The convenience of cloud. The benefits of local.
Inference to power workflows, features, and production AI with predictable flat-rate pricing. Vaults for agents to stay in sync and up to date. Sub-agents to offload coding usage. Never go over your AI budget again and improve team efficiency.
AI is great until it hits production.
A workflow, a feature, or a demo can work. Press go at production scale and throughput, plus an error rate that compounds and you blow past the budget, throttle the product, and collapse the experience.
Budgets balloon.
Token meters turn production volume into a bill that scales out of control.
Throughput throttles.
The product slows under real traffic, and the experience the demo promised does not hold.
A 2–5% error rate compounds.
A few bad tool calls stack until the workflow fails and the experience collapses.
Built for Production.
Dedicated capacity holds the budget. The tool-calling runtime holds the error rate. The serving runtime holds the throughput.
The budget
Dedicated capacity
The bill stays flat. Production can run as hard as the machine allows.
Our facility
Dedicated
Hardware dedicated to you
You don't buy, rack, or manage any hardware. Subscribe for a flat rate and get the benefits of local with the convenience of cloud. We own, manage, and host it in our facility, with backup power, redundant network connectivity, and spare hardware on site.
Privacy
Dedicated hardware. Not a shared pool.
Flat rate
One bill. No token meter.
Benefits of local
Hardware reserved for your team, one flat rate. Volume does not move the bill. The only ceiling is the machine itself.
Dedicated capacity
Dedicated capacity starts at $444/mo. We own, manage, and host it in our facility, with backup power, redundant network connectivity, and spare hardware on site.
Hub license
License the hub and run the same Vaults, sub-agents, and API on hardware you own and manage.
Error rate
A small miss does not get to compound.
A 2–5% error rate is how a working agent falls apart in production. Each bad tool call is another retry, and the misses stack until the experience collapses. We host the inference those agents call. The runtime is tuned for tool calling on Apple Silicon, on the same open weights a stock server would run. Gemma 4 26B A4B, 4-bit.
90.2%
Tool call accuracy on a 26B model at 4-bit. Ollama’s best is 87.4%. Parallel tool calls take a 13.5-point lead.
| Test | Midium | Ollama |
|---|---|---|
| ThinkingTool call accuracy | 90.2%↑ 2.8 Ollama thinking↑ 5.2 Ollama no-think | 87.4% |
| No-thinkSame test, thinking off | 89.7%↑ 2.3 Ollama thinking↑ 4.7 Ollama no-think | 85.0% |
| Parallel tool callsThe most difficult tool-calling test | No-think83.0% ↑ 5 Ollama thinking↑ 13.5 Ollama no-think | Thinking78.0% No-think69.5% |
Measured July 23, 2026 · Midium vs Ollama · Gemma 4 26B A4B, matched 4-bit · Apple Silicon, 128 GB · BFCL v4 official checker · 1,000 cases · temp 0.
Methodology: See the proof.
Throughput
The runtime keeps the product moving.
Same Gemma 4 26B A4B measurement: faster decode, and the first token arrives sooner. The serving path is built so that speed holds when the product is under load.
- Decode speed
- +37%
- 123.8 vs 90.1 tok/s
- Sooner to first token
- 2.3×
- 0.122s vs 0.277s
Batching
Requests are batched through the runtime.
Model pooling
Several instances sit behind one API and take work round-robin.
Loop detection
A watchdog detects an endless-loop hallucination, kills the model, and restarts it so throughput keeps moving.
Improve efficiency
Tools For The Team.
Keep agents in sync, up to date, and efficient with vaults and offload usage with free coding sub agents.
Vaults
Persistent, Shared Agent Memory
Vaults are shared and scoped memory for engineers and their coding agents. Cursor, Claude Code, Codex, and your own tools read them. Agents write context back as they work, so a session spends less time reorienting, and each team member's agent stays in sync with what the others are doing.
- Your personal Vault is free in Midium Desktop. Shared Vaults come with dedicated capacity or a Hub license.
- Cloud context is not used for training. Local Vaults stay on the device.
Sub-agents
Maximize Output on Your Plan
48 GB of unified memory is recommended. Midium Desktop exposes local models over MCP to Cursor, Claude Code, and Codex. Move at least 60M tokens a month off the plan you pay now, so the team can do more development or drop a subscription tier without cutting code throughput. Local inference and your personal Vault stay on your machine by default. Connectors only send what you point them at.
Start here
Start on tokens. Scale into Dedicated Capacity
Use our cloud on competitive token-based pricing to start, scale to dedicated capacity.
Download the free Desktop App on any M-series Mac to use local AI APIs, utilize sub-agents with your coding agents, and get a personal Vault.
| Model | Tier | Cloud input / M | Cloud output / M |
|---|---|---|---|
| Gemma 4 E2B | Lite | $0.024 | $0.048 |
| Gemma 4 26B A4B | Balanced | $0.13 | $0.40 |
| Gemma 4 31BDedicated capacity | Balanced | — | — |
| Laguna XS 2.1 | Balanced | $0.06 | $0.12 |
| Qwen3.8 27B | Balanced | $0.094 | $0.388 |
| GLM 5.3 FlashDedicated capacity | Frontier | — | — |
| Inkling SmallDedicated capacity | Frontier | — | — |
| Laguna S 2.1 | Frontier | $0.26 | $0.96 |
| Qwen3.8 Flash NextDedicated capacity | Frontier | — | — |
| BGE-M3 | Embeddings | $0.00 | $0.00 |
Shared models are billed per million tokens. Dedicated capacity is a flat rate, sized by throughput, team, and agent capacity.
