OpenClaw Cost Optimization: Run 19 Agents for $2/Month

Less than 10 cents a day. That's what it costs me to run 19 always-on AI agents right now.

The same setup, running on default configurations with Claude Opus 4.6 for everything, would run $1,000+ per month. I know because I was heading in that direction. A few configuration changes later and my bill for the entire month sits at $2.

This post walks through the five techniques I used. Each one is a single prompt or config change. None require touching code.

Why This Actually Matters#

My agent fleet is not small. I have a content agent, a product agent, a research agent, a dev agent that builds projects while I sleep, and over a dozen others running continuously. They've built Pomodoro timers, prompt managers, a thumbnail analyzer, a mission control dashboard showing live token usage. All autonomous, all running on a $6/month Hostinger VPS (use code MOE-LUEKER for 10% off).

Running all of that on premium models by default is how you burn through API credits without noticing. The fix is systematic, not random.

If you want the full agent setup before tackling cost optimization, start with how to run 20 OpenClaw agents on a $7/month VPS first, then come back here.

The Five Techniques#

1. Multi-Model Routing via OpenRouter#

This is the core change. Instead of one model handling everything, you route each task type to the cheapest model that can handle it well.

The setup: create an OpenRouter account, generate an API key with a $10/week credit limit, and paste it into your Hostinger Docker environment variables (not the OpenClaw config file directly, since that stores keys in plain text).

Then send OpenClaw a single prompt to configure OpenRouter as the default provider and set up the model routing rules. The model breakdown I use:

• General questions and brainstorming: Minimax 2.5 (very cheap, surprisingly capable)
• Coding and technical work: Deepseek V3 (cheap, strong on code)
• Complex reasoning: Claude Opus 4.6 (expensive, reserve for when it actually matters)
• Writing: Claude Sonnet 4.5
• Everything else: Minimax 2.5

Switching from a premium default to this routing saves 70 to 80% on token costs alone, before any of the other techniques.

You can switch models on the fly with /model sonnet or /model opus when a specific task needs it. The rest of the time, Minimax handles it for a fraction of the price.

2. Smart Heartbeats#

A heartbeat is the interval at which OpenClaw checks in with itself to see if there's anything pending. The default is 30 minutes, and it uses your primary model for each check.

Change two things: set the interval to 55 or 60 minutes, and assign the cheapest available model (Gemini Flash) to handle heartbeats instead of your primary model. The target should be set to "last" so it picks up where it left off.

One heartbeat on Opus 4.6 can cost more than an entire optimized month. Moving heartbeats to Flash and stretching the interval cuts idle costs dramatically.

3. Context Window Limits#

Context accumulates. Every message in a session adds to the token count for every subsequent message. Let a session run long enough and you're paying for the entire conversation history on each prompt.

The fix is a session management rule in your system prompt. I set mine to reset after 15 exchanges, after 30 minutes of continuous conversation, or when switching to a different task domain. On reset, OpenClaw outputs two to three sentences summarizing what it learned, then clears the context. The knowledge is preserved in memory; the token overhead is not.

If you're running longer projects, bump this to 30 exchanges and 100,000 tokens, but understand that every increase has a cost.

4. Memory Compaction#

When a session compacts (OpenClaw's built-in context management), there's a risk of losing important context in the compression. Memory flush before compaction prevents this.

The configuration: enable memory flush before compacting with a soft threshold of 4,000 tokens. OpenClaw writes the important bits to persistent memory before the compaction happens, so nothing critical disappears. You can raise the threshold if you need more working context, but keep it as low as your workflow allows.

This pairs directly with technique 3. Context limits control how often compaction triggers; memory flush controls what survives it.

5. Budget Guardrails#

A hard daily limit on API calls and token spend. Paste in a prompt that sets maximum tokens per day and maximum tool calls per session.

One caveat: set this too low and OpenClaw will stop mid-task when you actually need it to keep going. I set mine higher than I expect to use, just to have a ceiling. The goal is protection against runaway loops, not micromanagement of normal work.

OpenRouter also handles prompt caching automatically, which gives up to 90% discount on repeat content. If you're using a direct Anthropic or OpenAI key instead of OpenRouter, you need to configure caching manually. With OpenRouter, it's already handled.

The Numbers#

Before: default config, premium model, 30-minute heartbeats, no context limits. After: $2 for the month. Less than 10 cents a day. 19 agents running continuously.

The same pattern applies to any AI automation spend. I went through over $18,000 in failed AI systems before understanding that the cost of running these systems is a configuration problem, not a budget problem. The tools are cheap when you configure them correctly.

If you want to go deeper on the agent side, these two videos cover the foundation:

• Part 1, Clawdbot Setup for Beginners: https://youtu.be/pSvsLwGMy4A
• Part 2, 15 First Prompts for OpenClaw: https://youtu.be/5Xo_ni5VT-Y

Watch the full video on YouTube: https://youtu.be/-MtzLiQ9w1c

Some links below may be affiliate links. I only recommend tools I actually use, and it may give you a discount if you use my links.