Avrupa, Latin Amerika, Orta Doğu ve Afrika merkezli çok sayıda ülkede satılacak Fiat Grizzly için tasarımı resmi olarak paylaşıldı. Grande Panda’nın daha büyük versiyonu olarak tanımlanan araç, ailelere yönelik bütçe dostu bir SUV ve daha sportif “Coupe-SUV” versiyonu da bulunuyor. Araç hakkında ABD’nin Michigan eyaletinde düzenlenen etkinlikte konuşan Fiat CEO’su Olivier Francois, “Yeni Fiat Grizzly’nin burada iki farklı silüetini görüyorsunuz çünkü bu araç dünyanın üç farklı bölgesi için tasarlandı. Yeni model, Panda ve Grande Panda ailesini tamamlıyor. Aynı DNA’yı taşıyorlar ve yine Smart Car platformu üzerine inşa edildiler ancak bu kez karşımızda daha büyük bir araç var.” ifadelerini kullandı. Pazara benzinli, elektrikli ve hibrit altyapılarla çıkarılması beklenen Grizzly, resmi olarak Türkiye pazarına da getirilebilir.

Yeni model Fiat Grizzly için tasarım resmi olarak paylaşıldı yazısı ilk olarak Teknoloji haberleri – LOG üzerinde yayınlandı.

Have you ever run into any of these situations in Blender?

• You placed an object at Z = 0, but it’s floating in the air (or half-buried in the floor)
• An imported model has its origin at the mesh center instead of the bottom
• You tried to move the origin to the bottom, and it took way more steps than it should

If the origin is at the bottom center, you can just snap it to Z = 0 and it sits perfectly on the floor.
But getting there requires a few annoying steps:

1. Switch to Edit Mode, select the bottom face/vertices, and snap the 3D Cursor there
2. Switch back to Object Mode and set the origin to the 3D Cursor

When you’re building an interior scene with a bunch of furniture, doing this over and over gets old fast.

So I made an add-on that handles it with a right-click.

Origin to Bottom Center

A simple Blender add-on that automatically moves an object’s origin to the center of its lowest point (bottom face).

Select multiple objects and it updates each one individually — no need to do them one by one.

When it comes in handy

Placing furniture and props
With the origin at the bottom, dropping something at Z = 0 places it perfectly on the floor. No manual height adjustment needed.

Fixing imported models
Models from other apps often have inconsistent origin positions. Select them all and fix in one shot.

Organizing a scene
When arranging multiple objects side by side, having consistent origin positions makes placement a lot easier.

Download (Free)

Available for free on Booth:

👉 https://epartworks.booth.pm/items/8275371

Wrapping up

I’m planning to keep building small add-ons for operations that feel like they should be in Blender already but aren’t.

If this was useful, a follow would make my day 😊

Atalaya — Spanish for “watchtower”. A high vantage point with a clear view over the territory below.

The uncomfortable question

Open VSCode. Search “SQLite”. Pick the most popular extension. Run find node_modules -type d | wc -l after installing it locally. Most of them sit somewhere between 200 and 500 packages in their transitive dependency tree, plus native code bundled per platform.

For a tool whose single job is to read a .db file.

[TODO: drop a personal anecdote here — when did supply-chain risk in editor extensions stop being abstract for you? a coworker affected, a compromised package you remember, the recent xz-utils backdoor narrative, whatever feels honest. one paragraph max.]

When that’s the surface a tool sits on, every extension you install is a trust decision you’re making against your entire workspace: source code, environment variables, git credentials, the lot. And the threat is not theoretical — compromised VSCode extensions have shipped credential-stealers to thousands of developers in the last two years.

So I built one I could actually audit.

Meet Atalaya

Atalaya is a SQLite viewer for VSCode with a deliberately minimal footprint:

• One runtime dependency in the shipped .vsix: sql.js, the official SQLite compiled to WebAssembly
• Zero native modules — no node-gyp, no platform-specific compilation
• Zero network calls, zero telemetry, zero remote resource loading
• Strict CSP in the webview (default-src 'none' + per-load cryptographic nonce)
• All SQL identifiers are allowlist-validated; all SQL values bound via prepared statements
• MIT licensed, ~2,300 lines of code total

Features that actually matter

It opens .db, .sqlite, .sqlite3 files as a custom editor. From there:

• Browse any table with pagination, type-aware coloring, sortable headers, per-column filters
• Run arbitrary SQL from the Query tab with Ctrl/Cmd+Enter
• Edit cells inline by double-clicking; Enter saves to memory, Cmd+S writes to disk
• Undo and redo anything you ran in the SQL editor or edited inline — natively via VSCode’s edit stack
• Export any table or query result to CSV or JSON
• Read-only fallbacks for views, tables without a primary key, and BLOB columns — the UI gets out of your way when it can’t safely edit

The SQL editor has autocomplete — implemented in plain JS

I refused to pull in Monaco Editor because that’s another ~5 MB of code I’d ask you to trust. So the autocomplete is hand-rolled vanilla JS. About 250 lines. It scopes column suggestions to whatever tables your query already references.

After FROM, JOIN, INTO, UPDATE it suggests tables. After SELECT, WHERE, AND, OR, ON, ,, ( and friends it suggests columns. Arrow keys navigate, Enter / Tab insert, Esc closes. Cmd+Enter runs the query — the popup never intercepts it.

It’s not Monaco. It doesn’t do alias resolution or syntax highlighting. That’s the point. You can read every line of it.

Query results don’t surprise you

Anything that modifies data — INSERT, UPDATE, DELETE, or DDL — marks the document as modified and goes onto the undo stack. Nothing touches the disk until you press Cmd+S. Close without saving to throw away mutations.

What “audit-friendly” actually means

I want to be honest about what this label does and doesn’t promise, because it’s the most important claim I’m making.

It doesn’t mean invulnerable. Nothing is invulnerable.

It does mean every line of the runtime surface is short enough to read. Let me make that concrete:

• TypeScript source (4 files): ~810 lines
• Webview JS / CSS (viewer.js, viewer.css): ~1,460 lines
• That’s the whole story. ~2,300 lines.
• Plus the bundled sql-wasm.js glue (~46 KB of code wrapping the WASM blob)
• Plus the sql-wasm.wasm binary — opaque, but compiled from public, well-reviewed SQLite source

You can read all of the JS and TS in an afternoon. The WASM blob is what it is, but it’s the SQLite the world has been using for decades.

I also paid for that minimalism in features. Things Atalaya does not have:

• Schema visualization (coming, but only if I can do it without adding deps)
• Multi-file workspace, query history, saved snippets
• AI assistants, copilots, “smart suggestions”
• Subscription, account, sync, anything cloud

This isn’t a “minimum viable product”. It’s a deliberately small tool with a specific posture.

The receipts

Don’t take my word. Verify it yourself:

git clone https://github.com/Marioloez/atalaya
cd atalaya
npm ci --ignore-scripts          # neutralize any postinstall hooks
npm ls --omit=dev --all          # show the production dep tree

That last command prints exactly two lines:

atalaya@<version>
└── sql.js@<version>

Then read src/sqlite/service.ts and src/editor/sqliteEditor.ts. They’re short. They do exactly what they look like they do.

And if you find something I missed — open an issue. The whole point of this is that finding things is supposed to be possible.

A small clarification about the dev install

There’s one caveat I want to surface, because anyone running npm install will see it immediately: my devDependencies tree pulls in @vscode/vsce (the marketplace packager), which transitively pulls keytar (a native module for credential storage). That keytar binary runs a postinstall script during npm install.

None of it ships in the .vsix. I verified by running unzip -l atalaya-*.vsix — only my own compiled code, sql-wasm.js, sql-wasm.wasm, README, LICENSE, CHANGELOG, and the icon. Zero @azure/*, zero keytar, zero native binaries.

But if you intend to clone and inspect the source, run npm ci --ignore-scripts to keep the install hermetic. I documented this in the README. The “audit-friendly” claim is about the shipped artifact, not about the build chain. Clarity matters more than marketing.

Install

code --install-extension marioloez.atalaya

Or search Atalaya in the VSCode Extensions view.

GitHub: github.com/Marioloez/atalaya
Marketplace: marketplace.visualstudio.com/items?itemName=marioloez.atalaya

Why this matters beyond Atalaya

I’m not trying to replace every SQLite viewer. Some people genuinely need the feature-rich ones, and that’s fine — it’s a tradeoff worth taking when the cost is acceptable to them.

But the default trust posture of “install the popular extension, accept the 300 transitive packages, move on” deserves more scrutiny than it currently gets in developer workflows. The cost of that trust is hidden right up until someone exploits it.

Build small. Show the receipts. Make verifying easy.

[TODO: close with a short personal line in your voice — what would you ask a reader to do next? “audit a single extension in your editor this week” / “let me know what you’d improve” / “tell me what other tools deserve a minimal-trust rewrite”. keep it one sentence.]

Built with sql.js, the VSCode Custom Editor API, and a refusal to add dependencies.

Geliştirme çalışmaları uzun süredir devam eden küçük boyutlu Mercedes-Benz G-Class, bugün bir kez daha gündeme getirildiği kadarıyla hem elektrikli hem de hibrit olarak sunulacak. AMG’nin başındaki isim Michael Schiebe tarafından yapılan açıklamaya göre, bu önemli kararın arkasında ABD’deki bayilerin büyük talebi yer alıyor. Bayilerin elektriğe kıyasla çok daha yüksek satış potansiyeli bulunduğu için “mutlaka içten yanmalı motora sahip bir versiyonu da sunulmalı” dediği araç, CLA’da da kullanılan turbo destekli 1.5 litrelik dört silindirli hibrit üniteyi temel alabilir. İlk aşamada tamamen elektrikli olarak sunulması hedeflenen aracın, bu tarafta iki farklı elektrik motoru ve yaklaşık 724 km menzil sunacak 85 kWh kapasiteli NMC batarya ile donatılması bekleniyor. Resmi tanıtımının 2026 bitmeden yapılacağı, yollara ise 2027’de çıkarılacağı öne sürülen “Little G”, uzun süredir kamuflajlı olarak gerçek yol şartlarında test ediliyor.

“Ufak” Mercedes-Benz G-Class, bayi talebi nedeniyle içten yanmalı motor kazanmış yazısı ilk olarak Teknoloji haberleri – LOG üzerinde yayınlandı.

The Question That Started It All

Three posts in, I kept running into the same address:
11111111111111111111111111111111.

It showed up as the owner of every wallet I queried.
It appeared in every transaction I decoded in
Post 3.
My explorer from Post 2
labeled it “System Program” but I didn’t really
understand what that meant.

Why can’t I create an account myself? Why does the
System Program have to do it? Why does every wallet
on Solana belong to it?

The answer reveals Solana’s elegance: The System
Program is Solana’s kernel. Like a Unix kernel
manages processes and permissions, the System Program
manages accounts and state creation. Understanding it
means understanding why Solana works the way it does.

What is the System Program?

Address: 11111111111111111111111111111111

It’s an account like any other:

Owner: NativeLoader
Executable: true
Data: “system_program” (14 bytes of ASCII)
Balance: 1 lamport (rent-exempt minimum)

But it’s special. It’s built into Solana’s validator
runtime. When you send a transaction, the System
Program enforces:

• Who can transfer SOL
• Who can create accounts
• How much lamports accounts must hold
• Account closure rules

Run this yourself and see:

solana account 11111111111111111111111111111111

The System Program — owned by NativeLoader, built
into the validator runtime itself.

Core Insight: Programs Own Accounts, Not People

Here’s what breaks beginner brains:

Your wallet is NOT owned by you. It’s owned by the
System Program.

Your Wallet
├── Owner: 11111111111111111111111111111111 (System Program)
├── Executable: false
├── Data: (empty)
└── Lamports: 5000000000 (your SOL)
Run this to see it yourself:

solana account <your-wallet-address>

Your wallet — owned by the System Program, not by
you. Your keypair is the authorization mechanism,
not the owner.

But you can spend from it because:

• Your keypair can sign for this account
• The System Program’s rules say: “Only an account
  owner or authorized keypair can transfer from
  that account”
• Since your keypair authorized the transfer, the
  System Program allows it

You don’t own your account. You authorize
transactions on it. The System Program enforces
your authorization.

This is the security model: permission enforcement
is separate from asset ownership.

The System Program’s Four Main Instructions

1. CreateAccount

Creates a new account owned by you or another program.

// What it does:
// 1. Allocate <space> bytes for data
// 2. Set owner to <owner> (usually your program)
// 3. Charge enough lamports to cover rent
// 4. Fund from payer's account

Cost: ~2,400 lamports for a basic account +
space × 0.00348 lamports/byte

2. Transfer

Move SOL from one account to another.

// What it does:
// 1. Decrease sender's lamports by amount
// 2. Increase recipient's lamports by amount
// 3. Verify sender's keypair signature

No fee beyond the network fee. The System Program
allows SOL transfer to any account.

3. Allocate

Increase an account’s data space after creation.

// What it does:
// 1. Expand data buffer
// 2. Charge additional lamports for rent

4. Assign

Change an account’s owner.

// What it does:
// 1. Set owner to <new-owner>
// 2. Only works if current owner is System Program

Example: The Lifecycle of Creating an Account

When you create a token, the Token Program does this:
User sends transaction:
│
├─> System Program.CreateAccount(…)
│ ├─ Payer: Your wallet
│ ├─ New Account: Token mint address
│ ├─ Owner: Token Program
│ └─ Space: 82 bytes
│
└─> Token Program.InitializeMint(…)
├─ Reads the new account
├─ Writes mint data
└─ Returns success
Result:
Token Mint Account
├── Owner: TokenkegQfe… (Token Program)
├── Executable: false
├── Data: (82 bytes with supply, decimals, authorities)
└── Lamports: (rent-exempt amount)
Notice the two-step pattern:

1. System Program creates the container — allocates
   space, funds rent
2. Token Program initializes the data — writes the
   mint info

This separation is the genius: the System Program
handles account creation, programs handle
initialization.

Rent: The System Program’s Economic Rule

The System Program enforces rent-exemption economics.

You must hold:
(account_data_size + 128) × 0.00348 lamports/byte

// Example: 1 KB account
const dataSize = 1000;
const rentPerByte = 0.00348; // lamports
const requiredLamports = (dataSize + 128) * rentPerByte;
const requiredSOL = requiredLamports / 1e9;

console.log(`Required: ${requiredSOL} SOL`);

Check it yourself:

solana rent 1000

The exact SOL required to rent-exempt a 1KB
account. The System Program enforces this minimum
at account creation.

Note: Only 0.000000004 SOL for 1KB — rent-exemption
is cheap for small accounts. Scale this up to
10MB and it becomes meaningful.

Note on rent deprecation: Solana deprecated
ongoing rent collection, but the rent-exemption
minimum is still enforced when accounts are created.
You must fund new accounts above this threshold or
the transaction fails.

Seeing It Through the Explorer

If you built the account explorer from
Post 2,
run it against the System Program now:

node explorer.mjs 11111111111111111111111111111111

The same explorer from Post 2 — but now you know
exactly what you’re looking at. NativeLoader means
this program is baked into the validator, not deployed
like other programs.

The output tells the whole story in five fields.

Why This Design?

Consistency

Every program uses the same account model. Every
account creation uses the System Program. The
protocol scales because you don’t write custom
account-creation logic.

Security

The System Program is battle-tested and immutable.
Bugs there would be catastrophic. By centralizing
account logic, Solana minimizes the surface area
for vulnerabilities.

Economics

Rent exemption is managed uniformly. Everyone follows
the same rules: hold enough lamports and your account
lives forever.

Auditability

Want to know who owns an account? Read the owner
field. Solana’s permission model is transparent and
on-chain.

System Program vs. Ethereum

Ethereum:

• Anyone can create accounts implicitly
• Storage rent is implicit through gas fees
• Account ownership is straightforward

Solana:

• System Program explicitly creates all accounts
• Rent-exemption is explicit — you must hold a
  minimum balance
• Account ownership is decoupled from transaction
  authorization — owner vs. signer are different things

Solana’s model is more explicit. You see the kernel.
You can audit it. That explicitness is a feature,
not a bug.

Debugging: Common System Program Errors

“Account does not have enough lamports.”

❌ You tried to create an account without funding
it enough
✅ Calculate the rent-exempt amount and include
extra lamports

“Account is not owned by the System Program”

❌ You tried to Assign an account owned by a
different program
✅ Only the System Program can Assign accounts

“Cannot allocate more space”

❌ Account is owned by something other than the
System Program
✅ Only the System Program allows re-allocation

What to Do Next

1. Inspect the System Program

   solana account 11111111111111111111111111111111

1. Check your own wallet’s owner

   solana account <your-address>

Owner will always be 11111... for a standard wallet.

1. Check rent for different sizes

   solana rent 1000    # 1 KB
   solana rent 10000   # 10 KB
   solana rent 100000  # 100 KB

Watch how the required SOL scales with data size.

1. Read the System Program source
   The code is public. Reading it shows you the exact
   validation rules the entire network runs on.

Wrapping Up Epoch 1

This is the fourth and final post in my Epoch 1
Writing Challenge series. What started with
“everything is an account” ends here — with the one
program that makes the whole account model work.

If you’ve followed along from Post 1, you now have
a complete mental model:

• Post 1 — What accounts are and why they all
  share five fields
• Post 2 — How to query any account and read
  those fields directly
• Post 3 — How to decode the raw binary data
  sitting in those fields
• Post 4 — The System Program that creates,
  owns, and enforces rules on all of it

That’s Solana’s foundation. Everything else — DeFi,
NFTs, DAOs, gaming — is built on top of this.

Series: The Account Model Foundation

• Part 1: The Account Model Explained
• Part 2: Building an Account Explorer
• Part 3: Decoding Account Data
• Part 4: Solana’s System Program — Understanding
  the Kernel (this post)

Have questions about the System Program? Ask in
the comments — I’ll cover advanced patterns like
PDAs (Program Derived Addresses) in a future post.

Part of the 100 Days of Solana Epoch 1 Writing Challenge.

Microsoft Excel sonrasında sunum denildiği zaman akla gelen ilk yazılım olan Microsoft PowerPoint için de “ChatGPT” entegrasyonu geldi. Buradan elde edilebilen yeni ChatGPT eklentisi, PowerPoint’te bir yan panel olarak görev yapıyor ve kullanıcıların yazılı komutları üzerinden etkili sunumlar hazırlayabiliyor. Sunumlar üzerinde yazılı komutlarla düzenleme yapılmasına da imkan tanıyan altyapı, çok büyük zaman kazancı getiriyor ve bu süreçte verilen dokümanları/tablolar ile görselleri kullanabiliyor. Eklenti şu anda beta olarak “Business, Enterprise, Edu, Teacher, K-12, Free, Go, Pro ve Plus” planlarına açılmış durumda.

Excel sonrasında PowerPoint için de ChatGPT entegrasyonu geldi yazısı ilk olarak Teknoloji haberleri – LOG üzerinde yayınlandı.

Most startups overpay for cloud by 30–50% not because engineers are careless, but because the default billing model is on-demand pricing, and it’s the most expensive option available.

Cloud spending is projected to exceed $1 trillion annually by 2027 (Gartner), and the average company wastes 25–35% of that on idle resources, over-provisioned instances, or missed commitment discounts. For a startup running $100K/month on AWS, that’s $25K–$35K wasted every single month.

The tools below are categorized by function, not popularity.

What Are Cloud Cost Optimization Tools?

They fall into four functional categories:

• Visibility: surfaces where money is going by service, team, or environment. Doesn’t act on data; just surfaces it.
• Alerting: detects anomalies after they happen: a forgotten dev environment, an instance left running over a weekend.
• Rightsizing: analyzes utilization and recommends (or automates) instance changes or terminations.
• Commitment purchasing: automates Savings Plans, Reserved Instances, or Committed Use Discounts. An m5.xlarge running on-demand at $0.192/hr drops to $0.141/hr under a 1-year Compute Savings Plan: a 27% reduction without touching a line of code (verify at aws.amazon.com/savingsplans/pricing).

Most startups need tools from at least two categories. A common mistake is spending budget on visibility when the real opportunity is commitment purchasing.

How to Choose: Decision Framework by Stage

• Pre-revenue / $0–$10K/month Use cloud-native tools (AWS Cost Explorer, GCP Cost Management, Azure Cost Management) they’re free. Activate AWS Cost Anomaly Detection (free tier) to catch unexpected spikes. The waste at this level doesn’t justify paid tooling.

• Seed / Series A / $10K–$50K/month Add a visibility layer Vantage (free tier) or Infracost if you have developer-driven infrastructure. Focus on tagging hygiene and environment cleanup first. Start evaluating commitment purchasing if 50%+ of your compute is stable.

• Series B+ / $50K–$500K+/month Commitment purchasing automation pays for itself within weeks. A $200K/month EC2 bill running entirely on-demand has $60K–$80K/month in savings available through Savings Plans and Reserved Instances alone. Manual management at this scale is a full-time job.

The Tools

Vantage: Best for Multi-Cloud Visibility

Vantage connects to AWS, GCP, Azure, Kubernetes, Datadog, Snowflake, and 20+ other services. It provides cost reports, unit cost metrics, and automated savings recommendations.

Strengths: Cost allocation by team/service/environment, free tier, unit cost tracking (cost per customer, cost per API call), consistent multi-cloud reporting.

Limitation: Vantage surfaces recommendations but doesn’t purchase commitments. Acting on Savings Plan or Reserved Instance recommendations still requires manual purchasing or a separate tool.

Best for: Startups needing fast, low-friction visibility across multiple clouds without a dedicated FinOps engineer

Kubecost: Best for Kubernetes Workloads

AWS Cost Explorer tells you that you spent $40K on EC2 last month. Kubecost tells you $18K came from your data processing namespace, $12K from the API tier, and $10K from a test environment nobody shut down.

Strengths: Namespace-, deployment-, and pod-level cost breakdown; idle resource detection within clusters; cost allocation for shared infrastructure; on-premise deployment option.

Limitation: Doesn’t manage commitments. Doesn’t cover non-Kubernetes services, no RDS breakdown, no S3 attribution.

Best for: Startups with Kubernetes as primary compute spending $20K+/month on cluster resources.

Infracost: Best for Pre-Deployment Cost Control

Infracost integrates into CI/CD pipelines and PRs to show engineers the cost impact of infrastructure changes before they’re merged. A developer adds an RDS instance; Infracost posts the monthly cost delta as a PR comment.

Strengths: Catches expensive decisions before production, works with Terraform/Terragrunt/Pulumi, integrates with GitHub/GitLab/Bitbucket, engineers see cost impact in context with no separate login required.

Limitation: Purely a pre-deployment guardrail. Doesn’t manage existing cloud costs or cover non-IaC resources.

Best for: Startups with IaC discipline where engineering velocity is high enough that cost surprises are a recurring problem.

CloudZero: Best for SaaS Unit Economics

CloudZero maps cloud spend to business dimensions, customers, product features, tenants. It answers: “How much does it cost to serve each customer?” or “What does feature X cost us per month?” including for untagged and shared infrastructure.

Strengths: Cost-per-customer and cost-per-feature reporting, handles untagged resources, gross margin visibility, engineering team attribution without requiring perfect tagging.

Limitation: Pure analytics layer. No commitment purchasing automation.

Best for: SaaS startups post-Series A tracking gross margins at the unit level.

ProsperOps: Best for Hands-Off Commitment Management

ProsperOps autonomously manages Reserved Instances, Savings Plans, and Committed Use Discounts across AWS, Azure, and GCP continuously rebalancing to maximize discounts while minimizing over-commitment risk.

One important note: Flexera acquired ProsperOps in 2026. It’s now part of an enterprise IT asset management platform. Verify whether pricing and startup accessibility have changed before assuming startup-friendly terms still apply.

Strengths: Autonomous commitment management, risk-managed purchasing, percentage-of-savings fee model.

Limitation: No buyback guarantee on underutilized commitments underutilization protection depends on the platform’s risk model, not a cash return.

Best for: Multi-cloud teams at $50K+/month who want autonomous commitment management. Validate current pricing post-acquisition.

AWS Cost Explorer + Cost Anomaly Detection: Free, With Caveats

Cost Explorer is free (API calls billed separately) and genuinely useful for straightforward AWS environments. The key constraint: recommendations refresh every 72+ hours. At $10K/day in compute spend, three days of on-demand pricing versus a Savings Plan rate is a measurable cost.

Cost Anomaly Detection is a separate free service worth activating on every AWS account regardless of what else you run.

Best for: Early-stage startups (sub-$50K/month) as a no-cost baseline. The refresh lag becomes a real limitation at higher spend levels.

Usage.ai: Best for Commitment Automation With Zero Lock-In

If you want to understand how Insured Flex Commitments work in practice, Usage.ai covers the mechanics in detail here How Insured Flex Commitments Work

For startups that want the savings of commitment purchasing without the native AWS/GCP/Azure lock-in risk, Usage.ai was built specifically around this problem.

• Insured Flex Commitments deliver 30–60% savings on compute with no multi-year lock-in and no upfront payment. Every commitment is fully insured.
• Buyback Guarantee: If a commitment goes underutilized, Usage.ai buys it back and returns the value as cashback (real money, not credits).
• Zero Lock-In: Commitments adjust quarterly. Scale down with no penalty.
• 24-hour recommendation refresh vs. AWS Cost Explorer’s 72+ hours. At $6K–$12K/day in uncovered compute spend, that 3-day lag compounds to $18K–$36K per refresh cycle in unnecessary on-demand charges.

Operates at the billing layer only 30-minute setup, no infrastructure changes, percentage-of-savings fee model.

Supported clouds: AWS, Azure, GCP.

Are Commitment Tools Worth It for Startups?

Yes, if monthly compute spend is above $50K and at least 40–50% of compute is stable workloads.

The break-even math: A $200K/month EC2 bill running on-demand has ~$60K–$80K/month in Savings Plans savings available. At a 15% fee on $60K in savings, you pay $9K and net $51K/month. Payback period: day one.

The risk is committing beyond your stable usage baseline. Tools like ProsperOps and Usage.ai both analyze usage patterns before purchasing. Usage.ai adds the buyback guarantee so underutilization doesn’t become stranded, a protection ProsperOps doesn’t offer.

Cloud-Provider Commitment Mechanics

AWS: Compute Savings Plans (flexible, apply across EC2/Fargate/Lambda) and Reserved Instances (specific to service/region/instance type). Discount range: 20–60% vs. on-demand.

GCP: Resource-based and spend-based Committed Use Discounts (1 or 3 years), plus Sustained Use Discounts that apply automatically with no commitment required.

For a detailed breakdown of when to use each on GCP GCP CUDs vs SUDs: A Technical Comparison

Azure: Reserved VM Instances, Azure Savings Plans for compute, and Reserved Capacity for SQL Database, Cosmos DB, and Synapse Analytics. Dev/Test pricing is also available for non-production workloads.

Key Metrics to Track

• Commitment coverage rate: % of eligible compute covered by Savings Plans or RIs. Above 60% uncovered is the highest-ROI optimization signal.
• RI/SP utilization rate: % of purchased commitments actually being used. Below 85% means you’re paying for commitments you can’t absorb.
• Cost per unit of business output: cost per customer, API request, or transaction. Connects cloud spend to business economics.
• Untagged resource rate: high untagged rates indicate poor cost governance and make optimization harder.
• Idle resource spend: 10–15% of total spend in idle resources is common in growing startups.
  What’s the biggest blocker your team has hit when trying to act on cost optimization recommendations tooling, ownership, or just time?

Explore the full engineering perspective here → Best Cloud Cost Optimization Tools for Startups