diff --git a/rugnux/Rugnux.cpp b/rugnux/Rugnux.cpp
index 205ca1ea..3a6b46ea 100644
--- a/rugnux/Rugnux.cpp
+++ b/rugnux/Rugnux.cpp
@@ -506,6 +506,18 @@ ProcessResult Rugnux::RunPipeline(RugnuxObserver *observer, bool write_output, b
         // validation set; re-finding is expensive on the --redo-rotation-spots path). Accessed only
         // from single-threaded sections (the scheme feed and the validation loop), so no locking.
         std::map<int, std::vector<SpotToSave>> spot_cache;
+        // One analysis engine for the whole first pass. Constructing it allocates a CUDA stream and a
+        // full set of GPU buffers (preprocessing, spot finding, azimuthal integration, Bragg
+        // integration) - far more work than analysing a frame - so building it per image made
+        // --redo-rotation-spots pay for hundreds of them, serially. Only needed when spots have to be
+        // found; this section is single-threaded, so one engine is enough.
+        std::unique_ptr<MXAnalysisWithoutFPGA> analysis;
+        std::unique_ptr<AzimuthalIntegrationProfile> profile;
+        if (!reuse_spots) {
+            analysis = std::make_unique<MXAnalysisWithoutFPGA>(experiment_, mapping, pixel_mask_, *indexer,
+                                                              /*enable_fused_adaptive_gpu=*/true);
+            profile = std::make_unique<AzimuthalIntegrationProfile>(mapping);
+        }
         auto get_spots = [&](int ordinal) -> const std::vector<SpotToSave> & {
             auto it = spot_cache.find(ordinal);
             if (it != spot_cache.end())
@@ -519,16 +531,13 @@ ProcessResult Rugnux::RunPipeline(RugnuxObserver *observer, bool write_output, b
                     DataMessage m{};
                     m.number = ordinal;
                     m.original_number = image_idx;
-                    MXAnalysisWithoutFPGA analysis(experiment_, mapping, pixel_mask_, *indexer,
-                                                   /*enable_fused_adaptive_gpu=*/true);
-                    AzimuthalIntegrationProfile profile(mapping);
                     auto first_pass = config_.spot_finding;
                     first_pass.indexing = false;
                     first_pass.quick_integration = false;
                     m.image = img->image;
                     if (dataset->efficiency.size() > image_idx)
                         m.image_collection_efficiency = dataset->efficiency[image_idx];
-                    analysis.Analyze(m, profile, first_pass);
+                    analysis->Analyze(m, *profile, first_pass);
                     spots = std::move(m.spots);
                 }
             } catch (const std::exception &e) {