diff --git a/python/ray/autoscaler/autoscaler.py b/python/ray/autoscaler/autoscaler.py
index a806e3b62..1b2f45ebf 100644
--- a/python/ray/autoscaler/autoscaler.py
+++ b/python/ray/autoscaler/autoscaler.py
@@ -50,6 +50,9 @@ CLUSTER_CONFIG_SCHEMA = {
     # node. This takes precedence over min_workers.
     "max_workers": (int, REQUIRED),
 
+    # The number of workers to launch initially, in addition to the head node.
+    "initial_workers": (int, OPTIONAL),
+
     # The autoscaler will scale up the cluster to this target fraction of
     # resources usage. For example, if a cluster of 8 nodes is 100% busy
     # and target_utilization was 0.8, it would resize the cluster to 10.
@@ -324,6 +327,7 @@ class StandardAutoscaler(object):
         self.num_failures = 0
         self.last_update_time = 0.0
         self.update_interval_s = update_interval_s
+        self.bringup = True
 
         # Node launchers
         self.launch_queue = queue.Queue()
@@ -418,6 +422,8 @@ class StandardAutoscaler(object):
             num_launches = min(max_allowed, target_workers - num_workers)
             self.launch_new_node(num_launches)
             logger.info(self.info_string())
+        else:
+            self.bringup = False
 
         # Process any completed updates
         completed = []
@@ -467,10 +473,15 @@ class StandardAutoscaler(object):
                 logger.exception("StandardAutoscaler: Error parsing config.")
 
     def target_num_workers(self):
-        target_frac = self.config["target_utilization_fraction"]
-        cur_used = self.load_metrics.approx_workers_used()
-        ideal_num_nodes = int(np.ceil(cur_used / float(target_frac)))
-        ideal_num_workers = ideal_num_nodes - 1  # subtract 1 for head node
+        initial_workers = self.config["initial_workers"]
+
+        if self.bringup:
+            ideal_num_workers = initial_workers
+        else:
+            target_frac = self.config["target_utilization_fraction"]
+            cur_used = self.load_metrics.approx_workers_used()
+            ideal_num_nodes = int(np.ceil(cur_used / float(target_frac)))
+            ideal_num_workers = ideal_num_nodes - 1  # subtract 1 for head node
         return min(self.config["max_workers"],
                    max(self.config["min_workers"], ideal_num_workers))
 
@@ -577,6 +588,8 @@ class StandardAutoscaler(object):
         if self.num_failed_updates:
             suffix += " ({} failed to update)".format(
                 len(self.num_failed_updates))
+        if self.bringup:
+            suffix += " (bringup=True)"
         return "StandardAutoscaler [{}]: {}/{} target nodes{}\n{}".format(
             datetime.now(), len(nodes), self.target_num_workers(), suffix,
             self.load_metrics.info_string())
diff --git a/python/ray/autoscaler/aws/example-full.yaml b/python/ray/autoscaler/aws/example-full.yaml
index afe767a93..a415135a1 100644
--- a/python/ray/autoscaler/aws/example-full.yaml
+++ b/python/ray/autoscaler/aws/example-full.yaml
@@ -9,6 +9,11 @@ min_workers: 0
 # node. This takes precedence over min_workers.
 max_workers: 2
 
+# The initial number of worker nodes to launch in addition to the head
+# node. When the cluster is first brought up (or when it is refreshed with a
+# subsequent `ray up`) this number of nodes will be started.
+initial_workers: 0
+
 # This executes all commands on all nodes in the docker container,
 # and opens all the necessary ports to support the Ray cluster.
 # Empty string means disabled.
diff --git a/python/ray/autoscaler/gcp/example-full.yaml b/python/ray/autoscaler/gcp/example-full.yaml
index b841781e9..c7f2d8f72 100644
--- a/python/ray/autoscaler/gcp/example-full.yaml
+++ b/python/ray/autoscaler/gcp/example-full.yaml
@@ -9,6 +9,11 @@ min_workers: 0
 # node. This takes precedence over min_workers.
 max_workers: 2
 
+# The initial number of worker nodes to launch in addition to the head
+# node. When the cluster is first brought up (or when it is refreshed with a
+# subsequent `ray up`) this number of nodes will be started.
+initial_workers: 0
+
 # This executes all commands on all nodes in the docker container,
 # and opens all the necessary ports to support the Ray cluster.
 # Empty string means disabled.
diff --git a/python/ray/autoscaler/local/example-full.yaml b/python/ray/autoscaler/local/example-full.yaml
index 88d20dadd..51f64cbfb 100644
--- a/python/ray/autoscaler/local/example-full.yaml
+++ b/python/ray/autoscaler/local/example-full.yaml
@@ -1,6 +1,7 @@
 cluster_name: default
 min_workers: 0
 max_workers: 0
+initial_workers: 0
 docker:
     image: ""
     container_name: ""
diff --git a/test/autoscaler_test.py b/test/autoscaler_test.py
index 4312fdcb1..10e0cda1a 100644
--- a/test/autoscaler_test.py
+++ b/test/autoscaler_test.py
@@ -98,6 +98,7 @@ SMALL_CLUSTER = {
     "cluster_name": "default",
     "min_workers": 2,
     "max_workers": 2,
+    "initial_workers": 0,
     "target_utilization_fraction": 0.8,
     "idle_timeout_minutes": 5,
     "provider": {
@@ -314,6 +315,27 @@ class AutoscalingTest(unittest.TestCase):
         autoscaler.update()
         self.waitForNodes(10)
 
+    def testInitialWorkers(self):
+        config = SMALL_CLUSTER.copy()
+        config["min_workers"] = 0
+        config["max_workers"] = 20
+        config["initial_workers"] = 10
+        config_path = self.write_config(config)
+        self.provider = MockProvider()
+        autoscaler = StandardAutoscaler(
+            config_path,
+            LoadMetrics(),
+            max_launch_batch=5,
+            max_concurrent_launches=5,
+            max_failures=0,
+            update_interval_s=0)
+        self.waitForNodes(0)
+        autoscaler.update()
+        self.waitForNodes(5)  # expected due to batch sizes and concurrency
+        autoscaler.update()
+        self.waitForNodes(10)
+        autoscaler.update()
+
     def testDelayedLaunch(self):
         config_path = self.write_config(SMALL_CLUSTER)
         self.provider = MockProvider()