diff --git a/.github/workflows/benchmark.yaml b/.github/workflows/benchmark.yaml
index 86b256c1..4f1749a9 100644
--- a/.github/workflows/benchmark.yaml
+++ b/.github/workflows/benchmark.yaml
@@ -62,7 +62,7 @@ jobs:
 
       - name: Delete previous benchmark files
         if: always()
-        run: rm -rf inst/extdata/benchmark-*.rds
+        run: rm -rf inst/extdata/benchmark*.rds
 
 
 
@@ -192,7 +192,7 @@ jobs:
 
           g <- ggplot2::ggplot(
             benchmarks,
-            ggplot2::aes(x = version, y = time / 1e9)
+            ggplot2::aes(x = version, y = time)
           ) +
             ggplot2::stat_summary(fun.data = mean_sd, geom = "pointrange", size = 0.5, linewidth = 1) +
             ggplot2::facet_grid(rows = ggplot2::vars(benchmark_function), cols = ggplot2::vars(database)) +
diff --git a/DESCRIPTION b/DESCRIPTION
index 2c8d13eb..e3e10bda 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -39,6 +39,7 @@ Imports:
     tidyselect,
     utils
 Suggests:
+    bench,
     callr,
     conflicted,
     duckdb,
@@ -46,7 +47,6 @@ Suggests:
     jsonlite,
     knitr,
     lintr,
-    microbenchmark,
     odbc,
     rmarkdown,
     roxygen2,
diff --git a/data-raw/benchmark.R b/data-raw/benchmark.R
index cc456ab7..047e6279 100644
--- a/data-raw/benchmark.R
+++ b/data-raw/benchmark.R
@@ -118,7 +118,8 @@ if (identical(Sys.getenv("CI"), "true") && identical(Sys.getenv("BACKEND"), ""))
       }
 
       # Construct the list of benchmarks
-      update_snapshot_benchmark <- microbenchmark::microbenchmark(scdb_updates(conn, data_on_conn), times = 25) |>
+      update_snapshot_benchmark <- bench::mark(scdb_updates(conn, data_on_conn), iterations = 25) |>
+        tidyr::unnest(c(time, gc)) |>
         dplyr::mutate(
           "benchmark_function" = "update_snapshot()",
           "database" = names(conns)[[1]],
@@ -145,7 +146,8 @@ if (identical(Sys.getenv("CI"), "true") && identical(Sys.getenv("BACKEND"), ""))
         }
 
         # Construct the list of benchmarks
-        update_snapshot_benchmark <- microbenchmark::microbenchmark(scdb_updates(conn, data), times = 5) |>
+        update_snapshot_benchmark <- bench::mark(scdb_updates(conn, data), iterations = 5) |>
+          tidyr::unnest(c(time, gc)) |>
           dplyr::mutate(
             "benchmark_function" = "update_snapshot() - complexity",
             "database" = names(conns)[[1]],
diff --git a/vignettes/benchmarks.Rmd b/vignettes/benchmarks.Rmd
index 80798cb7..ed5656d6 100644
--- a/vignettes/benchmarks.Rmd
+++ b/vignettes/benchmarks.Rmd
@@ -31,7 +31,7 @@ This data forms the basis for three "snapshots" used in the benchmarks:
 The benchmark function uses three consecutive calls to `update_snapshot()` to create the table with first snapshot and
 then update it to the second and third snapshot. Finally, the table is deleted.
 
-The performance of this benchmark function is timed with the `microbenchmark` package using 10 replicates.
+The performance of this benchmark function is timed with the `bench` package using 10 replicates.
 All benchmarks are run on the same machine.
 
 The results of the benchmark are shown graphically below (mean and standard deviation), where we compare the current
@@ -96,7 +96,7 @@ benchmark_1 <- benchmark_1 |>
 
 g <- ggplot2::ggplot(
   benchmark_1,
-  ggplot2::aes(x = version, y = time / 1e9)
+  ggplot2::aes(x = version, y = time)
 ) +
   ggplot2::stat_summary(fun.data = mean_sd, geom = "pointrange", size = 0.5, linewidth = 1) +
   ggplot2::facet_grid(rows = ggplot2::vars(benchmark_function), cols = ggplot2::vars(database)) +
@@ -111,11 +111,11 @@ g
 
 
 We include another benchmark to highlight the complexity scaling of the `update_snapshot() ` with the size of the input
-data. The datasets are similar to the first benchmark is used, but the number of repeats is varied to see the impact of
+data. The data sets are similar to the first benchmark is used, but the number of repeats is varied to see the impact of
 increasing data size. The benchmarks are run from a "clean" state, where the target_table does not exists. The benchmark
 measures both the time to create the table and to remove it again afterwards (to restore the clean state).
 
-The performance of this benchmark function is timed with the `microbenchmark` package using 5 replicates.
+The performance of this benchmark function is timed with the `bench` package using 5 replicates.
 All benchmarks are run on the same machine.
 
 The results of the benchmark are shown graphically below (mean and standard deviation), where we compare the current
@@ -128,7 +128,7 @@ benchmark_2 <- benchmarks |>
 
 ggplot2::ggplot(
   benchmark_2,
-  ggplot2::aes(x = n * nrow(iris) / 1e3, y = time / 1e9, color = version)
+  ggplot2::aes(x = n * nrow(iris) / 1e3, y = time, color = version)
 ) +
   ggplot2::stat_summary(fun.data = mean_sd, geom = "pointrange", size = 0.5, linewidth = 1) +
   ggplot2::facet_grid(rows = ggplot2::vars(benchmark_function), cols = ggplot2::vars(database)) +