Exosphere – Platform for async/batch AI agents

Hacker News

Hey HN, We built Exosphere (exosphere.host) – a platform to orchestrate and run batch AI agents on large data with connectors, autoscaling, and affordable inference (up to 75% cheaper). Think of it as a control plane for async AI workloads. Why we built it: Running background AI workflows (like summarising 1M support chats or processing 10K PDFs) is messy – you need queueing, scaling, model hosting, cost control, and integration with your systems. Most infra today is optimised for chat apps, not bulk tasks or pipelines. This is going to become messier with multistep AI agents/workflows coming in. What Exosphere does: - Supports batch AI agents with parallelism, retries, and memory - Integrates with tools like S3, Notion, GCS, Pinecone etc - Works with open-source models like DeepSeek, LLaMA, and Claude via API - Has a soon-to-be open-source orchestrator called Orbit (built from scratch) - Cost-optimised infra tuned for large data inference and delayed inference Easy onboarding, no GPU setup required Example use cases: - Classify or extract info from 100K PDFs - Run retrieval-based QA across millions of records - Summarize and route large volumes of tickets or feedback - Batch label images or text for finetuning We’d love feedback from this community – thoughts on dev experience, connectors to add, model support, or features you'd want in the agent platform. You can try it out here or just reply here if you want some free credits for trying open-source models in batch. Thanks! – Nivedit (ex-Azure OpenAI) and the team

Visit product View on Hacker NewsMay 21, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, claude · Missing: mac, macos, cursor

99%99% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: supports, para · Missing: reddit linkedin, podcasting, created

91%91% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: llama, pipe, io · Missing: https docs, excited, just released

59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: apps, para · Missing: mobile apps, ios, personal

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host, soon · Missing: plus, intuitive, reviews

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

28%28% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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