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OpenScale vs Boundless
OpenScale and Boundless both sell DeepSeek V4.1 Flash with 1M context. OpenScale charges far less, and Boundless adds a wider catalog and reserved capacity.
By The Subconscious Team · Updated
OpenScale vs Boundless: key differences
OpenScale, a Y Combinator-backed API out of Singapore, sells DeepSeek V4.1 Flash at $0.06 in, $0.003 cached and $0.24 out per million tokens under a 50% promotion, at 200+ tokens per second. A fast version runs about twice as fast for $0.28 in and $1.10 out. Boundless charges $0.20 in, $0.01 cached and $1.00 out for DeepSeek V4.1 Flash, which Vercel AI Gateway measures at 121 tokens per second, and $0.24 in and $0.96 out for the 1017 version at 342. Both work with OpenAI and Anthropic clients and offer zero data retention, though OpenScale keeps Responses API state for 30 days unless you switch it off.
Pick OpenScale for the lowest price on cache-heavy coding agent traffic, keeping in mind the headline rates are promotional. Pick Boundless for models beyond DeepSeek, US or Europe processing, or a reserved deployment with uptime and latency targets, which OpenScale does not offer.
What OpenScale and Boundless do
OpenScale
OpenScale is a Y Combinator-backed inference API built by LatentSpace and run out of Singapore. It sells open models behind one endpoint that speaks Chat Completions, the OpenAI Responses API and Anthropic Messages, so Claude Code, Codex, OpenCode, Cline, GitHub Copilot, Zed and other coding agents can point at it with a base URL and key. Its public catalog today is DeepSeek V4.1 Flash in two versions, standard and fast, both with a 1M token context window and up to 384K output tokens.
Example models: DeepSeek V4.1 Flash
Full OpenScale profileBoundless
Boundless launched in September 2026 as an inference partner for AI-native startups. It sells two things. The Model API serves pre-tuned models on shared capacity, billed per token from prepaid credits with a budget per API key. Tailored inference reserves capacity for one customer, and Boundless engineers pick the model on the customer’s own evals, tune precision, batching, caching, speculative decoding and parallelism, then run the deployment against uptime, throughput and latency targets written into the contract. A free two-week evaluation tests those targets on real traffic against the customer’s current provider first.
Example models: GLM-5.3, DeepSeek V4.1 Flash, Kimi K3
Full Boundless profileShould you choose OpenScale or Boundless?
OpenScale vs Boundless at a glance
| Attribute | OpenScale | Boundless |
|---|---|---|
| Model access | Open weights | Open weights, plus closed GPT and Claude |
| Flagship models | DeepSeek V4.1 Flash | GLM-5.3, DeepSeek V4.1 Flash, Kimi K3 |
| Speed | 200+ tok/s; fast tier ~2x | 342 tok/s on DeepSeek V4.1 Flash 1017 (Vercel) |
| Price | $0.06 in, $0.24 out (promo, DeepSeek V4.1 Flash) | $0.04–$2.30 in, $0.21–$11.40 out per 1M (open) |
| Customization | N/A | Serving-stack tuning, no fine-tuning |
| Deployment | Serverless API | Serverless API, reserved tailored capacity |
| Long context | 1M context | 1M context on most models |
Frequently asked questions
What is the difference between OpenScale and Boundless?
OpenScale and Boundless both sell DeepSeek V4.1 Flash with 1M context. OpenScale charges far less, and Boundless adds a wider catalog and reserved capacity.
When should I choose OpenScale over Boundless?
$0.003 cached input on DeepSeek V4.1 Flash; Setup guides for a dozen coding agents; Prepaid credits with no minimums.
When should I choose Boundless over OpenScale?
A catalog beyond DeepSeek; Processing in the US or Europe; Reserved capacity with contract targets.
Is OpenScale or Boundless cheaper?
OpenScale: $0.06 in, $0.24 out (promo, DeepSeek V4.1 Flash). Boundless: $0.04–$2.30 in, $0.21–$11.40 out per 1M (open). The cheaper choice depends on the model and workload.
Which has more context, OpenScale or Boundless?
OpenScale: 1M context. Boundless: 1M context on most models.
Related comparisons
Running long-horizon agents?
If your agents run past 200K tokens, compare both against Subconscious. Our inference stack treats a long-horizon trace as the primary workload, so speed, cost, and accuracy hold up deep into the trace.