All postsHow We Run 9 Specialized AI Agents for Under $200/mo — and Why We Built GridMind

How We Run 9 Specialized AI Agents for Under $200/mo — and Why We Built GridMind

Last month, our 9-agent trading system processed 2.3 million decisions. Our infrastructure bill was $187.

Six months ago, that same workload cost us $4,200/mo and required a full-time engineer just to keep the lights on. This is the story of how we got from there to here, and why the solution turned into a company.

The Problem: AI Agents That Worked, Infrastructure That Didn't

In late 2025, we built GRIDLOCK — a crypto trading platform powered by AI agents. Not a single monolithic model making all the calls, but a team of specialists, each handling one piece of the puzzle:

  1. Market Scanner — continuous monitoring of price feeds, order book depth, and volume anomalies across 40+ trading pairs
  2. Risk Assessor — evaluating position exposure, correlation risk, and drawdown scenarios before any trade
  3. Execution Engine — optimal order routing, slippage estimation, and fill management
  4. Portfolio Optimizer — rebalancing recommendations based on target allocations and drift thresholds
  5. Compliance Checker — enforcing trading limits, wash trade detection, and regulatory constraints
  6. Sentiment Analyzer — processing social feeds, news, and on-chain signals for directional bias
  7. Order Router — selecting venues based on liquidity, fees, and historical fill rates
  8. Position Manager — tracking open positions, managing stop-losses, and coordinating partial exits
  9. P&L Reporter — real-time attribution of returns to strategies, agents, and individual decisions

The agents worked. Each one did exactly what we designed it to do. The problem was everything underneath them.

The Cost Wall

Month one in production, we opened our AWS bill and stared at $4,200.

The breakdown was grim. LLM API costs were the largest line item — every agent was running Claude Sonnet for everything, whether it was a simple price check or a complex risk assessment. GPU instances ran 24/7 for agents that were active maybe 6% of the time. We had no way to attribute costs to individual agents. When the Market Scanner's API calls spiked, we couldn't tell if it was doing useful work or stuck in a retry loop.

The infrastructure overhead was the real killer, though. Each agent ran in its own container. Nine containers, each pulling in the full Python runtime, LangChain, and a pile of dependencies. Memory usage was absurd — 80-150MB per agent at idle, spiking to 400MB+ under load. Cold starts took 12-15 seconds, which meant our scaling was either too slow to catch market moves or too aggressive and burning money.

We were a three-person team. One of us was spending 60% of their time on infrastructure instead of improving the trading logic.

What We Tried (And Why It Didn't Work)

We went through the standard playbook:

LangChain for orchestration. It handled the agent abstractions fine, but the resource footprint was enormous. Each agent loaded the entire framework. Memory usage was non-negotiable. And debugging agent interactions through LangChain's abstraction layers was an exercise in frustration.

Kubernetes for scaling. K8s gave us autoscaling, but the overhead was massive for our scale. The control plane alone cost more than some of our agents. HPA couldn't react fast enough to market conditions — by the time a new pod was healthy, the trading opportunity was gone.

Managed databases and caching. RDS and ElastiCache added reliability but also added $600/mo in baseline costs for services that were mostly idle.

Centralized logging and monitoring. Necessary, but another $200/mo and zero insight into per-agent economics.

After three months, we'd cut costs to about $3,100/mo. Still unacceptable. And we were still spending 40% of engineering time on infrastructure instead of the actual product.

The worst part: we had no cost visibility at the agent level. When the bill came in, we could see total LLM spend and total compute spend, but we couldn't answer basic questions like "how much does the Risk Assessor cost per decision?" or "is the Sentiment Analyzer worth what we're paying for it?"

The Insight

We kept looking for a better orchestration framework, a cheaper hosting provider, a smarter way to configure K8s. Then we realized we were optimizing the wrong layer.

The problem wasn't our agents. The agents were good. The problem was the layer between the agents and the cloud — the runtime, the orchestration, the scaling logic, the model routing. Every existing solution assumed agents were heavyweight Python processes that needed heavyweight infrastructure. But most of our agents spent 94% of their time idle, and when they were active, half of them were doing work that didn't need a frontier model.

We needed a runtime that was:

  • Small enough that running 9 agents didn't require 9 expensive containers
  • Smart enough to route each task to the cheapest model that could handle it
  • Fast enough that cold starts didn't miss market opportunities
  • Observable enough that we could see exactly what each agent cost

Nothing like that existed. So we built it.

NullClaw: 678KB That Changed Everything

We wrote NullClaw in Zig. Not because we're language hipsters, but because Zig gave us exactly what we needed: manual memory control, zero runtime overhead, predictable performance, and a binary that compiles to 678KB.

That's not a typo. The entire agent runtime — YAML parser, task scheduler, model router, tool sandbox, event mesh integration, state management — fits in 678KB. For comparison, a minimal Python container with LangChain starts at 150MB.

Each agent runs in approximately 1MB of RAM. Cold starts are under 2 seconds. We can run all 9 agents on a single $5/mo VPS with room to spare.

The same 9 agents, the same trading logic, the same strategies: $4,200/mo dropped to $187/mo.

Here's where the money went:

Model Routing: The 60% LLM Cost Reduction

The single biggest cost lever was model routing. Before NullClaw, every agent used Sonnet for everything. But most agent tasks don't need Sonnet.

The Market Scanner checks price feeds and classifies volume patterns. That's perception work — Haiku handles it at a fraction of the cost. The P&L Reporter aggregates numbers and formats reports. Also Haiku. The Sentiment Analyzer classifies social media posts. Haiku again.

The Risk Assessor needs to reason about correlated exposures and tail scenarios. That's Sonnet territory. The Portfolio Optimizer runs multi-constraint optimization. Sonnet.

Trade execution decisions — the ones that actually move money — those get Opus. Maximum capability for maximum stakes.

NullClaw's model routing is declared in the team YAML:

agents:
  - name: market-scanner
    model: haiku         # Perception: fast, cheap
  - name: risk-assessor
    model: sonnet        # Reasoning: balanced
  - name: execution-engine
    model: opus          # Critical: highest capability
    approval_required: true

The runtime enforces these boundaries. A Haiku agent can't accidentally escalate to Opus (unless you configure an escalation path for error recovery). This alone cut our LLM costs by 60%.

Cost Attribution: Every Decision Has a Price Tag

NullClaw tracks every agent action with full cost attribution:

  • Model cost — which model processed the request, input/output tokens, price per token
  • Compute cost — CPU time, memory allocation, duration
  • Tool costs — external API calls, database queries, network I/O

This is tracked per agent, per task, per session. For the first time, we could answer questions like:

  • The Risk Assessor costs $0.003 per evaluation. It runs 8,400 evaluations/month. Total: $25.20.
  • The Sentiment Analyzer costs $0.0004 per classification. It processes 180,000 posts/month. Total: $72.00.
  • The Execution Engine costs $0.02 per trade decision, but it only makes 340 decisions/month. Total: $6.80.

When you can see these numbers, optimization becomes obvious. We discovered our Compliance Checker was re-evaluating the same positions repeatedly due to a caching bug. Fixing it cut its monthly cost from $18 to $4.

Before and After

Category Before (LangChain + K8s) After (NullClaw)
LLM API costs $2,800/mo $112/mo
Compute (containers/VMs) $680/mo $15/mo
Managed databases $420/mo $35/mo
Monitoring/logging $200/mo $15/mo
Load balancer/networking $100/mo $10/mo
Total $4,200/mo $187/mo
Engineering time on infra 40% ~5%
Cold start time 12-15s <2s
RAM per agent 80-150MB ~1MB
Cost attribution None Per-agent, per-task

From Internal Tool to Product

We showed NullClaw to a few other teams running agent-based systems. The reaction was consistent: "Can we use this?"

A YC-backed startup running a 6-agent customer support system was paying $3,800/mo. They ported to NullClaw in a weekend and dropped to $290/mo. A fintech team with 12 agents for fraud detection cut from $8,500/mo to $640/mo.

The pattern was the same every time. Smart people building useful agents, drowning in infrastructure costs, with zero visibility into where the money was going.

That's when we decided to build GridMind — a managed platform on top of NullClaw. You define your agent team in YAML, deploy it, and GridMind handles scaling, monitoring, cost tracking, and optimization. NullClaw stays open-source (MIT for the core runtime). GridMind adds the fleet management, dashboards, and auto-scaling that teams need in production.

What a Team Config Looks Like

Here's a simplified version of our GRIDLOCK trading team:

team:
  name: gridlock-trading
  version: "2.4"

agents:
  - name: market-scanner
    role: "Monitor price feeds and detect volume anomalies"
    model: haiku
    tools: [price_feed, order_book, volume_analyzer]

  - name: sentiment-analyzer
    role: "Classify social and news signals for directional bias"
    model: haiku
    tools: [social_feed, news_api, sentiment_classifier]

  - name: risk-assessor
    role: "Evaluate position exposure and correlation risk"
    model: sonnet
    tools: [portfolio_snapshot, correlation_matrix, var_calculator]
    depends_on: [market-scanner, sentiment-analyzer]

  - name: portfolio-optimizer
    role: "Generate rebalancing recommendations"
    model: sonnet
    tools: [allocation_engine, drift_calculator]
    depends_on: [risk-assessor]

  - name: compliance-checker
    role: "Enforce trading limits and regulatory constraints"
    model: haiku
    tools: [rule_engine, position_limits]
    depends_on: [portfolio-optimizer]

  - name: order-router
    role: "Select optimal execution venues"
    model: haiku
    tools: [venue_selector, fee_calculator, liquidity_check]
    depends_on: [compliance-checker]

  - name: execution-engine
    role: "Execute trades with optimal fill management"
    model: opus
    tools: [order_api, fill_monitor, slippage_tracker]
    depends_on: [order-router]
    approval_required: true

  - name: position-manager
    role: "Track positions and manage stop-losses"
    model: sonnet
    tools: [position_tracker, stop_loss_manager]
    depends_on: [execution-engine]

  - name: pnl-reporter
    role: "Real-time P&L attribution by strategy and agent"
    model: haiku
    tools: [pnl_calculator, report_generator]
    depends_on: [position-manager]

routing:
  default_model: haiku
  escalation_model: opus

scaling:
  min_instances: 1
  max_instances: 5
  scale_on: "queue_depth > 10"
  idle_timeout: 5m

Nine agents. One YAML file. $187/mo.

What's Next

We're heads-down on GridMind. The managed platform launches in Q2 2026 with:

  • Deploy any agent team from a YAML config
  • Auto-scaling that reacts to actual workload, not arbitrary thresholds
  • Per-agent cost dashboards with optimization recommendations
  • Framework connectors for teams that want to keep their LangChain or CrewAI agents but get NullClaw's runtime economics
  • SOC 2 and CMMC compliance for regulated industries

NullClaw itself is MIT-licensed and always will be. If you want to run it yourself, you can. If you want someone else to deal with the infrastructure, that's what GridMind is for.

Try It

NullClaw is available now:

curl -fsSL https://get.nullclaw.dev | sh
nullclaw run your-team.yaml

Define your agents in YAML, run them locally, and see what your infrastructure costs could look like.

For managed hosting, join the waitlist at gridmindai.dev. We're onboarding teams weekly.

If you're running AI agents in production and your infrastructure bill makes you wince every month, we built this for you. The agents aren't the expensive part. The layer underneath them is. And that layer doesn't need to cost what you're paying.

Zack is a co-founder of Assertive Mind, the company behind GridMind and NullClaw. Previously built infrastructure at scale for defense and fintech. Reach out at zack@assertivemind.com or find us on Discord.