilization is normal, not exceptional. Teams blame batching. The real cause is a prompt-mix distribution nobody profiled before signing the reservation, and the invoice for that gap does not carry a "token" label. Storage is the second. Vector stores, eval traces, and audit logs outpace the token bill within a couple of months of any real RAG workload going live. It is not that any single thing is expensive. It is that nobody set a lifecycle policy at design time and the growth curve is invisible until it is not. Governance is the third and the most awkward, because most FinOps units skip it entirely. Evaluation pipelines, red-team runs, human-review loops, policy scans. Engineering time and pipeline compute, not a line on the AI vendor invoice, but it is TCO. Anyone who runs a compliance-adjacent workload has felt this bucket outgrow the token bucket without ever showing up on a cost dashboard. The docs and pricing pages train us to argue about fifteen cents versus thirty cents per million tokens as if that is the FinOps decision surface. It is the marketing surface. So the practitioner question. What unit does your team actually use for AI workloads? - cost per token - cost per successful task or workflow - cost per active user per month - cost per business outcome (ticket resolved, fraud caught, revenue attributed) Or is your team stuck between the vendor unit and the business unit with nothing that stays honest under load? submitted by /u/matiascoca [link] [comments]

Every developer knows rubber-duck debugging: you explain your code to a rubber duck on your desk, and halfway through the explanation you spot the bug yourself. The duck just sits there. Silent. Judging. I wanted a duck that judges out loud . So I built Unducked . Paste in your code, and a foul-mouthed rubber duck reviews it like Gordon Ramsay reviews a risotto. It roasts you. It calls your function RAW. And then, annoyingly, it finds the actual bug and hands you the fix. Try it right now: unducked.com . There's a dice button that fires a random piece of broken code at the duck, so you can taste a roast without pasting anything. It's genuinely useful (the roast is a real code review) and it's the kind of thing you screenshot and send to the group chat. The whole thing is one TypeScript file, a cheap model, and a public streaming endpoint on AWS. Two things surprised me building it, and both are the interesting part of this post: The "AI" was the easy bit. The duck's entire personality is one system prompt. The model never changed. Getting a public endpoint was the hard bit , and not for the reason you'd think. More on that in Step 6. Here's how to build your own. The mental model: an agent is a model + a prompt The "AI" here isn't complicated. An agent is just a model with a personality bolted on via a system prompt. That's the entire trick. Here's the shape of what we're building: Browser (unducked.com) → CloudFront + Lambda proxy (public HTTPS; signs requests for the browser) → AgentCore Runtime (hosted agent endpoint) → Strands Agent (Chef Duck persona) → Bedrock (Amazon Nova Lite) You write a Strands agent in TypeScript. The AgentCore CLI deploys it as a hosted endpoint on AWS. A tiny Lambda proxy makes that endpoint safely callable from a browser. No hand-written Lambda business logic, no API Gateway, no Docker. Just TypeScript and a couple of CLI commands. Step 1: Set up your environment (Node.js, AWS CLI, AgentCore) You'll need an AWS account, Node.js 22+, and npm. You also need AWS credentials and a couple of CLI tools on your machine. The fast path (let an AI agent do it). If you use a coding agent (Claude Code, Cursor, Kiro, Codex), hand it this and let it set everything up for you: Set up Agent Toolkit for AWS by following these instructions: https://raw.githubusercontent.com/aws/agent-toolkit-for-aws/refs/heads/main/setup-instructions/setup.md It configures credentials and installs the AWS tooling in one shot. Or, manually: # 1. Install the AWS CLI (macOS shown; see AWS docs for other platforms) brew install awscli # 2. Configure credentials, then verify they work aws configure aws sts get-caller-identity # 3. Install the AgentCore CLI and the AWS CDK (AgentCore uses CDK to deploy) npm install -g @aws/agentcore aws-cdk One more thing: in the Bedrock console, enable model access for Amazon Nova Lite . That's your toolchain. Step 2: Scaffold the project with AgentCore CLI One command scaffolds everything: agentcore create agent \ --name Unducked \ --type create \ --build CodeZip \ --language TypeScript \ --framework Strands \ --model-provider Bedrock \ --memory none You get this structure: Unducked/ ├── agentcore/ # Config + CDK (you won't touch this) └── app/Unducked/ ├── main.ts # The agent ← the file that matters ├── model/load.ts # Which Bedrock model to use ├── package.json └── tsconfig.json The scaffold drops in an example tool and an MCP client. Nice for later, but we'll strip them out for a pure roasting duck. Step 3: Write the agent (the system prompt is the product) This is where the personality lives, and it's the whole product. // app/Unducked/main.ts import { BedrockAgentCoreApp } from ' bedrock-agentcore/runtime ' ; import { Agent } from ' @strands-agents/sdk ' ; import { loadModel } from ' ./model/load.js ' ; const SYSTEM_PROMPT = `You are Chef Duck — a foul-mouthed-but-brilliant rubber duck that reviews code like Gordon Ramsay runs a kitchen. - Open with a short, savage roast of the CODE (never the person). Kitchen metaphors encouraged: "this function is RAW", "it's so nested it's got its own zip code". - Then ACTUALLY HELP. Every roast must name the concrete bug and give the fix. Useful first, funny second. - The "no bug" path: if the code has no real defect, roast it for being boring, concede in one line ("...fine. It's not garbage."), and STOP. Type annotations, input validation, and null checks are NOT bugs — never suggest them for code that already works. Inventing improvements is failing. - Keep it tight. PG-13 — spicy, not vile. Plain prose, no headings, a fenced code block for the fix.` ; const model = loadModel (); // One Agent per session so follow-up questions keep the roast in context. const agents = new Map < string , Agent > (); const app = new BedrockAgentCoreApp ({ invocationHandler : { async * process ( payload : any , context : any ) { const sessionId = context ?. sessionId ?? ' default-session ' ; let agent = agents . get ( sessionId ); if ( ! agent ) { agent = new Agent ({ model , systemPrompt : SYSTEM_PROMPT }); agents . set ( sessionId , agent ); } for await ( const event of agent . stream ( payload . prompt ?? '' )) { if ( event . type === ' modelStreamUpdateEvent ' && event . event ?. type === ' modelContentBlockDeltaEvent ' && event . event . delta ?. type === ' textDelta ' ) { yield { data : event . event . delta . text }; } } }, }, }); app . run ({ port : parseInt ( process . env . PORT ?? ' 8080 ' ) }); Three pieces: The system prompt is the product. Everything that makes it "Chef Duck" is those few sentences. BedrockAgentCoreApp wires the agent to the HTTP endpoints the runtime expects. You just write the handler. Stream the roast back by iterating over agent.stream() and yielding each text delta. loadModel() points at Amazon Nova Lite , ~$0.06/$0.24 per million tokens on Bedrock, so roasts cost a fraction of a cent. But here's the thing that took the most iteration: the hardest part of the prompt is the "no bug" path. Cheap models are desperate to be helpful. Hand them working code and they'll "improve" it with type checks and validation nobody asked for, which ruins the joke and gives bad advice. The prompt has to explicitly forbid that and tell the duck to just concede when the code is fine. Getting a cheap model to shut up was harder than getting it to roast. Gotcha that cost me time: the stream emits several event types, and you can only reach event.event after narrowing on event.type first. The three-part if above is what actually compiles. A bare event.event?.delta?.type throws a TypeScript error. Copy it exactly. Swap the model ID in model/load.ts for Claude Haiku or Sonnet if you want more polish. Step 4: Test locally with agentcore dev agentcore dev In another terminal: agentcore dev "function last(arr) { return arr[arr.length]; }" You'll get an off-by-one roast streamed back, live. If that works, your duck is alive. Step 5: Deploy to AWS agentcore deploy The CLI compiles your TypeScript, packages it, uses CDK to stand up the IAM roles and an AgentCore Runtime endpoint, and wires up CloudWatch logging. First deploy takes a few minutes while CDK bootstraps; after that it's fast. agentcore invoke "def add(a, b): return a - b" --stream If the duck tells you your add function is a liar, you're live on AWS. Step 6: Make the endpoint public (the actually-hard part) Here's the wrinkle nobody warns you about. Your agent is deployed, but the AgentCore endpoint requires AWS SigV4-signed requests . A browser can't call it directly, and you must never sign from client-side JS (that ships your AWS credentials in the page source). So you need something in the middle that holds an IAM role and signs on the browser's behalf. The obvious move, a public Lambda Function URL with AuthType: NONE , does not work . The reason is a great story: AWS's own security tooling detects the world-accessible Lambda and automatically scopes the permissions back down. Your calls quietly start returning Forbidden . The platform is protecting you from yourself. The setup that actually holds up: CloudFront distribution (public HTTPS, injects CORS, SigV4-signs to origin) → Lambda Function URL (AuthType = AWS_IAM, streaming proxy) → AgentCore Runtime (InvokeAgentRuntime) CloudFront is the public face. It signs each request to a private, IAM-authed Lambda using an Origin Access Control (OAC) . The Lambda is never world-accessible; CloudFront is. The Lambda itself is tiny: it forwards the prompt to the runtime and streams the SSE response straight back: // proxy/index.mjs — the whole proxy, minus CORS boilerplate export const handler = awslambda . streamifyResponse ( async ( event , responseStream ) => { const { prompt } = JSON . parse ( event . body ?? ' {} ' ); const res = await client . send ( new InvokeAgentRuntimeCommand ({ agentRuntimeArn : RUNTIME_ARN , runtimeSessionId : sessionId , // AgentCore requires ≥ 33 chars accept : ' text/event-stream ' , contentType : ' application/json ' , payload : new TextEncoder (). encode ( JSON . stringify ({ prompt })), })); // The runtime already emits well-formed `data: ...\n\n` SSE frames. Forward verbatim. for await ( const chunk of res . response ) responseStream . write ( chunk ); responseStream . end (); }); Two gotchas here each cost me an afternoon, so I'll save you both: POST bodies need an x-amz-content-sha256 header. Lambda Function URLs behind OAC reject unsigned payloads. CloudFront signs assuming the client already hashed the body . So the browser has to send the SHA-256 of the request body, or you get "signature does not match." CloudFront needs both lambda:InvokeFunctionUrl and lambda:InvokeFunction . Grant only the first and you still get Forbidden . The repo's blogs/deployment-notes.md has the exact CLI commands for the proxy, the OAC, the CORS response-headers policy, and the IAM. Reproduce it from scratch in a few minutes. Step 7: The frontend (SSE streaming from the browser) The UI is one HTML file, no build step, and I'm going to spend almost no time on it because the interesting work is behind it. It's a paste box, an ASCII duck, and that dice button. The only part that matters is how it talks to the agent: send the code, read back a Server-Sent Events stream . const res = await fetch ( API_URL , { method : " POST " , headers : { " Content-Type " : " application/json " , " Accept " : " text/event-stream " , // required: the agent streams SSE, not JSON " X-Amzn-Bedrock-AgentCore-Runtime-Session-Id " : sessionId , // For prod, CloudFront's OAC needs the body hash (see Step 6): " x-amz-content-sha256 " : await sha256Hex ( body ), }, body : JSON . stringify ({ prompt : code }), }); const reader = res . body . getReader (); const decoder = new TextDecoder (); let buffer = "" ; while ( true ) { const { done , value } = await reader . read (); if ( done ) break ; buffer += decoder . decode ( value , { stream : true }); const frames = buffer . split ( " \n\n " ); buffer = frames . pop (); // keep any partial frame for ( const frame of frames ) { const line = frame . split ( " \n " ). find (( l ) => l . startsWith ( " data: " )); if ( line ) onToken ( JSON . parse ( line . slice ( 5 ). trim ())); // append token to the page } } Two things to remember: the server requires Accept: text/event-stream (without it you get a JSON error, not a stream), and the response is a stream of token strings, not one JSON blob. That's what makes the roast type out live, like the duck is thinking. Locally the frontend detects localhost and skips CloudFront, talking straight to agentcore dev on port 8080. One safety note since you're injecting model output into the page: escape everything before you format any Markdown. A dozen lines of regex handles bold and code fences without letting raw HTML through. Step 8: Put it on the internet with GitHub Pages Push to GitHub, then Settings → Pages → Deploy from branch main , folder / . A minute later your duck is live. HTTPS, free, auto-deploying on every push. Point a custom domain at it (I use unducked.com ), set the frontend's production endpoint to your CloudFront URL, and you've got a product. git clone https://github.com/tmoreton/tutorials open tutorials/index.html Watch the bill: cost controls for a public AI endpoint The endpoint is public and unauthenticated, so anyone with the URL can spend your Bedrock tokens. Nova Lite is cheap (a fraction of a cent per roast), but a viral moment shouldn't become a surprise invoice, so at minimum: Set a reserved-concurrency cap on the Lambda (I use 2). That's a hard ceiling on how fast anyone can burn tokens. Add an AWS budget alarm so you find out early. Rate limiting and WAF: locking it down without a login wall The whole appeal of Unducked is that you click a link and roast some code, no signup, no API key. That's also the problem: a public, unauthenticated endpoint is a standing invitation for someone to script a loop against it, drain your token budget, and lock everyone else out. The goal is to make that expensive and annoying for an abuser while staying frictionless for a real visitor. Here's the stack of defenses I settled on, cheapest first. None of them ask the user to sign in. 1. Cap the input size (already in the proxy). The single biggest lever on cost is how many tokens each request carries. A roast needs a snippet, not a novel, so the proxy truncates the prompt before it ever reaches Bedrock: const MAX_PROMPT_CHARS = parseInt ( process . env . MAX_PROMPT_CHARS ?? ' 8000 ' ); // ... const prompt = ( body . prompt ?? '' ). slice ( 0 , MAX_PROMPT_CHARS ); That one line turns "paste a 2 MB file and cost me dollars" into a non-event. It also bounds output indirectly because the system prompt already tells the duck to keep it tight. 2. Reserved concurrency is your circuit breaker. The Lambda cap from above isn't just about tokens; it's the ceiling on total throughput . With a reserved concurrency of 2, there is no amount of traffic that makes the bill run away; excess requests get throttled at the proxy, not billed at Bedrock. Set it deliberately low and treat it as the backstop behind everything else. 3. Put AWS WAF in front of CloudFront. This is the real fix. WAF (Web Application Firewall) is a rules engine that sits in front of your CloudFront distribution and inspects every request before it reaches your origin. Nothing changes in your Lambda or your frontend; you attach a "Web ACL" (a bundle of rules) to the distribution and CloudFront enforces it. For a public toy the one rule that matters is a rate limit , and it needs no login: Rate-based rule, keyed by client IP. WAF counts requests per IP over a rolling window (1, 2, 5, or 10 minutes) and acts on anyone over the limit. The floor is 100 requests per 5 minutes, well above a human clicking "Roast it," far below a script in a loop. Challenge action instead of a hard block. Rather than returning 403 , set the over-limit action to Challenge (or CAPTCHA ). WAF serves a silent browser proof-of-work that a real browser passes invisibly but a curl loop or headless scraper fails. The challenge only fires on requests above the rate limit, so normal visitors stay under it and never see anything. This is the closest you get to "login-grade" protection with zero friction. Geo or bot-control rules if you want to go further. The AWS Managed Rules bot-control group catches common scrapers, though it adds cost. To enable it, in the WAF console : create a Web ACL, set Resource type → CloudFront distributions , associate your distribution, add a rate-based rule (limit 100 , aggregate on source IP , evaluation window 5 minutes ), set its action to Challenge , and save. Or the one CLI call that does the same thing (CloudFront Web ACLs live in us-east-1 ): aws wafv2 create-web-acl \ --name unducked-rate-limit --scope CLOUDFRONT --region us-east-1 \ --default-action Allow ={} \ --visibility-config SampledRequestsEnabled = true ,CloudWatchMetricsEnabled = true ,MetricName = unducked \ --rules '[{"Name":"rate-per-ip","Priority":0, "Statement":{"RateBasedStatement":{"Limit":100,"AggregateKeyType":"IP","EvaluationWindowSec":300}}, "Action":{"Challenge":{}}, "VisibilityConfig":{"SampledRequestsEnabled":true,"CloudWatchMetricsEnabled":true,"MetricName":"rate-per-ip"}}]' # then associate the returned Web ACL ARN with the distribution (set it as the distribution's WebACLId) That's exactly what's guarding unducked.com right now. WAF's own logs and CloudWatch metrics then show you who got challenged, so you can watch for abuse without watching the bill. 4. Keep CORS locked to your origin. The proxy already restricts Access-Control-Allow-Origin to https://unducked.com . It won't stop a determined attacker (CORS is browser-enforced, and curl ignores it), but it stops the lazy case where someone embeds your endpoint from their own site. The honest limit: without authentication you can't make abuse impossible , only uneconomical . But layering all four (an input cap, a hard concurrency ceiling of 2, a WAF rate-limit-plus-challenge, and CORS locked to the origin) is exactly what's live on unducked.com , and together they mean a casual attacker bounces off while a real visitor never notices a thing. A budget alarm catches anything that slips through. If it ever went truly viral-with-a-target-on-its-back, the next step would be a lightweight anonymous token (a per-session nonce your page mints), but for a code-roasting duck that's overkill. The full picture: architecture summary Layer What How Personality A system prompt The whole product, really Model Amazon Nova Lite Amazon Bedrock Agent ~30 lines of TypeScript Strands Agents SDK + AgentCore Backend hosting agentcore deploy AgentCore Runtime Public endpoint Streaming Lambda + CloudFront Signs requests for the browser Frontend hosting Push to GitHub GitHub Pages The lesson underneath the jokes: a capable model plus a sharp system prompt is a shippable product, and the AI is the cheap part. The fiddly work is the plumbing that makes it public and safe. Change the prompt and Chef Duck becomes a patient mentor, a passive-aggressive senior dev, or a security auditor. Same stack, same afternoon. Source code The complete code is on GitHub → Go roast some code. Your duck is disappointed in you already.