The Data Layer for Autonomous AI Agents
AI agents fire hundreds of micro-queries per minute. Legacy warehouses bill each at a 60-second minimum. HatiData delivers sub-second SQL with per-second billing — purpose-built for agentic workloads.
THE PROBLEM
What You're Dealing With
The 60-Second Tax on Micro-Queries
Your agent runs a 200ms lookup. The warehouse bills 60 seconds. At 500 queries/hour, you're paying for 8.3 hours of compute but using 1.7 minutes.
Cold Start Kills Agent Latency
Agents need sub-second responses. Legacy warehouses take 5-30 seconds to resume from idle. Your RAG pipeline stalls while the meter runs.
Unpredictable Costs at Scale
Agent query volume is bursty and unpredictable. Fixed warehouse sizing means you either overpay for idle capacity or throttle your agents.
THE HATIDATA FIX
How HatiData Solves It
200ms Median Query Latency
DuckDB's in-process vectorized engine returns results in milliseconds. No network hop to a separate warehouse cluster. Your agents get answers at memory speed.
Per-Second Billing, No Minimums
200ms of compute costs 200ms. Not 60 seconds. At 500 queries/hour, you save 99.7% on billing overhead alone.
Elastic Scaling for Bursty Workloads
Nodes spin up in milliseconds. Auto-suspend in 5 seconds. Your agents get the compute they need, when they need it — and you stop paying the instant they don't.
Side-by-Side Comparison
| Feature | Legacy Warehouse | HatiData |
|---|---|---|
| Minimum Billing | 60 seconds/query | Per-second (actual usage) |
| Query Latency (p50) | 2-5 seconds | ~200ms |
| Cold Start | 5-30 seconds | Instant (in-process) |
| Cost @ 500 queries/hr | $33.33/hr (60s × 500) | $0.14/hr (actual compute) |
| Auto-Suspend | 60 seconds | 5 seconds |
| Concurrency | Limited per cluster | Auto-scaling multi-node |
CODE EXAMPLES
Drop-In Integration
import psycopg2
# HatiData as the retrieval layer for RAG
conn = psycopg2.connect(
host="hatidata.internal",
port=5439,
dbname="knowledge_base"
)
def retrieve_context(query_embedding, top_k=5):
"""Sub-200ms semantic search over structured data"""
cursor = conn.cursor()
cursor.execute("""
SELECT document_id, chunk_text, metadata
FROM knowledge_chunks
WHERE category = %s
ORDER BY embedding <-> %s::vector
LIMIT %s
""", (category, query_embedding, top_k))
return cursor.fetchall()
# Agent tool: SQL lookup for real-time data
def agent_sql_tool(query: str):
"""Execute arbitrary SQL — billed per-second, not per-minute"""
cursor = conn.cursor()
cursor.execute(query)
return cursor.fetchall()COST CALCULATOR
The Agent Tax: Calculated
$33.33/hr
Legacy Warehouse
$0.14/hr
HatiData
$23900/mo
Projected Monthly Savings
billing overhead eliminated
median agent query latency
more cost-efficient than legacy
“We switched our RAG pipeline from a legacy warehouse to HatiData. Agent query costs dropped from $4,200/mo to $180/mo. Same SQL. Same drivers. Just per-second billing instead of per-minute.”
Stop Paying the 60-Second Tax.
Run the free audit script. See what you're really spending. Switch in 14 days.
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