The Data Layer for a Telegram Trading Bot or Terminal SaaS (2026)

Users judge a Solana trading bot by its edge features — rug checks at buy time, wallet PnL cards, smart-money alerts — not by its swap routing. This is the architecture guide for teams building a Telegram bot or web terminal: which intelligence features users actually expect in 2026, the endpoint that powers each one, the quota math per active user, what you may cache, and the point where Enterprise white-labeling becomes the right call.

MadeOnSol·July 5, 2026· 5 min read

The Data Layer for a Telegram Trading Bot or Terminal SaaS (2026)

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import { MadeOnSol } from "madeonsol";

// Get a free API key at madeonsol.com/pricing
const client = new MadeOnSol({ apiKey: "msk_your_key" });

const { trades } = await client.kol.feed({ limit: 10 });
const { alerts } = await client.deployer.alerts({ limit: 5 });
const { tools } = await client.tools.search({ q: "trading" });

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Build a Solana Rug-Check Telegram Bot in 10 Minutes (Free API Key)

Paste a mint into Telegram, get a scored rug-risk report back in seconds. This tutorial walks the open-source rug-check-telegram-bot starter: zero dependencies, ~150 lines of Node, a free MadeOnSol API key, and a transparent 0–100 score where every point is attributed to a named factor — mint authority, liquidity depth, LP burn, bundled launch, deployer track record.

5 minJuly 5, 2026

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The feature set users expect in 2026, endpoint by endpoint

Rug check at buy time. The table-stakes feature. POST /tokens/batch/risk scores up to 50 mints in one ~200ms call that counts as a single request — which is the difference between "affordable at every buy" and "quota bleed." The 0–100 score comes with named factors (mint/freeze authority, liquidity thinness, LP burn, Token-2022 transfer fee, bundled launch, deployer bond rate, blacklist), so your bot can show reasons, not just a scary number. Users forgive a wrong score with visible reasoning far more readily than an opaque one.

Insider exposure. The question sophisticated users ask after the rug score: who's still holding? The bundle endpoint returns which same-slot bundle wallets bought at launch and — the headline number — what percentage of supply they still hold. "Launch bundle fully exited" and "bundle holds 18% of supply" are different trades.

Wallet PnL cards. The most-shared surface in any trading bot — screenshots of PnL cards are free marketing. GET /wallet/{address}/pnl does honest FIFO cost-basis accounting: realized plus unrealized SOL, profit factor, drawdown, hold-time stats, a daily curve, and per-position breakdowns. Works for any wallet, not just your users', which enables the "scan any wallet" feature that drives viral usage. (This plus rug scoring is precisely the pair most competitor data vendors don't offer.)

Smart-money and deployer alerts. The retention features — reasons to open your bot when not trading. The KOL feed streams trades from 1,000+ labeled wallets with win rates attached; deployer alerts fire when a wallet with a real bonding track record launches again. Both are available as polling (fine for a digest feature), WebSocket channels (kol:trades, deployer:alerts — real-time), or webhooks with HMAC signatures — usually the right shape for a bot backend, since delivery lands on your server and fans out to your users without you holding a socket open.

The quota math

A worked example for a bot with 1,000 active users, assuming each averages 6 buys/day (each batch-scored), 2 PnL card views, and shared alert feeds:

Rug checks: buys arrive continuously, but batching per-block or per-second keeps this around 2–4k calls/day

PnL cards: ~2k calls/day (cache hits on the same wallet within the refresh window are free)

Alert feeds: shared, not per-user — one webhook subscription or one WS session serves everyone

That lands comfortably inside Ultra's 100k/day with a 10× growth margin — roughly $0.15 per user per month in data cost against subscriptions bots typically price at $20–50/month or 1% per trade. Pro's 10k/day fits the same architecture up to a few hundred users. Prototype on the free tier (200/day — enough for a working demo, the same code path throughout).

Two ToS boundaries to design around from day one: operational caching is fine, but accumulating responses into your own historical database is not — and your users seeing scores in your UI is exactly what the standard plans permit, while exposing raw data downstream is not. The full map of that line is in the white-label and redistribution guide.

Architecture that holds up

The pattern that works: your backend is the only API consumer — one key, server-side — and your users consume your product. A thin enrichment service subscribes to webhooks/WS, maintains a short-lived cache keyed by mint, and answers your bot handlers locally. Score-at-alert-time (batch risk against every new deploy alert) turns two features into one pipeline. Keep execution and intelligence as separate services with separate failure modes — a data hiccup should degrade badges, never block a sell.

For a working reference of the free-key-first pattern, the rug-check Telegram bot tutorial is a ~150-line version of the enrichment half, and the copy-trade starter shows tier-adaptive polling vs WebSocket.

When you've outgrown self-serve

Three signals it's time for the Enterprise conversation: you need more than 100k calls/day; you want the intelligence under your own brand with no attribution; or partners want data from you (redistribution). That's an annual contract covering white-label, redistribution rights, extended caching, dedicated limits, an SLA, and custom endpoints — the details, and the build-vs-buy math that usually precedes it, are in the builder's guide and the cost comparison.

The Data Layer for a Telegram Trading Bot or Terminal SaaS (2026)

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The feature set users expect in 2026, endpoint by endpoint

The quota math

Architecture that holds up

When you've outgrown self-serve