diff --git a/backend/routers/ingest.py b/backend/routers/ingest.py
index a665731..034ed7d 100644
--- a/backend/routers/ingest.py
+++ b/backend/routers/ingest.py
@@ -2,9 +2,15 @@ from datetime import datetime, timedelta
 from fastapi import APIRouter, Query, HTTPException
 from pydantic import BaseModel
 from services import s1_client
+from services.async_cache import async_ttl_cache, cache_stats, cache_clear
 
 router = APIRouter()
 
+# Dashboard endpoints can be expensive on busy tenants. Cache results in-process
+# for a short TTL so reloads and parallel widgets are instant. Pass ?nocache=1
+# to bypass for a forced refresh.
+_DASHBOARD_TTL_SECONDS = 300
+
 
 def _date_range(days: int) -> tuple[str, str]:
     now = datetime.utcnow()
@@ -22,41 +28,65 @@ def _date_range_hours(hours: int) -> tuple[str, str]:
     )
 
 
+@async_ttl_cache(ttl_seconds=_DASHBOARD_TTL_SECONDS)
+async def _top_sources_cached(hours: int) -> dict:
+    """Cache key: hours only. days is normalised to hours upstream."""
+    from_dt, to_dt = _date_range_hours(hours)
+    query = "| group events=count() by dataSource.name | sort -events | limit 25"
+    result = await s1_client.run_powerquery(query, from_dt, to_dt)
+    return {"data": result.get("events", [])}
+
+
 @router.get("/top-sources")
 async def get_top_sources(
     days: int = Query(None, ge=1, le=90),
-    hours: int = Query(None, ge=1, le=24),
+    hours: int = Query(None, ge=1, le=720),
+    nocache: bool = Query(False, description="Bypass dashboard cache"),
 ):
-    """Top log sources by event count over the given period."""
-    if hours is not None:
-        from_dt, to_dt = _date_range_hours(hours)
-        period_label = f"{hours}h"
+    """Top log sources by event count.
+
+    Note: SDL returns 'internal Scalyr error' when this query uses day-scale
+    timestamps on busy tenants, but the same window expressed in hours runs
+    fine. We normalise days -> hours internally for stability.
+    """
+    if hours is None and days is None:
+        days = 7
+    if hours is None:
+        hours = days * 24
+        period_label = f"{days}d"
     else:
-        from_dt, to_dt = _date_range(days or 7)
-        period_label = f"{days or 7}d"
-    query = "| group events=count() by dataSource.name | sort -events | limit 25"
+        period_label = f"{hours}h"
     try:
-        result = await s1_client.run_powerquery(query, from_dt, to_dt)
+        cached = await _top_sources_cached(hours, nocache=nocache)
     except Exception as e:
         raise HTTPException(502, f"PowerQuery error: {e}")
-    return {"period": period_label, "data": result.get("events", [])}
+    return {"period": period_label, "data": cached["data"]}
+
+
+@async_ttl_cache(ttl_seconds=_DASHBOARD_TTL_SECONDS)
+async def _by_event_type_cached(days: int) -> dict:
+    # Same days->hours normalisation as top-sources for tenant stability.
+    from_dt, to_dt = _date_range_hours(days * 24)
+    query = "| group events=count() by dataSource.name, event.type | sort -events | limit 100"
+    result = await s1_client.run_powerquery(query, from_dt, to_dt)
+    return {"data": result.get("events", [])}
 
 
 @router.get("/by-event-type")
-async def get_by_event_type(days: int = Query(7, ge=1, le=90)):
+async def get_by_event_type(
+    days: int = Query(7, ge=1, le=90),
+    nocache: bool = Query(False),
+):
     """Event counts grouped by source and event type."""
-    from_dt, to_dt = _date_range(days)
-    query = "| group events=count() by dataSource.name, event.type | sort -events | limit 100"
     try:
-        result = await s1_client.run_powerquery(query, from_dt, to_dt)
+        cached = await _by_event_type_cached(days, nocache=nocache)
     except Exception as e:
         raise HTTPException(502, f"PowerQuery error: {e}")
-    return {"period_days": days, "data": result.get("events", [])}
+    return {"period_days": days, "data": cached["data"]}
 
 
-@router.get("/daily-volume")
-async def get_daily_volume(days: int = Query(5, ge=1, le=7)):
-    """Total event count per day — queries run in parallel."""
+@async_ttl_cache(ttl_seconds=_DASHBOARD_TTL_SECONDS)
+async def _daily_volume_cached(days: int) -> list:
     import asyncio
 
     now = datetime.utcnow()
@@ -78,6 +108,27 @@ async def get_daily_volume(days: int = Query(5, ge=1, le=7)):
     return list(reversed(results))
 
 
+@router.get("/daily-volume")
+async def get_daily_volume(
+    days: int = Query(5, ge=1, le=7),
+    nocache: bool = Query(False),
+):
+    """Total event count per day — queries run in parallel."""
+    return await _daily_volume_cached(days, nocache=nocache)
+
+
+@router.get("/cache-stats")
+def ingest_cache_stats():
+    """Inspect dashboard cache (entry count + TTL remaining per key)."""
+    return cache_stats()
+
+
+@router.delete("/cache")
+def ingest_cache_clear():
+    """Forcefully wipe the dashboard cache (next call refetches from SDL)."""
+    return {"cleared": cache_clear()}
+
+
 class FilterRule(BaseModel):
     source: str = ""
     event_type: str = ""
diff --git a/backend/services/async_cache.py b/backend/services/async_cache.py
new file mode 100644
index 0000000..b019067
--- /dev/null
+++ b/backend/services/async_cache.py
@@ -0,0 +1,84 @@
+"""Tiny in-process async TTL cache for backend endpoints.
+
+Two-fold benefit:
+  * Identical concurrent calls share one upstream PowerQuery (single-flight).
+  * Repeat reads within TTL return instantly (no SDL round-trip).
+
+Designed for read-only dashboard endpoints. Keep it stdlib-only so it adds
+no dependency. Caches live until the process restarts.
+
+Usage:
+    @async_ttl_cache(ttl_seconds=300)
+    async def get_top_sources(...): ...
+"""
+
+from __future__ import annotations
+import asyncio
+import functools
+import time
+from typing import Any, Awaitable, Callable, Tuple
+
+
+# Maps cache-key -> (expires_at, value)
+_STORE: dict[Tuple[str, Tuple[Any, ...], Tuple[Tuple[str, Any], ...]], Tuple[float, Any]] = {}
+# Maps cache-key -> asyncio.Lock for single-flight
+_LOCKS: dict[Tuple[str, Tuple[Any, ...], Tuple[Tuple[str, Any], ...]], asyncio.Lock] = {}
+
+
+def _make_key(name: str, args: tuple, kwargs: dict) -> Tuple[str, Tuple[Any, ...], Tuple[Tuple[str, Any], ...]]:
+    # Skip the special "nocache" kwarg so it doesn't fragment the cache.
+    kw = tuple(sorted((k, v) for k, v in kwargs.items() if k != "nocache"))
+    return (name, args, kw)
+
+
+def async_ttl_cache(ttl_seconds: int = 300) -> Callable:
+    """Decorator: cache an async function's result for ttl_seconds.
+
+    The wrapped function may accept an optional `nocache=True` kwarg to
+    bypass + refresh the cache for that call.
+    """
+    def decorator(fn: Callable[..., Awaitable[Any]]) -> Callable[..., Awaitable[Any]]:
+        @functools.wraps(fn)
+        async def wrapper(*args, **kwargs):
+            nocache = bool(kwargs.pop("nocache", False))
+            key = _make_key(fn.__qualname__, args, kwargs)
+
+            if not nocache:
+                hit = _STORE.get(key)
+                if hit and hit[0] > time.monotonic():
+                    return hit[1]
+
+            lock = _LOCKS.setdefault(key, asyncio.Lock())
+            async with lock:
+                # Re-check after acquiring lock — another caller may have populated.
+                if not nocache:
+                    hit = _STORE.get(key)
+                    if hit and hit[0] > time.monotonic():
+                        return hit[1]
+
+                value = await fn(*args, **kwargs)
+                _STORE[key] = (time.monotonic() + ttl_seconds, value)
+                return value
+
+        return wrapper
+    return decorator
+
+
+def cache_stats() -> dict:
+    """Debug helper: return current cache entries (no values)."""
+    now = time.monotonic()
+    return {
+        "entries": len(_STORE),
+        "live": [
+            {"key": str(k), "ttl_remaining_s": round(v[0] - now, 1)}
+            for k, v in _STORE.items()
+            if v[0] > now
+        ],
+    }
+
+
+def cache_clear() -> int:
+    """Wipe the cache; returns the number of entries removed."""
+    n = len(_STORE)
+    _STORE.clear()
+    return n