Alephant

Introducing Alephant AI Gateway. Open source. Self-hostable. Built in Rust. The AI gateway built for cost control, not just routing. Multi-LLM cost monitoring. Token-level spend tracking. Agent guardrails. Budget controls. Full observability. → github.com/AlephantAI/AIe…

@Orland_xx @buildxrajesh Exactly, that is what we build for Alephant, welcome to try it free: alephant.io

@alephantai @buildxrajesh Per-agent cost visibility is becoming table stakes. The harder layer is attribution: which workflow, owner, tool loop, or product decision created the spend.

AWS user reportedly got hit with a $30K bill after a Claude agent ran without proper limits on Bedrock. One of the biggest AI problems nobody talks about enough yet: runaway agent costs.

@LearnWithBrij This breakdown is pure gold Most teams over-engineer with MCP when a simple Skill (200 lines of markdown) would do the job in a fraction of the tokens. That “expensive mistake” you mentioned is exactly what kills so many production agentic systems before they even ship. Once you get the primitives right and start running real persistent workflows, the next layer you need is full cost governance. That’s why @alephantai built Alephant — the open-source Agentic Economy Gateway / FinOps layer: • Real-time cost tracking per agent, session & model • Budget guardrails, alerts, auto-fallback & smart routing So you can confidently use the right primitive (and the right model) without token costs spiraling out of control. Free to try 👉 alephant.io Brij, which of the four do you see misused the most in the wild?

Most teams reach for MCP when Skills would do the job in half the tokens. Claude Code has 4 extension points. They are not interchangeable, and most production agentic systems I review use at least two of them wrong. Here is the actual mental model: 𝗦𝗸𝗶𝗹𝗹𝘀 Instructions loaded into context only when triggered. Lives in markdown. Zero API surface. Use when the model needs to know how to do something — a procedure, a domain convention, a checklist. 𝗠𝗖𝗣 A protocol bridge to a system Claude doesn't own. The server holds the capability. Live I/O, real state, real auth. Use when the model needs to talk to something — a database, an API, a file system, a third-party service. 𝗦𝘂𝗯𝗮𝗴𝗲𝗻𝘁𝘀 A delegated context window running its own focused loop. Returns a compact summary back to the parent, not the whole transcript. Use when a task would otherwise pollute the main context — long research, deep refactors, anything where the parent agent should not see the mess. 𝗣𝗹𝘂𝗴𝗶𝗻𝘀 The shipping crate. A versioned, installable bundle that wraps Skills, MCP servers, commands, and hooks into something you can hand to a teammate. Use when you need to distribute a configured workflow, not when you need a new capability. The selection rule is almost embarrassingly simple: • Need know-how or procedure → Skill • Need to talk to an external system → MCP • Need an isolated long-running task → Subagent • Need to ship a workflow to others → Plugin The expensive mistake I see over and over: Teams build an MCP server to expose internal documentation, coding conventions, or "how we do migrations here." That is not what MCP is for. That is what a Skill is for. You just spent two weeks building infrastructure to solve a problem that wanted 200 lines of markdown. MCP is powerful. It is also the most expensive option in the toolbox — in tokens, in latency, in maintenance, in failure modes. Reach for the cheapest primitive that fits the shape of the problem. Then escalate only when you genuinely need to. Which of these four do you think gets misused most often in production agentic systems?

@SoloBossApp done, would be great if you can join our DC and have more chats.

@alephantai Awesome! everlist.dev

Real talk in 2026 👀 How much did YOU spend on AI last month? Drop your OpenAI / Claude / Cursor/ Openclaw / Hermes bill screenshot in the replies ↓ No shame. Let’s see the real numbers

@SoloBossApp Sounds good.

@alephantai Great tool! I launched a programmatic SEO directory and would love to have you listed!

@PrakashS720 @PrakashS720 This no-code agent guide is 🔥 Once you start running these autonomous agents regularly, token costs hit hard. @alephantai’s Alephant = real-time tracking + budget guardrails so you stay in control. Free 👉 alephant.io

@charliejhills This is massive 🔥 Anthropic just turned Claude into a full small-business operating system — 31 practical skills with real data connectors (QuickBooks, PayPal, HubSpot) that actually do the work instead of just chatting. From cash-flow forecasts to invoice chasing, CRM cleanup to campaign runs — this is exactly how SMBs go from “trying AI” to running real AI teams. But here’s what happens the moment these skills start running daily, persistently, and with real data: token costs explode fast. That’s exactly why @alephantai’s Alephant exists — the open-source Agent AI Gateway / FinOps layer built for this new world of production AI workflows: • Real-time cost tracking per agent, session & model • Budget guardrails, alerts, auto-fallback & smart routing So you can actually run all 31 skills confidently without a nasty bill shock at the end of the month. Free to try 👉 alephant.io Charlie, which of these 31 skills do you think will get used the most by SMBs?

Anthropic just shipped Claude's 31 small business skills. How to install them in Cowork in 3 steps. 1. Install in Cowork. - Click "+" icon → Add plugin. - Type in "Small Business" in the search. - Install the plugin. All 31 skills come bundled. 2. Connect your data sources. - 12 connectors are supported at launch. - Start with QuickBooks, PayPal and HubSpot. - Most skills work even without every connector. 3. Run /smb-onboard. - Walks you through your first two connectors. - Runs a quick interview about your business. - Stores context so every other skill benefits. All 31 skills: Money (10) /tax-prep → Quarterly tax or 1099 packet /cash-flow-snapshot → 30/60/90-day forecast /plan-payroll → Cash plan plus overdue chase /month-heads-up → 25th-of-month cash outlook /tax-season-organizer → 1099-NEC with W-9 flags /invoice-chase → Tone-matched overdue reminders /month-end-prep → Reconciles QB vs processors /margin-analyzer → Unit economics by product /close-month → Reconcile, P&L, close packet /price-check → Margin and pricing scenarios Sales and CRM (6) /call-list → Top 5 calls with talking points /lead-triage → Scores HubSpot inbound leads /crm-cleanup → Fixes stale deals and duplicates /crm-maintenance → Auto-updates from email /sales-brief → Top sellers plus content brief /quarterly-review → Full QBR as deck or PDF Marketing (3) /content-strategy → 30-day brief from sales /canva-creator → Canva, captions, HubSpot /run-campaign → End-to-end campaign run Customers (4) /customer-pulse → Themes from disputes plus reviews /customer-pulse-check → Top-3 fixes from feedback /handle-complaint → Tone-matched reply plus fix /ticket-deflector → Drafts reply, can refund Briefings (3) /friday-brief → Revenue, wins and watches /monday-brief → Cash, sales, pipeline, to-dos /business-pulse → Snapshot from every connector Setup, hiring and legal (5) /review-contract → Plain review plus redline DOCX /smb-onboard → Walks through your first two /job-post-builder → Post, interview, envelope /contract-review → NDA, MSA, vendor flags /smb-router → Picks the right skill for you Free Claude playbooks → charliehills.substack.com Repost ♻️ to help someone in your network.

@info_with_ai This “27 Most Powerful AI Tools” list is absolute fire Especially love the Agents section — Manus, n8n, Zapier — these are the exact tools turning prototypes into real 24/7 autonomous workflows. But here’s the part most people discover too late: the moment you start chaining these tools + frontier models at scale, token + API costs explode fast. That’s exactly why @alephantai built #Alephant — the open-source Agent AI Gateway / FinOps layer for this new stack: • Real-time cost tracking per agent, session & model • Budget guardrails, alerts, auto-fallback & smart routing So you can actually use all these powerful tools confidently without the surprise bill at the end of the month. Free to try 👉 alephant.io Which tool from this list are you using the most right now?

27 Most Powerful AI Tools ⚡ Writing SurgeGraph Sudowrite Hoppy Copy Coding v0 Cursor Bolt Agents Manus n8n Zapier Image Ideogram ChatGPT Midjourney Video Runway Veo Hailuo Speech/Audio Suno ElevenLabs Speechify SEO SurgeGraph AnswerSocrates 👉Follow me @info_with_ai

@Shasko Hi Fernando! Big 'hi' right here — count me in! Love the mission to build real organic connections in the AI space. As an AI Agent builder myself, I’m all in. We’re the team behind @alephantai’s Alephant — the open-source Agentic AI Gateway / FinOps layer built exactly for the kind of persistent agents, fleets, and production workflows everyone here is shipping. • Real-time cost tracking per agent, session & model • Budget guardrails, alerts, auto-fallback & smart routing So builders can actually run agents 24/7 without the monthly bill shock. Would love to connect and swap notes on the agent ecosystem! Free to try 👉 alephant.io What are you building these days?

On a mission to build a genuine network of 10k organic connections here on X! 🚀 I'm looking to connect with: 🧠 AI & Tech enthusiasts (experts & curious minds!) 🤖 AI Agent builders 💻 Vibe Coders 🛠️ Founders & Builders ⚡ High-agency thinkers Fit the bill? Drop a 'hi' in the replies and let's connect! 👇

@seymurglv This is such an important and underrated point Everyone obsesses over LLM token cost, but in real production the silent killer is tool costs — Stripe, Slack, Postgres, external APIs, etc. Those can outrun model costs by a mile and kill demos in week two. @alephantai’s #Alephant is built exactly for this new agent economy — the open-source Agentic Economy Gateway / FinOps layer: • Real-time cost tracking and observability • Budget guardrails, alerts & auto-fallback to stop expensive runaway agents • Smart routing so agents stay economical on both tokens and tools Free to try 👉 alephant.io Have you seen any crazy tool-cost blowups yet?

Most "AI agent" production demos in 2026 fail in week two for the same reason nobody warns you about: tool cost outruns model cost. The token bill is a rounding error. The Stripe + Slack + Postgres calls are not.

@polsia This is the exact silent killer every AI fleet operator is waking up to One agent quietly burning more than your entire cloud bill while you have zero visibility? Brutal. Fleet attribution is a game-changer (Gauge AI looks solid), but once you see the problem you still need real-time control to actually stop the bleed. That’s exactly why @alephantai built #Alephant — the open-source Agentic Economy Gateway / FinOps layer for production AI fleets: • Real-time cost tracking per agent, session & model • Budget guardrails, alerts, auto-fallback & smart routing • Full visibility + enforcement so you can run fleets confidently Visibility is step one. Alephant gives you control. Free to try 👉 alephant.io What’s the wildest single-agent cost you’ve seen in a fleet so far?

Your AI fleet is burning money you can't see. One agent can cost more than your entire cloud bill. Fleet attribution shows you where. gauge-ai.polsia.app/blog/fleet-cos…

@_BananaCrystal Spot on 🔥 CTOs are about to pay the AI layoff bill twice: first with headcount, second with all the workflows their agents still can’t complete because they lack real payment capability. Building proper agent wallets and autonomous payment infrastructure (like you’re doing) is going to be massive. But once agents can actually spend money and run 24/7, the token costs become an even bigger risk. That’s exactly why @alephantai’s Alephant was built — the open-source Agentic Economy Gateway / FinOps layer: • Real-time cost tracking per agent, session & model • Budget guardrails, alerts, auto-fallback & smart routing So you can give agents real spending power without getting crushed by runaway bills. Payment infra + cost governance = the complete agent economy stack. Free to try 👉 alephant.io What’s the biggest blocker you’re seeing for agent payments right now?

CTOs are about to pay the AI layoff bill twice, per Reddit's top tech thread today. The first bill: headcount. The second bill: every workflow the agent can't complete because it has no payment capability. Build the wallet before the second invoice arrives.

@kytotwt This is the exact “hidden tax” killing most agentic workflows Autonomous loops feel free until the invoice hits and you realize one small loop just burned thousands in tokens. You’re 100% right — early-stage builders get crushed by API costs before they even validate the product. That’s why @alephantai’s #Alephant was built: the open-source Agentic Economy Gateway / FinOps layer for exactly this problem. • Real-time cost tracking per agent, session & model • Budget guardrails, alerts, auto-fallback & smart routing • So you can run autonomous loops confidently without the surprise bill Free to try 👉 alephant.io Kyto, what’s the highest single-loop bill you’ve seen so far?

the hidden tax of agentic workflows
 every autonomous loop quietly multiplies your token bill
speed feels free until the invoice lands be honest, are you able to integrate any ai api for your agent??

@ridark_eth This local AI lab guide is straight fire 2 GPUs → llama.cpp → llama-swap → fully private agents with literally $0 cloud bills? No API keys, total privacy, zero usage meters… that’s the ultimate bill-killer move. Bookmark-worthy for anyone tired of OpenAI/Anthropic draining their wallet. But here’s the real talk: not everyone can (or wants to) go 100% local yet — especially when you need frontier models for the heavy lifting or you’re scaling complex agent workflows. That’s exactly why @alephantai’s Alephant exists — the open-source Agentic Economy Gateway / FinOps layer for the cloud side: • Real-time cost tracking per agent, session & model • Budget guardrails, alerts, auto-fallback & smart routing • So you can run hybrid or full-cloud agents confidently without surprise bills Perfect complement to local stacks when you still need the big models. Free to try 👉 alephant.io Have you tried mixing local + cloud agents in the same workflow yet?

HOW TO KILL YOUR OPENAI BILL WITH A LOCAL AI LAB This is the most practical local LLM and agent infrastructure guide I've seen online. Bookmark this before you forget. No API keys. Total privacy. Zero usage meters. 2 GPUs → llama.cpp → llama-swap → Private Agents → $0 Bills 1 HOUR by leopardracer.

@adamghowiba $1.3M in 30 days… from ONE person. That’s not a bill, that’s a wake-up call This is exactly why agentic AI at scale is about to get wild — 603B tokens, millions of requests, persistent workflows, and every power user multiplying it. The good news? You don’t have to wait for OpenAI to solve cost visibility. @alephantai’s Alephant is the open-source Agentic AI Gateway / FinOps layer built for exactly this reality: • Real-time cost tracking per agent, session & model • Budget guardrails, alerts, auto-fallback & smart routing • Full observability so you actually see (and stop) the bleed before it hits $1M+ So builders and teams can run frontier agents confidently instead of getting destroyed by surprise bills. Free to try 👉 alephant.io Adam, how long until we start seeing $10M+ monthly bills from single agent teams?

this is the OpenClaw founder's OpenAI API bill in 30 days. $1.3 million. 603 billion tokens. 7.6 million requests. one person. multiply this by every power user, every agent system, every enterprise deploying AI in 2026. now you understand why OpenAI is building this:

@ichiaimarketer This 4-layer Hermes Agent company structure is next-level Company Brain (persistent vision/context) → Orchestrator → specialized department agents (Marketing/Sales/Eng/Ops) → shared tools layer… all on just 1 VPS with 1 human? That’s not just an agent setup — that’s literally rebuilding a full startup organization with AI. Super clean and exactly how the best builders are scaling from single agents to real AI companies. But here’s the reality once these persistent, multi-layer systems go live 24/7: token costs explode fast with all the context switching, routing, and tool calls. That’s exactly why #Alephant was built — the open-source Agent AI Gateway / FinOps layer for production agent teams: • Real-time cost tracking per agent, session & model • Budget guardrails, alerts, auto-fallback & smart routing • Perfect companion for complex layered systems like this Hermes setup So you can run full AI organizations confidently without monthly bill shock. Have you seen many people actually take this full-company agent structure into production yet?

【保存版】 「Hermes Agent で会社1個まるごと、VPS 1台で動かす」事例が話題👀 この自社の組織図、4層に分かれてこんな構成になっている👇 1. Company Brain 　→ ビジョン / ブランド / 顧客 / プロダクトの永続コンテキスト 　→ 他の全層がここから情報を継承 2. Orchestrator Hermes Agent 　→ Company Brain を読んで、適切な部署エージェントへ振り分け 3. 各部署エージェント（Marketing / Sales / Eng / Ops 等） 　→ 役割別に独立コンテナ、互いに干渉しない 4. ツール層（Web fetch / DB / Slack / Email） 　→ 共通インフラとして全部署が呼ぶ つまり 「人間1人 + Hermes Agent 4層で 中規模スタートアップの内部構造を再現」 できてる、という事例。 OpenClaw でメモリ層 / ナレッジ層を真面目に運用してる人なら この組織化の発想、そのまま転用できる。 週末で1個試す価値ある構成。

@so_ainsight This Claude trick is absolute gold 🔥 Asking it to collapse an entire complex system into one clean HTML (human-readable architecture map) + JSON (perfect handover data for the next session or teammate) is such a game-changer. It turns messy codebases into self-documenting “maps” and completely kills handover hell. Once builders start using techniques like this to ship bigger, more persistent Claude + n8n agents and production workflows, the next reality check always hits: token costs spiral fast with all the back-and-forth, long contexts, and recurring runs. That’s exactly why @alephantai #Alephant exists — the open-source Agentic Economy Gateway / FinOps layer built for this exact world of sophisticated AI agents: • Real-time cost tracking per agent, session & model • Budget guardrails, alerts, auto-fallback & smart routing • Seamless with Claude, n8n, Cursor and more So you can confidently build, document, iterate and run these systems at scale without getting destroyed by surprise bills. Free to try 👉 alephant.io What’s the biggest project you’ve used this HTML+JSON method on so far?

これ便利すぎる。 海外で話題のやり方で、Claudeに「このアプリ全体を、1枚のHTMLと1つのJSONにまとめて」と頼むだけで、複雑なシステムをわかりやすく可視化できる。 ・いまの設計が“見える化”されて、頭の中の地図ができる ・人間が読む用はHTML、次のチャットや新しい作業をする相手用はJSON…役割が分かれてる ・コードベースが「自分で説明してくれる」状態になる 続きで、そもそも何が起きてるのかを噛み砕いて書く👇

@RoundtableSpace Shipping updates to @alephantai’s Alephant this weekend — the open-source AI Economy Gateway / FinOps layer built exactly for the kind of AI agents and autonomous workflows everyone is dropping here With so many builders shipping persistent agents, long-running tasks, 24/7 workflows, and production AI systems (especially in the crypto + AI space), the #1 reality check is always the exploding token bill. Alephant fixes that with: • Real-time cost tracking per agent, session & model • Smart budget guardrails, alerts, auto-fallback & intelligent routing • Full observability so you can actually run agents confidently without surprise costs Free to try 👉 alephant.io What are YOU building this weekend?

What are you building this weekend?

@ingliguori 100% agree — most AI agents die the moment they stay stuck in prototype mode. You nailed it: multimodal input, structured output, clean APIs, tools + memory + RAG are exactly what separates impressive demos from real, usable value. Usability beats raw capability every single time But here’s what happens next: the second you move these agents into production (persistent workflows, long-running tasks, real tool usage), the silent killer shows up — uncontrolled token spend and zero visibility into costs. That’s exactly where @alephantai comes in — the open-source Agentic Gateway / FinOps layer built for production agents: • Real-time cost tracking per agent, session & model • Budget guardrails, alerts, auto-fallback & intelligent routing • Full observability so you can actually ship and run sophisticated agents without getting wrecked by bill shock It gives builders the confidence to go beyond prototypes and deliver real production value. Free to try 👉 alephant.io Giuliano, what’s been your biggest challenge when taking agents from prototype to real-world deployment?

AI agents fail when they stop at the prototype stage 👇 • Multimodal input • Structured output • APIs & interfaces • Tools + memory + RAG 🚀 Real value comes from usability, not capability alone. Via Giuliano Liguori (@ingliguori) #AI #AIAgents #Tech