Hopscotch Labs
Hopscotch is an OpenAI-compatible intelligence layer and LLM gateway: one API and base URL for models from multiple providers. It aims to give developers unified access, cost control, model choice, classified failover, and usage/spend visibility without maintaining separate provider integrations.
At a Glance
- AI application developers
- Startups and individual developers
- Organizations with multiple model providers or production LLM workloads
- Enterprise engineering and platform teams
- +1 more
AI Tools by Hopscotch Labs
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Hopscotch
Unified AI Model Gateway API
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Latest News
Hopscotch AI launched on Product Hunt as a unified API for 500+ AI models, offering model comparison, automatic fallbacks, usage/spend tracking, and provider-rate pricing.
Hopscotch published 'Why we built Hopscotch,' announcing the gateway as live and describing its cost-tier routing, classified failover, and single-bill model.
Hopscotch's official site announced a $7.5M raised figure while presenting the product as an intelligence layer for AI.
Products & Services
Hosted, OpenAI-compatible API gateway. Developers point an existing OpenAI client at Hopscotch, use one key and endpoint, and call provider/model IDs across OpenAI, Anthropic, Google, and other model providers.
The hopscotch/auto route selects a model inside a low, balanced, or high cost tier; named models can be pinned. Classified transient, capacity, malformed-request, and adapter failures determine whether fallback is appropriate.
Dashboard for API keys, request/attempt logs, usage and spend, workspaces, roles, project caps, rate limits, prepaid credits, playground requests, and bring-your-own-provider-key configuration.
Market Position
Hopscotch positions itself as a hosted alternative to maintaining direct integrations with every model provider: one OpenAI-compatible endpoint, provider-qualified model IDs, cost-tier routing, classified failover, and unified usage/billing. Public Product Hunt alternatives identify Eden AI, liteLLM, and OpenRouter Model Fusion; the official positioning emphasizes routing and operational controls rather than only model aggregation.
Leadership
Founders
Kevin Callahan
CEO and co-founder. He led global business development, strategy, and operations at Twitter, then growth and ecosystem partnerships at Coinbase, joining before its IPO. In 2022 he founded Uniblock.
David Liu
Co-founder. He teaches blockchain at the University of Toronto, has had seven-figure exits including ANIFTY, and co-founded Uniblock as CTO, where he worked on routing, adapters, and provider abstraction.
Executive Team
Kevin Callahan
CEO & Co-Founder
Former global business development, strategy, and operations leader at Twitter; later led growth and ecosystem partnerships at Coinbase; founded Uniblock in 2022.
David Liu
Co-Founder
University of Toronto blockchain educator, ANIFTY entrepreneur with seven-figure exits, and Uniblock co-founder/CTO.
Founding Story
The team says it had already spent years solving provider selection, retry/failover, normalization, and routing for production RPC infrastructure at Uniblock. It started Hopscotch after recognizing that teams building with AI models face the same multi-provider integration, rate-limit, capacity, pricing, and fallback problems; Hopscotch applies that gateway approach to models.
Business Model
Revenue Model
Hosted API usage funded by prepaid credits. Customers buy credit and pay the provider's list price for model usage; Hopscotch says it currently adds no platform fee or token markup. Enterprise customers can use invoices, volume commitments, and an MSA instead of card billing.
Pricing Tiers
Buy credits and spend them across models. Pricing is per million input/output tokens and varies by model; the official pricing page lists 37 models and says extra upstreams have their own prices.
Invoice billing, volume commit at list price, MSA/DPA and security-questionnaire support, and a named account contact.
Target Markets
- AI application developers
- Startups and individual developers
- Organizations with multiple model providers or production LLM workloads
- Enterprise engineering and platform teams
- Teams using OpenAI-compatible frameworks and coding agents
- Teams standardizing access to multiple LLM providers
- AI products that need model choice, fallback, and resilience without building provider adapters
- Developers comparing models on their own prompts
- Production applications needing cost controls, spend tracking, and per-key limits
- Individual developers and side projects that want one SDK integration instead of several provider SDKs
- Framework and coding-agent integrations using an OpenAI-compatible provider