Thanks again for all speakers, helpers, sponsors to make apidays Hong Kong happen together. Let us know your feedback here ( #ApidaysHongKong# #Apidays# #OpenBanking# #OpenAPI# #APIEconomy# #APIEcosystem# #APIGateway# #APIPlatform# #APICommunity# #HKAPICommunity#

Cut your AI API bill by up to 60% — with just one line of code changed. Title: Cheaper Inference - Save up to 60% on AI models URL: Cheaper Inference is an API gateway that aggregates discounted pricing from OpenAI, Anthropic, Google, xAI, AWS Bedrock, and more through a single OpenAI-compatible endpoint. No code changes beyond swapping the base URL and API key. Key Points 💰 Up to 60% savings with price-cap guarantees Live market rates are aggregated across multiple providers, with a hard cap ensuring you never pay more than direct provider pricing. No monthly commitments — start with just $5. Essentially zero-risk to try. 🔌 Zero code changes — just swap the base URL Full OpenAI SDK compatibility means only the base URL changes to ` Supports text and image generation, vision input (up to 10 images, 5MB each), reasoning models with configurable effort, prompt caching, and streaming. 🔒 Enterprise-grade security controls per API key Model allowlists, IP address filtering, rate limits, daily quotas, and monthly budget caps — all configurable per API key. Zero data retention option and audit logging via the History feature make it viable even for compliance-sensitive environments. The practical appeal is clear: cost savings without touching your existing architecture. As AI API costs increasingly factor into product economics, having a procurement layer like this in your stack is worth considering. #LLMCost# #APIGateway#

Arc Builder Spotlight: @AIsaOneHQ AIsa is building a unified API gateway for AI agents, with models, data APIs, search tools, and stablecoin payments in one developer experience. Join @samconnerone and Haonan live on July 29 at 10:00 AM ET for a walkthrough of: → AIsa product and developer flow → x402 payment flow and pay-per-call API access → Nanopayments powered by Circle Gateway → How agents, APIs, and software can transact using USDC

No more hardcoded endpoints. Model routing for API Gateway is in Public Preview! Access Gemini, Claude, and OSS models through 1 single endpoint using the model name as a parameter in your OpenAPI spec. Read the guide to configure yours ➔

What is Quant Network's Overledger? Overledger is Quant’s (@quantnetwork) interoperability platform, designed to connect different blockchains and traditional systems through a common API layer. It tries to make different blockchain networks communicate without forcing developers to build separate integrations for every chain. (1) Overledger acts as a universal API gateway. Developers can use standardized APIs to interact with supported blockchains instead of learning and maintaining separate infrastructure for each network. (2) It works as a translation layer between different blockchain environments. Quant describes Overledger as a “universal translator,” allowing applications to communicate across different DLT architectures through a common interface. (3) This is where Overledger differs from the typical bridge model. Instead of every application relying on a separate bridge between two chains, Overledger provides a common interoperability layer that can connect multiple networks. That can reduce the need for point-to-point integrations, which become increasingly complex as more blockchains enter the ecosystem. (4) Overledger can also power multi-chain applications. Quant calls these mApps, applications designed to operate across multiple distributed ledgers rather than being locked to one blockchain. The platform can also handle transaction signing, asset transfers, and other blockchain interactions through its APIs. (5) But Overledger does not completely eliminate bridges. Quant also provides its own bridge infrastructure for transferring assets between networks, using standardized APIs and smart contracts. Since June 2026, this has also expanded into Quant Fusion, a multi-ledger rollup connecting dozens of blockchains. The bigger idea is that the applications can have interoperability via an infrastructure layer as opposed to establishing the connections all over again.

Quant runs Overledger the way banks buy software, and that's the design @quant_network's Overledger is not a blockchain but an API gateway, passing data and instructions between chains and bank systems without imposing any consensus of its own, per Quant's site. ethereum:0x4a220e6096b25eadb88358cb44068a3248254675 buys access, with paid plans, KYC checks and standard commercial terms handled by Quant Network Ltd directly, per Blockworks' transparency filing. There is no foundation or DAO in the loop, which is unusual for crypto and entirely normal for the enterprise clients Quant sells to. The newer Fusion rollup extends the same model, with approved node operators staking QNT and Quant running the sequencer.

Most engineering teams start customer-facing analytics on Postgres. It works great for running the app, so why not use it to answer your questions? Great question. There is a good reason people pushback. Postgres works for your analytics workloads.... until you outgrow it. If you're using Postgres for your analytics, you may recognize some of the growing pains: 🤨 Aggregates take minutes. A bigger instance buys you a few months. 😠 The analytics tab is your top product complaint. Support tickets mention it, and so do renewals. 🐢 Your analytics replica is just as slow. It runs the same row storage and the same query planner on a different machine. 💩 Materialized views are either stale or expensive to refresh, and someone has added a last_refreshed_at column so users know how old the numbers are. 🫠 Customer-facing pages hit statement timeouts. Your API gateway gives up at 60 seconds and the customer gets a 500. 🐢 Indexes added for a weekly report slow down every write your app makes. 🙅 Your ETL is a pg_dump piped to psql that runs at 3 AM, and nobody wants to be the one to touch it. Recognize 3 or more and you're already at the wall. The fix is to stop wrestling Postgres for analytics. Give analytics its own engine. Postgres stays your system of record and keeps running the app while a scheduled sync copies the analytics tables into an OLAP (like MotherDuck). Our 'Postgres is Full' field guide walks through the whole move with a runnable repo. Check it out:

# Decision Points for Embedding AI Agents in Enterprise Systems # Synchronous vs Asynchronous 🎯 The Hook Is your agent making users stare at a loading spinner, or are they getting notified when the work is done? This choice directly shapes the user experience, the architecture, and the scalability ceiling. A quick chat response and a multi-SaaS cross-platform analysis require fundamentally different execution models. Choose wrong and you get either timeout hell or a chatbot that takes minutes to answer a simple question 🔑 📋 Overview Synchronous execution fits cases where the back-and-forth conversation itself is the source of value. Response time is expected to be under 5 seconds, and real-time interaction is critical — think Slack chatbots, Zendesk live chat, or in-app copilots. Streaming output (token-by-token display) can further smooth the perceived latency. Asynchronous execution fits cases where processing takes tens of seconds to minutes: cross-SaaS investigations, large-scale data aggregation, full-sprint Jira report generation, and similar heavy workloads. These belong in a job queue, with completion notifications sent via Slack or email. Event-driven agents triggered by webhooks or CDC naturally fall into the async category as well 📊 🔍 Decision Points The decision rests on two axes: expected processing time and whether the user is actively waiting. Under 5 seconds → Synchronous is fine Over 10 seconds → Consider asynchronous 5-10 seconds → Evaluate whether streaming can sustain a synchronous feel An additional factor is whether conversational round-trips create value. If the user needs to ask follow-up questions, clarify, or iterate, synchronous is the right choice. For batch processing or scheduled reports, the user is not at the screen — async is the only sensible option. When concurrent request spikes reach thousands, a job queue with backpressure control makes async the safe choice ⚡ 💡 Key Details Hybrid configurations are the most common in production: Sync-start with async escalation: Begin responding synchronously, and if processing exceeds 10 seconds, tell the user "processing in the background" and hand off to a job queue. Notify via Slack or email on completion. Streaming with progress indicators: Stream output synchronously while executing tool calls in parallel behind the scenes. Displaying intermediate results reduces perceived wait time significantly. Consider ServiceNow incident response as a concrete example: first-response answers are returned via synchronous chat immediately, while root cause analysis and cross-incident investigation run as async jobs. Recovery requirements also drive this decision. If you need checkpoint-based resumption after mid-process failures, async with a durable queue is non-negotiable 🔄 ⚖️ Trade-offs Making everything synchronous leads to frequent timeouts. API Gateway 30-second limits get hit, users stare at blank screens, and connection pool exhaustion can bring down the entire system 😩 Making everything asynchronous degrades the chat experience. Routing a simple question through a job queue adds unnecessary latency — nobody wants to wait 3 minutes for a Slack notification answering "what's the status of ticket X." Missing completion notifications is another overlooked trap. If async jobs complete silently, users never come back for the results. The system is perceived as unreliable, and adoption collapses ⚠️ 🛠️ Use Cases Slack chatbot: Knowledge search and FAQ answers run synchronously (under 5 seconds, streamed output). Report generation and data analysis requests run asynchronously (job queue, thread notification on completion). Ideally, the same bot automatically switches based on estimated processing time 📚 Salesforce opportunity analysis: A single opportunity summary is rendered synchronously in the side panel. A quarterly cross-opportunity analysis runs as a background job and updates the dashboard on completion 🛒 CI/CD pipeline integration: Pull request diff summaries are posted as synchronous comments. Full-codebase security scans run as async jobs, with results filed as Jira tickets 🔧 Practical tip: Always set a timeout on synchronous endpoints with an automatic fallback to async. "It will probably finish in 5 seconds" is never a reliable assumption 💪 #AIAgents# #EnterpriseArchitecture#