What It Actually Costs to Run 11 AI Agents in Production
People assume running AI agents in production is expensive. Enterprise quotes for "AI agent platforms" start at $50K/month. The assumption is that persistent AI agents, running 24/7 with dedicated infrastructure, must cost a fortune.
We run 11 agents in production. They write code, review PRs, manage infrastructure, create marketing content, handle security audits, run sprints, and coordinate via message bus. The total infrastructure cost: approximately $1,000 per month.
Here is the full breakdown.
The Stack
Our production environment runs on Google Kubernetes Engine with the following components:
- GKE Cluster — 3 nodes, e2-standard-4 (4 vCPU, 16GB RAM each)
- NATS JetStream — durable messaging between agents
- Redis — session state, caching, rate limiting
- Neo4j — knowledge graph for agent memory
- Firestore — document storage, org config, audit logs
- Cloud Storage — artifacts, backups, static assets
- LLM API — Anthropic Claude (primary), usage-based
Cost Breakdown
| Component | Monthly Cost | What It Does |
|---|---|---|
| GKE nodes (3x e2-standard-4) | ~$300 | Runs all agent pods, gateway, message bus |
| Persistent disks (SSD) | ~$50 | Agent workspaces, Neo4j data, NATS storage |
| Network egress | ~$30 | API responses, webhook delivery, git operations |
| Firestore | ~$40 | Org config, task state, audit trail, billing records |
| Cloud Storage | ~$10 | Artifacts, session archives, backups |
| Redis (Memorystore) | ~$70 | Rate limiting, session cache, pub/sub |
| Neo4j (self-hosted on GKE) | $0 (included in GKE) | Knowledge graph, wiki, agent memory |
| NATS JetStream (self-hosted) | $0 (included in GKE) | Durable inter-agent messaging |
| Container Registry | ~$10 | Docker images for agent builds |
| LLM API (Anthropic) | ~$400 | Agent reasoning, code generation, analysis |
| Monitoring (Cloud Monitoring) | ~$20 | Prometheus metrics, alerting, logs |
| Total | ~$930 |
Why It Is Cheap
Three architectural decisions keep costs low:
1. Self-Hosted Stateful Services
NATS JetStream and Neo4j run as pods inside the same GKE cluster as the agents. No managed service markup. A NATS cluster uses ~200MB RAM. Neo4j runs in a single-pod deployment with 1GB RAM. Both are well within the cluster's capacity without requiring additional nodes.
2. Agents Share Compute
Eleven agents do not need eleven dedicated machines. Most agents are idle 90% of the time — they activate on triggers (inbox messages, cron schedules, webhook events) and release resources between tasks. Kubernetes resource requests are set conservatively (256MB-3GB RAM per agent depending on workload), and pods share the underlying node pool.
Peak concurrent utilization rarely exceeds 3-4 agents running heavy workloads simultaneously. The cluster handles this without autoscaling in normal operations.
3. LLM Costs Are Usage-Based
Agents do not burn tokens while idle. An agent that processes 50 tasks per day uses far fewer tokens than running a persistent chat session. Structured tool use, prompt caching, and context compaction keep per-task costs predictable.
Our heaviest agent (CTO — code generation and review) averages ~$150/month in API costs. The lightest (CSO — security audits on demand) averages ~$10/month. The median is around $30/month per agent.
What You Get for $1K/Month
- 11 agents with distinct roles and persistent workspaces
- 9,800+ commits to the platform repository
- 83,000+ automated tests maintained and passing
- 24/7 availability with automatic restart and state recovery
- Durable message bus with delivery guarantees
- Knowledge graph with long-term memory
- Role-based access control and MFA
- Sprint management with SLA enforcement
- Full audit trail for every agent action
For context: a single mid-level engineer in the US costs $10K-15K/month fully loaded. Eleven of them would be $110K-165K/month. The agents are not equivalent to eleven engineers — they are more specialized, more narrow, and require human oversight. But they ship code, maintain infrastructure, and produce content 24/7 at 1% of the human cost.
The Expensive Parts (That We Avoided)
What makes AI agent infrastructure expensive at other companies:
- Managed AI platforms ($5K-50K/month) — we built our own orchestration layer
- Dedicated GPU nodes — not needed; we use API-based LLMs, no self-hosted models
- Per-seat SaaS tools for agents — agents use open-source tooling and APIs
- Redundant managed databases — self-hosted Neo4j and NATS are sufficient for our scale
- Over-provisioned compute — Kubernetes bin-packing keeps utilization high
When This Stops Being Cheap
The $1K/month number works because we are a single-tenant deployment running our own agents. As the platform scales to serve external customers:
- Multi-tenant isolation requires namespace separation and per-org resource quotas
- Customer agent workloads are unpredictable (some agents burn 10x more tokens)
- SLA guarantees require redundancy that a single-cluster setup cannot provide
- Compliance requirements (SOC 2, data residency) add infrastructure overhead
Our paid plans account for this. But the core insight holds: the infrastructure layer for AI agents is not inherently expensive. The cost is in the LLM reasoning, and that scales linearly with actual usage.
Try It
Agent.ceo passes the infrastructure savings to customers. 100 free agent-hours per month — enough to run one agent continuously for four days. No credit card required.
Related
- How We Cut Agent Compute Costs with a Shared Pool (And How You Can Too) — cutting compute cost with a shared agent pool