We manage inference so you can find what's next
One interface for every agent token stream, delivering frontier-level reasoning at a fraction of the cost without complex setup or management.
Frontier-grade quality on long agentic coding tasks
Live from the calculator below. Run your own numbers
Routing layer target, custom SLA terms on Enterprise agreements
Every request logged, capped, and policy-checked
Frontier reasoning scored on Terminal-Bench v2.1, July 2026. Cost modeled in the calculator below. The outlined chip marks a commitment. Plain numbers are measurements.
Beyond intelligent routing
Serverless managed inference for coding agents. The control layer keeps deciding while the answer is written: open models carry the routine spans; frontier steps in only when the span requires it. When a better model ships, it is tested, benchmarked, and the mixture updates transparently.
Routers dispatch requests. Prizmal governs tokens: the decision happens while the answer is written, not before it.
Mid-task escalation moves the control boundary. The frontier model takes the span that needs it and hands the answer back when it is done.
Every handoff carries the full context. The next model picks up mid-thought and your agent never notices.
Allowlists, spend caps, kill switch, full audit trail. Policies apply before and during streams.
Copilot
Cursor
Windsurf
Claude Code
Cline
Codex
opencode
Pi
Aider
OpenAI
Anthropic
DeepSeek
Qwen
Kimi
GLM
Frontier reasoning without the frontier bill
Your coding workloads execute within the Prizmal zone, the optimal balance of reasoning depth and cost efficiency.
The last few points of score cost more than the first eighty-four. The curve breaks exactly where we sit, and that position is not luck. It is the routing target.
Terminal-Bench v2.1 scores agentic terminal work 0 to 100. A routed Prizmal answer is scored the way any single model is: the suite sees answers, not the mix.
Estimate your savings
A quick look at what Prizmal managed inference could do for your workload
For scale: a full-day developer on agentic coding runs 75M to 300M tokens a day, with exceptions reaching 1B. Knowledge work supported by agents runs 50M to 200M. Profiles from published 2026 agent token math and provider usage data.
- Frontier baseline
- $577,500
- Prizmal total
- $199,125
- Seats ($50 all-in)
- $30,000
- Routing ($0.75 / 1k calls)
- $4,125
- Serverless inference ($0.20/M)
- $165,000
Estimate: seats modeled at an all-in $50 per month, plus the routing fee of $0.75 per 1,000 routed API calls (1 call ≈ 150,000 tokens), serverless inference is passthrough, estimated at a blended $0.20/M (input, cache, output), frontier baseline at a blended $0.70/M, 250 working days.
The $50 modeled here is the actual seat price on Pricing: one flat $50 per seat per month.
Governance
The layer that inspects is the layer that records. Where tokens went, what agents were allowed to touch, and what was refused: one exportable trail.
| Gate | Volume | Checks | Refused | Escalated |
|---|---|---|---|---|
| TOOLS | 18,420 | 214 | 61 | |
| MCP | 9,106 | 88 | 24 | |
| CI/CD | 3,742 | 47 | 12 | |
| SPEND | 12,980 | 9 | 3 | |
| REGION | 44,311 | 0 | 0 |
Same fields in the export: JSON, CSV, or streamed to your SIEM
What the board asks
Ungoverned agents are a CFO and board nightmare: unbounded spend, answers no one can audit, public code bleeding into proprietary repositories. The panels above are technical. The reason for them is not.
Per-key, per-team, per-agent caps with a mid-answer kill switch. The bill cannot outrun the budget by more than one answer.
Every refusal, escalation, and approval in one trail, scoped to a window, filterable by team, key, and agent. Evidence on demand.
Public code entering proprietary code is a diligence failure waiting for an acquirer. Allowlisted models only, CI/CD gates on agent-written changes, and the per-span record shows which model wrote what.
SOC 2 Type II examination in progress. EU AI Act and ISO 42001 posture, region and residency enforced at the routing layer. One export answers the auditor, the underwriter, and the audit committee.
Insurers underwrite what they can see. Boards approve what they can defend. Your auditor gets the export instead of a meeting.
See where every token went: which model wrote which span, at what cost, under which policy.
Every routing decision and every control-plane change, logged and exportable.
Allowlists and blocklists for the tools and MCP servers your agents may touch. Grey areas escalate to a human.
Agent-written changes meet policy before they merge. The pipeline enforces what the meeting decided.
Per-key, per-team, per-agent caps. A kill switch that works mid-answer.
Region and residency enforced at the routing layer. EU AI Act and ISO 42001 posture, SOC 2 in progress.
When the board asks what your agents did last quarter, this layer is the answer: a complete record of every decision an agent was allowed to make, and every one it was not. Insurers underwrite what they can see. Boards approve what they can defend. Directors and officers get the file their duty of care asks for, and your auditor gets an export instead of a meeting.
We run the AI stack. You ship your product.
Testing all models, all the time: neo clouds, providers, tokens per second, quantization, reasoning quality. The mixture and the rules adapt continuously underneath your key.
Thousands of tokens per answer, all day, on every seat. Most are routine. A few carry the whole thing.
Every token billed at frontier, whether it needed the frontier or not. Usage nobody can forecast becomes a bill nobody can defend.
A new model every week; every venue with its own throughput, price curve and failure modes; every quant recipe trading quality per bit differently. Touch one dial and every other dial moves.
Keeping this optimal is not a project; it is a permanent operation: bench re-runs on every release, serving and quantization engineers, routing and governance kept current, the scientists to make it work. Millions a year, and it never ends. With Prizmal, that operation is the product.
| Model | Serving | Quant | Tok/s | Reasoning | Status |
|---|---|---|---|---|---|
| GLM 5.2 | Our env | FP8 | 142 | 82 | Promoted |
| Gemma 4 | Neo cloud A | FP8 | 156 | 79 | Promoted |
| DeepSeek | Provider direct | INT8 | 118 | 81 | Testing |
| Qwen | Neo cloud B | AWQ 4-bit | 131 | 77 | Testing |
| Kimi | Provider direct | FP8 | 104 | 76 | Held |
| New open release | Quarantine | Evaluating | -- | -- | Queued |
Every release, across neo clouds and providers: throughput, quantization recipes, reasoning on Terminal-Bench and our own suites. Winners are promoted into the mixture; governance re-tunes with the fleet. Illustrative slice; the live board is in your dashboard.
Developing your product. One key, one policy, one stream you own; the mixture and the rules stay current underneath, and every change is logged where your auditor can read it.
When a better model ships, it is tested, benchmarked, and the mixture updates transparently. That is managed inference. Cost-risk figures: Gartner, 2026.
How it works
Three steps from your current stack to managed inference
- 01Get an API key
Create your account and generate a key in a couple of minutes
- 02Point to Prizmal
Set one base URL in your coding harness and keep your existing agent, prompts, and tools
- 03We manage each request
We handle execution for every token so you can find, plan, build, and scale what's next
1# ~/.zshrc or ~/.bashrc2export ANTHROPIC_BASE_URL=https://api.prizmal.ai/anthropic3export ANTHROPIC_AUTH_TOKEN=$PRIZMAL_API_KEY4export ANTHROPIC_MODEL=prizmal-autoWorks with the coding harnesses you already run, no rules to define and no agent to rebuild
Sixty seconds, start to routed
One key, one base URL, first routed request. See the full developer setup →
Point your app at api.prizmal.ai
One key, one policy, one stream you own
No self-serve signup yet. We onboard you by hand and you have your key the same day.
In production with a limited group of teams since July 2026.
Your code passes through the layer. Zero retention, never trained on. How we handle data