DigitalOcean Marketplace lists an OpenCode 1-Click App that deploys the open-source terminal coding agent onto a Droplet, preconfigured to use DigitalOcean Gradient AI for inference, according to DigitalOcean documentation. The Marketplace listing states OpenCode supports Llama 3.3 70B, Qwen3, DeepSeek, and other models via a single Gradient model access key (DigitalOcean docs). System requirements in the listing show a minimum of 1 GB RAM, 1 vCPU, and 25 GB storage and a recommended configuration of 2 GB RAM, 2 vCPU, and 50 GB storage (DigitalOcean docs). Separate DigitalOcean inference documentation describes using the https://inference.do-ai.run endpoint, creating a MODEL_ACCESS_KEY (format sk-do-...), and exporting it into the shell environment to connect coding agents to Gradient inference (DigitalOcean docs).
What happened
DigitalOcean Marketplace publishes an OpenCode 1-Click App that deploys the open-source terminal coding agent onto a Droplet, preconfigured to use DigitalOcean Gradient AI for inference, per DigitalOcean documentation. The listing specifies OpenCode supports Llama 3.3 70B, Qwen3, DeepSeek, and other models accessible via a single Gradient model access key (DigitalOcean docs). The Marketplace listing lists system requirements as minimum 1 GB RAM, 1 vCPU, 25 GB storage and recommended 2 GB RAM, 2 vCPU, 50 GB storage (DigitalOcean docs).
What happened (inference setup)
DigitalOcean's inference documentation documents a unified inference control plane and an endpoint at https://inference.do-ai.run that coding agents can use as a drop-in proxy (DigitalOcean docs). The docs instruct users to create a model access key (format sk-do-...), export it as MODEL_ACCESS_KEY in the shell profile, and use that key to list models and route requests via the Model Catalog (DigitalOcean docs). The same docs include agent-specific setup notes and an example of connecting claude by writing ~/.claude/settings.json and using the access key mechanism (DigitalOcean docs).
Editorial analysis - technical context
Terminal-first coding agents that keep code and context local while routing inference to hosted models align with an industry pattern where client-side tooling minimizes data surface while off heavy compute to managed inference. For practitioners, that pattern typically reduces on-host resource needs but retains dependency on provider-side model availability, routing, and billing.
Context and significance
For engineers and ML practitioners, a Marketplace 1-Click App simplifies provisioning a reproducible dev environment for agent-driven workflows. Industry observers note that preconfigured images lower the barrier to experimentation but shift operational attention to access keys, model catalog selection, and inference routing, which are the surface areas teams usually monitor when adopting hosted inference.
What to watch
- •Model availability and pricing in DigitalOcean's Model Catalog and any changes to the inference.do-ai.run routing rules.
- •Access key management patterns and how teams handle billing and secrets for MODEL_ACCESS_KEY-style credentials.
- •Updates to the OpenCode listing (versions, included packages) or to supported models in Gradient that affect latency, cost, and capability.
Scoring Rationale #
This is a practical product update that reduces friction for practitioners wanting a terminal-based coding agent with hosted inference. It is notable for developer workflows but not a frontier-model release or major platform shift.
Practice with real Ad Tech data
90 SQL & Python problems · 15 industry datasets
[Active Search Campaigns by BudgetEasy](/problems/sql/active-search-campaigns-by-budget)
[High CPC Clicks & Poor Landing PagesMedium](/problems/sql/high-cpc-clicks-poor-landing-page)
[Campaign ROAS by Attribution ModelHard](/problems/sql/campaign-roas-by-attribution-model)
250 free problems · No credit card