There’s a dirty secret in the AI agent world: most of the tokens your agent burns are wasted on tasks a much smaller model could handle.
Your orchestrator calls GPT-4o to classify a support ticket. It calls Claude to extract a date from an email. It calls Gemini to rewrite a subject line. Every one of those calls is a $20 sledgehammer driving a $0.02 nail. And you’re paying for every swing.
We built the Neurometric SLM Marketplace to fix this. Today it has 115 task-specific small language models — each under 20B parameters, each fine-tuned for a single job — and an Auto-SLM Creator that can generate new custom models on request. No ML expertise required.
The pricing: you can download any model for free, or we can host it for you for free for up to 100 million tokens per month, then $2/month per model after that for unlimited token usage.
One hundred million tokens. Free. That’s not a typo, and it’s not a bait-and-switch. It’s what happens when you stop paying frontier-model prices for non-frontier tasks.
The agent frameworks getting traction right now — CrewAI, LangGraph, AutoGen, whatever ships next week — all share a common pattern. They decompose complex goals into sequences of discrete tasks. Classify this. Extract that. Summarize this. Route that. Score this. Draft that.
Each of those tasks has a well-defined input, a well-defined output, and a narrow scope. That’s not a job for a 400B-parameter general-purpose model. That’s a job for a 7B-parameter specialist that was trained to do exactly one thing and do it well.
Here’s why this matters when you’re building agents:
1. Latency drops off a cliff. An agent that chains six LLM calls needs each one to be fast. A task-specific SLM running inference on a single function returns in milliseconds, not seconds. Your agent goes from feeling sluggish to feeling instant. When your agent is customer-facing, that’s the difference between adoption and abandonment.
2. Costs become predictable (and tiny). Frontier model pricing is a tax on every agent interaction. At scale — thousands of users, millions of daily task executions — the bill gets ugly fast. With SLMs at 100M free tokens per month and $2/model after that, you can model your costs on a napkin and actually trust the number. You stop worrying about runaway API bills and start thinking about what your agent should do.
3. Reliability goes up. A model fine-tuned to classify support tickets into five categories doesn’t hallucinate a sixth category. It doesn’t go off-script. It doesn’t inject a haiku into your structured output. Task-specific models are more deterministic precisely because they have less surface area for failure. When you’re chaining six calls together in an agent, compounding reliability at each step is everything.
4. You can swap and upgrade without rewiring. The Marketplace organizes models by function — People Management, Accounting & Finance, Engineering & Product, Customer Success, Legal & Compliance, Sales, Marketing, Developer Tools, and more. When a better model appears for a given task, you swap the endpoint. Your agent’s architecture doesn’t change. This is the microservices principle applied to inference.
5. Custom models close the last-mile gap. The 115 models in the Marketplace today cover the most common business tasks. But your agent probably has at least one task that’s specific to your domain — your taxonomy, your workflow, your edge case. That’s what the Auto-SLM Creator is for. Describe the task in plain language, and we build the model. You get a purpose-built SLM without hiring an ML team.
The industry is slowly waking up to something we’ve been saying for a while: the future isn’t one model to rule them all. It’s the right model for the right task at the right cost. Frontier models are extraordinary — and they should be reserved for the tasks that actually require them. Everything else should run on something smaller, faster, and cheaper.
We call it the 25/75 split. Roughly 25% of the tasks in a typical enterprise AI workflow genuinely need a frontier model’s reasoning depth. The other 75% are structured, repetitive, and narrow — classification, extraction, formatting, routing, scoring, summarization. Those are SLM territory.
If you’re building agents today and routing every call through a single frontier API, you’re leaving money on the table, adding latency you don’t need, and introducing failure modes you could eliminate.
The 115 models span 14 categories:
People Management — performance reviews, feedback drafting, job description generation
Accounting & Finance — invoice parsing, expense classification, financial summarization
Engineering & Product — code review triage, bug classification, spec extraction
Customer Success — churn signal detection, health scoring, renewal prep
Customer Support — ticket classification, response drafting, escalation routing
Marketing & Content — headline generation, tone matching, content tagging
Sales & Business Development — lead scoring, email personalization, objection handling
Legal & Compliance — clause extraction, risk flagging, policy matching
Document Intelligence — entity extraction, format conversion, key-value parsing
Human Resources — resume screening, policy Q&A, onboarding task routing
Office & Executive Assistant — meeting summarization, scheduling intent, email triage
Investment & Corporate Finance — earnings extraction, deal screening, comparable analysis
Developer Tools — log parsing, error classification, config validation
Plus 11 general-purpose models under 20B parameters for tasks that don’t fit neatly into a single category.
Every model is API-accessible. OpenAI SDK compatible. No infrastructure to manage. You get a key, you call the endpoint, you get structured output back. That’s it.
Browse the full catalog at marketplace.neurometric.ai. Test any model in the Playground. If you need something that doesn’t exist yet, tell us what you need and the Auto-SLM Creator will build it.
One hundred million tokens. Free. $2/month per model after that.
Your agent has been overpaying for inference. Time to fix that.