diff --git a/apps/docs/pages/guides/ai/choosing-compute-addon.mdx b/apps/docs/pages/guides/ai/choosing-compute-addon.mdx
index fb7bf5c9fa..fef2f75860 100644
--- a/apps/docs/pages/guides/ai/choosing-compute-addon.mdx
+++ b/apps/docs/pages/guides/ai/choosing-compute-addon.mdx
@@ -21,6 +21,36 @@ The number of dimensions in your embeddings is the most important factor in choo
 
 ## HNSW
 
+### 384 Dimensions
+
+This benchmark uses the dbpedia-entities-openai-1M dataset, which contains 1,000,000 embeddings of text. Each embedding is 384 dimensions created with the [gte-small](https://huggingface.co/Supabase/gte-small).
+
+<Tabs
+  scrollable
+  size="small"
+  type="underlined"
+  defaultActiveId="gte384"
+>
+<TabPanel id="gte384" label="gte-small-384">
+
+| Plan   | Vectors   | m   | ef_construction | ef_search | QPS  | Latency Mean | Latency p95 | RAM Usage  | RAM    |
+| ------ | --------- | --- | --------------- | --------- | ---- | ------------ | ----------- | ---------- | ------ |
+| Free   | 100,000   | 16  | 64              | 60        | 580  | 0.017 sec    | 0.024 sec   | 1.2 (Swap) | 1 GB   |
+| Small  | 250,000   | 24  | 64              | 60        | 440  | 0.022 sec    | 0.033 sec   | 2 GB       | 2 GB   |
+| Medium | 500,000   | 24  | 64              | 80        | 350  | 0.028 sec    | 0.045 sec   | 4 GB       | 4 GB   |
+| Large  | 1,000,000 | 32  | 80              | 100       | 270  | 0.073 sec    | 0.108 sec   | 7 GB       | 8 GB   |
+| XL     | 1,000,000 | 32  | 80              | 100       | 525  | 0.038 sec    | 0.059 sec   | 9 GB       | 16 GB  |
+| 2XL    | 1,000,000 | 32  | 80              | 100       | 790  | 0.025 sec    | 0.037 sec   | 9 GB       | 32 GB  |
+| 4XL    | 1,000,000 | 32  | 80              | 100       | 1650 | 0.015 sec    | 0.018 sec   | 11 GB      | 64 GB  |
+| 8XL    | 1,000,000 | 32  | 80              | 100       | 2690 | 0.015 sec    | 0.016 sec   | 13 GB      | 128 GB |
+| 12XL   | 1,000,000 | 32  | 80              | 100       | 3900 | 0.014 sec    | 0.016 sec   | 13 GB      | 192 GB |
+| 16XL   | 1,000,000 | 32  | 80              | 100       | 4200 | 0.014 sec    | 0.016 sec   | 20 GB      | 256 GB |
+
+Accuracy was 0.99 for benchmarks.
+
+</TabPanel>
+</Tabs>
+
 ### 1536 Dimensions
 
 This benchmark uses the [dbpedia-entities-openai-1M](https://huggingface.co/datasets/KShivendu/dbpedia-entities-openai-1M) dataset, which contains 1,000,000 embeddings of text. And 224,482 embeddings from [Wikipedia articles](https://huggingface.co/datasets/Supabase/wikipedia-en-embeddings) for compute add-ons `large` and below. Each embedding is 1536 dimensions created with the [OpenAI Embeddings API](https://platform.openai.com/docs/guides/embeddings).
@@ -33,18 +63,18 @@ This benchmark uses the [dbpedia-entities-openai-1M](https://huggingface.co/data
 >
 <TabPanel id="openai1536" label="OpenAI-1536">
 
-| Plan   | Vectors   | m   | ef_construction | ef_search | QPS  | Latency Mean | Latency p95 | RAM Usage          | RAM    |
-| ------ | --------- | --- | --------------- | --------- | ---- | ------------ | ----------- | ------------------ | ------ |
-| Free   | 15,000    | 16  | 40              | 40        | 480  | 0.011 sec    | 0.016 sec   | 1 GB + 200 Mb Swap | 1 GB   |
-| Small  | 50,000    | 32  | 64              | 100       | 175  | 0.031 sec    | 0.051 sec   | 2 GB + 200 Mb Swap | 2 GB   |
-| Medium | 100,000   | 32  | 64              | 100       | 240  | 0.083 sec    | 0.126 sec   | 4 GB               | 4 GB   |
-| Large  | 224,482   | 32  | 64              | 100       | 280  | 0.017 sec    | 0.028 sec   | 8 GB               | 8 GB   |
-| XL     | 500,000   | 24  | 56              | 100       | 360  | 0.055 sec    | 0.135 sec   | 13 GB              | 16 GB  |
-| 2XL    | 1,000,000 | 24  | 56              | 250       | 560  | 0.036 sec    | 0.058 sec   | 32 GB              | 32 GB  |
-| 4XL    | 1,000,000 | 24  | 56              | 250       | 950  | 0.021 sec    | 0.033 sec   | 39 GB              | 64 GB  |
-| 8XL    | 1,000,000 | 24  | 56              | 250       | 1650 | 0.016 sec    | 0.023 sec   | 40 GB              | 128 GB |
-| 12XL   | 1,000,000 | 24  | 56              | 250       | 1900 | 0.015 sec    | 0.021 sec   | 38 GB              | 192 GB |
-| 16XL   | 1,000,000 | 24  | 56              | 250       | 2200 | 0.015 sec    | 0.020 sec   | 40 GB              | 256 GB |
+| Plan   | Vectors   | m   | ef_construction | ef_search | QPS  | Latency Mean | Latency p95 | RAM Usage     | RAM    |
+| ------ | --------- | --- | --------------- | --------- | ---- | ------------ | ----------- | ------------- | ------ |
+| Free   | 15,000    | 16  | 40              | 40        | 480  | 0.011 sec    | 0.016 sec   | 1.2 GB (Swap) | 1 GB   |
+| Small  | 50,000    | 32  | 64              | 100       | 175  | 0.031 sec    | 0.051 sec   | 2.2 GB (Swap) | 2 GB   |
+| Medium | 100,000   | 32  | 64              | 100       | 240  | 0.083 sec    | 0.126 sec   | 4 GB          | 4 GB   |
+| Large  | 224,482   | 32  | 64              | 100       | 280  | 0.017 sec    | 0.028 sec   | 8 GB          | 8 GB   |
+| XL     | 500,000   | 24  | 56              | 100       | 360  | 0.055 sec    | 0.135 sec   | 13 GB         | 16 GB  |
+| 2XL    | 1,000,000 | 24  | 56              | 250       | 560  | 0.036 sec    | 0.058 sec   | 32 GB         | 32 GB  |
+| 4XL    | 1,000,000 | 24  | 56              | 250       | 950  | 0.021 sec    | 0.033 sec   | 39 GB         | 64 GB  |
+| 8XL    | 1,000,000 | 24  | 56              | 250       | 1650 | 0.016 sec    | 0.023 sec   | 40 GB         | 128 GB |
+| 12XL   | 1,000,000 | 24  | 56              | 250       | 1900 | 0.015 sec    | 0.021 sec   | 38 GB         | 192 GB |
+| 16XL   | 1,000,000 | 24  | 56              | 250       | 2200 | 0.015 sec    | 0.020 sec   | 40 GB         | 256 GB |
 
 Accuracy was 0.99 for benchmarks.
 
@@ -59,49 +89,61 @@ It is possible to upload more vectors to a single table if Memory allows it (for
 
 </Admonition>
 
+<div>
+  <img
+    alt="multi database"
+    className="dark:hidden"
+    src="/docs/img/ai/instance-type/hnsw-dims--light.png"
+  />
+  <img
+    alt="multi database"
+    className="hidden dark:block"
+    src="/docs/img/ai/instance-type/hnsw-dims--dark.png"
+  />
+</div>
+
 ## IVFFlat
 
-### 512 Dimensions
+### 384 Dimensions
 
-This benchmark uses the [GloVe Reddit comments](https://nlp.stanford.edu/projects/glove/) dataset, which contains 1,623,397 embeddings of text. Each embedding is 512 dimensions. Random vectors were generated for queries.
+This benchmark uses the dbpedia-entities-openai-1M dataset, which contains 1,000,000 embeddings of text. Each embedding is 384 dimensions created with the [gte-small](https://huggingface.co/Supabase/gte-small).
 
 <Tabs
   scrollable
   size="small"
   type="underlined"
-  defaultActiveId="glove512"
+  defaultActiveId="gte384_98"
 >
-<TabPanel id="glove512" label="GloVe-512, probes = 10">
+<TabPanel id="gte384_98" label="gte-small-384, accuracy=.98">
 
-| Plan   | Vectors   | Lists | QPS  | Latency Mean | Latency p95 | RAM Usage          | RAM    |
-| ------ | --------- | ----- | ---- | ------------ | ----------- | ------------------ | ------ |
-| Free   | 100,000   | 100   | 250  | 0.395 sec    | 0.432 sec   | 1 GB + 300 Mb Swap | 1 GB   |
-| Small  | 250,000   | 250   | 440  | 0.223 sec    | 0.250 sec   | 2 GB + 200 Mb Swap | 2 GB   |
-| Medium | 500,000   | 500   | 425  | 0.116 sec    | 0.143 sec   | 3.7 GB             | 4 GB   |
-| Large  | 1,000,000 | 1000  | 515  | 0.096 sec    | 0.116 sec   | 7.5 GB             | 8 GB   |
-| XL     | 1,623,397 | 1275  | 465  | 0.212 sec    | 0.272 sec   | 14 GB              | 16 GB  |
-| 2XL    | 1,623,397 | 1275  | 1400 | 0.061 sec    | 0.075 sec   | 22 GB              | 32 GB  |
-| 4XL    | 1,623,397 | 1275  | 1800 | 0.027 sec    | 0.043 sec   | 20 GB              | 64 GB  |
-| 8XL    | 1,623,397 | 1275  | 2850 | 0.032 sec    | 0.049 sec   | 21 GB              | 128 GB |
-| 12XL   | 1,623,397 | 1275  | 3700 | 0.020 sec    | 0.036 sec   | 26 GB              | 192 GB |
-| 16XL   | 1,623,397 | 1275  | 3700 | 0.025 sec    | 0.042 sec   | 29 GB              | 256 GB |
+| Plan   | Vectors   | Lists | Probes | QPS  | Latency Mean | Latency p95 | RAM Usage     | RAM    |
+| ------ | --------- | ----- | ------ | ---- | ------------ | ----------- | ------------- | ------ |
+| Free   | 100,000   | 500   | 50     | 205  | 0.048 sec    | 0.066 sec   | 1.2 GB (Swap) | 1 GB   |
+| Small  | 250,000   | 1000  | 60     | 160  | 0.062 sec    | 0.079 sec   | 2 GB          | 2 GB   |
+| Medium | 500,000   | 2000  | 80     | 120  | 0.082 sec    | 0.104 sec   | 3.2 GB        | 4 GB   |
+| Large  | 1,000,000 | 5000  | 150    | 75   | 0.269 sec    | 0.375 sec   | 6.5 GB        | 8 GB   |
+| XL     | 1,000,000 | 5000  | 150    | 150  | 0.131 sec    | 0.178 sec   | 9 GB          | 16 GB  |
+| 2XL    | 1,000,000 | 5000  | 150    | 300  | 0.066 sec    | 0.099 sec   | 10 GB         | 32 GB  |
+| 4XL    | 1,000,000 | 5000  | 150    | 570  | 0.035 sec    | 0.046 sec   | 10 GB         | 64 GB  |
+| 8XL    | 1,000,000 | 5000  | 150    | 1400 | 0.023 sec    | 0.028 sec   | 12 GB         | 128 GB |
+| 12XL   | 1,000,000 | 5000  | 150    | 1550 | 0.030 sec    | 0.039 sec   | 12 GB         | 192 GB |
+| 16XL   | 1,000,000 | 5000  | 150    | 1800 | 0.030 sec    | 0.039 sec   | 16 GB         | 256 GB |
 
 </TabPanel>
-<TabPanel id="glove512_60" label="GloVe-512, probes = 60">
+<TabPanel id="gte384_99" label="gte-small-384, accuracy=.99">
 
-| Plan   | Vectors   | Lists | QPS | Latency Mean | Latency p95 | RAM Usage | RAM    |
-| ------ | --------- | ----- | --- | ------------ | ----------- | --------- | ------ |
-| Free   | 100,000   | 100   | -   | -            | -           | -         | 1 GB   |
-| Small  | 250,000   | 250   | -   | -            | -           | -         | 2 GB   |
-| Medium | 500,000   | 500   | 75  | 0.656 sec    | 0.750 sec   | 3.7 GB    | 4 GB   |
-| Large  | 1,000,000 | 1000  | 102 | 0.488 sec    | 0.580 sec   | 7.5 GB    | 8 GB   |
-| XL     | 1,000,000 | 1000  | 188 | 0.525 sec    | 0.596 sec   | 14 GB     | 16 GB  |
-| XL     | 1,623,397 | 1275  | 75  | 0.679 sec    | 0.798 sec   | 14 GB     | 16 GB  |
-| 2XL    | 1,623,397 | 1275  | 160 | 0.314 sec    | 0.384 sec   | 22 GB     | 32 GB  |
-| 4XL    | 1,623,397 | 1275  | 300 | 0.083 sec    | 0.113 sec   | 20 GB     | 64 GB  |
-| 8XL    | 1,623,397 | 1275  | 565 | 0.105 sec    | 0.141 sec   | 21 GB     | 128 GB |
-| 12XL   | 1,623,397 | 1275  | 840 | 0.093 sec    | 0.124 sec   | 26 GB     | 192 GB |
-| 16XL   | 1,623,397 | 1275  | 940 | 0.084 sec    | 0.108 sec   | 29 GB     | 256 GB |
+| Plan   | Vectors   | Lists | Probes | QPS  | Latency Mean | Latency p95 | RAM Usage     | RAM    |
+| ------ | --------- | ----- | ------ | ---- | ------------ | ----------- | ------------- | ------ |
+| Free   | 100,000   | 500   | 70     | 160  | 0.062 sec    | 0.079 sec   | 1.2 GB (Swap) | 1 GB   |
+| Small  | 250,000   | 1000  | 100    | 100  | 0.096 sec    | 0.113 sec   | 2 GB          | 2 GB   |
+| Medium | 500,000   | 2000  | 120    | 85   | 0.117 sec    | 0.147 sec   | 3.2 GB        | 4 GB   |
+| Large  | 1,000,000 | 5000  | 250    | 50   | 0.394 sec    | 0.521 sec   | 6.5 GB        | 8 GB   |
+| XL     | 1,000,000 | 5000  | 250    | 100  | 0.197 sec    | 0.255 sec   | 10 GB         | 16 GB  |
+| 2XL    | 1,000,000 | 5000  | 250    | 200  | 0.098 sec    | 0.140 sec   | 10 GB         | 32 GB  |
+| 4XL    | 1,000,000 | 5000  | 250    | 390  | 0.051 sec    | 0.066 sec   | 11 GB         | 64 GB  |
+| 8XL    | 1,000,000 | 5000  | 250    | 850  | 0.036 sec    | 0.042 sec   | 12 GB         | 128 GB |
+| 12XL   | 1,000,000 | 5000  | 250    | 1000 | 0.043 sec    | 0.055 sec   | 13 GB         | 192 GB |
+| 16XL   | 1,000,000 | 5000  | 250    | 1200 | 0.043 sec    | 0.055 sec   | 16 GB         | 256 GB |
 
 </TabPanel>
 </Tabs>
@@ -118,18 +160,18 @@ This benchmark uses the [gist-960-angular](http://corpus-texmex.irisa.fr/) datas
 >
 <TabPanel id="gist960" label="gist-960, probes = 10">
 
-| Plan   | Vectors   | Lists | QPS  | Latency Mean | Latency p95 | RAM Usage          | RAM    |
-| ------ | --------- | ----- | ---- | ------------ | ----------- | ------------------ | ------ |
-| Free   | 30,000    | 30    | 75   | 0.065 sec    | 0.088 sec   | 1 GB + 100 Mb Swap | 1 GB   |
-| Small  | 100,000   | 100   | 78   | 0.064 sec    | 0.092 sec   | 1.8 GB             | 2 GB   |
-| Medium | 250,000   | 250   | 58   | 0.085 sec    | 0.129 sec   | 3.2 GB             | 4 GB   |
-| Large  | 500,000   | 500   | 55   | 0.088 sec    | 0.140 sec   | 5 GB               | 8 GB   |
-| XL     | 1,000,000 | 1000  | 110  | 0.046 sec    | 0.070 sec   | 14 GB              | 16 GB  |
-| 2XL    | 1,000,000 | 1000  | 235  | 0.083 sec    | 0.136 sec   | 10 GB              | 32 GB  |
-| 4XL    | 1,000,000 | 1000  | 420  | 0.071 sec    | 0.106 sec   | 11 GB              | 64 GB  |
-| 8XL    | 1,000,000 | 1000  | 815  | 0.072 sec    | 0.106 sec   | 13 GB              | 128 GB |
-| 12XL   | 1,000,000 | 1000  | 1150 | 0.052 sec    | 0.078 sec   | 15.5 GB            | 192 GB |
-| 16XL   | 1,000,000 | 1000  | 1345 | 0.072 sec    | 0.106 sec   | 17.5 GB            | 256 GB |
+| Plan   | Vectors   | Lists | QPS  | Latency Mean | Latency p95 | RAM Usage     | RAM    |
+| ------ | --------- | ----- | ---- | ------------ | ----------- | ------------- | ------ |
+| Free   | 30,000    | 30    | 75   | 0.065 sec    | 0.088 sec   | 1.1 GB (Swap) | 1 GB   |
+| Small  | 100,000   | 100   | 78   | 0.064 sec    | 0.092 sec   | 1.8 GB        | 2 GB   |
+| Medium | 250,000   | 250   | 58   | 0.085 sec    | 0.129 sec   | 3.2 GB        | 4 GB   |
+| Large  | 500,000   | 500   | 55   | 0.088 sec    | 0.140 sec   | 5 GB          | 8 GB   |
+| XL     | 1,000,000 | 1000  | 110  | 0.046 sec    | 0.070 sec   | 14 GB         | 16 GB  |
+| 2XL    | 1,000,000 | 1000  | 235  | 0.083 sec    | 0.136 sec   | 10 GB         | 32 GB  |
+| 4XL    | 1,000,000 | 1000  | 420  | 0.071 sec    | 0.106 sec   | 11 GB         | 64 GB  |
+| 8XL    | 1,000,000 | 1000  | 815  | 0.072 sec    | 0.106 sec   | 13 GB         | 128 GB |
+| 12XL   | 1,000,000 | 1000  | 1150 | 0.052 sec    | 0.078 sec   | 15.5 GB       | 192 GB |
+| 16XL   | 1,000,000 | 1000  | 1345 | 0.072 sec    | 0.106 sec   | 17.5 GB       | 256 GB |
 
 </TabPanel>
 </Tabs>
@@ -146,18 +188,18 @@ This benchmark uses the [dbpedia-entities-openai-1M](https://huggingface.co/data
 >
 <TabPanel id="dbpedia1536" label="OpenAI-1536, probes = 10">
 
-| Plan   | Vectors   | Lists | QPS  | Latency Mean | Latency p95 | RAM Usage          | RAM    |
-| ------ | --------- | ----- | ---- | ------------ | ----------- | ------------------ | ------ |
-| Free   | 20,000    | 40    | 135  | 0.372 sec    | 0.412 sec   | 1 GB + 200 Mb Swap | 1 GB   |
-| Small  | 50,000    | 100   | 140  | 0.357 sec    | 0.398 sec   | 1.8 GB             | 2 GB   |
-| Medium | 100,000   | 200   | 130  | 0.383 sec    | 0.446 sec   | 3.7 GB             | 4 GB   |
-| Large  | 250,000   | 500   | 130  | 0.378 sec    | 0.434 sec   | 7 GB               | 8 GB   |
-| XL     | 500,000   | 1000  | 235  | 0.213 sec    | 0.271 sec   | 13.5 GB            | 16 GB  |
-| 2XL    | 1,000,000 | 2000  | 380  | 0.133 sec    | 0.236 sec   | 30 GB              | 32 GB  |
-| 4XL    | 1,000,000 | 2000  | 720  | 0.068 sec    | 0.120 sec   | 35 GB              | 64 GB  |
-| 8XL    | 1,000,000 | 2000  | 1250 | 0.039 sec    | 0.066 sec   | 38 GB              | 128 GB |
-| 12XL   | 1,000,000 | 2000  | 1600 | 0.030 sec    | 0.052 sec   | 41 GB              | 192 GB |
-| 16XL   | 1,000,000 | 2000  | 1790 | 0.029 sec    | 0.051 sec   | 45 GB              | 256 GB |
+| Plan   | Vectors   | Lists | QPS  | Latency Mean | Latency p95 | RAM Usage     | RAM    |
+| ------ | --------- | ----- | ---- | ------------ | ----------- | ------------- | ------ |
+| Free   | 20,000    | 40    | 135  | 0.372 sec    | 0.412 sec   | 1.2 GB (Swap) | 1 GB   |
+| Small  | 50,000    | 100   | 140  | 0.357 sec    | 0.398 sec   | 1.8 GB        | 2 GB   |
+| Medium | 100,000   | 200   | 130  | 0.383 sec    | 0.446 sec   | 3.7 GB        | 4 GB   |
+| Large  | 250,000   | 500   | 130  | 0.378 sec    | 0.434 sec   | 7 GB          | 8 GB   |
+| XL     | 500,000   | 1000  | 235  | 0.213 sec    | 0.271 sec   | 13.5 GB       | 16 GB  |
+| 2XL    | 1,000,000 | 2000  | 380  | 0.133 sec    | 0.236 sec   | 30 GB         | 32 GB  |
+| 4XL    | 1,000,000 | 2000  | 720  | 0.068 sec    | 0.120 sec   | 35 GB         | 64 GB  |
+| 8XL    | 1,000,000 | 2000  | 1250 | 0.039 sec    | 0.066 sec   | 38 GB         | 128 GB |
+| 12XL   | 1,000,000 | 2000  | 1600 | 0.030 sec    | 0.052 sec   | 41 GB         | 192 GB |
+| 16XL   | 1,000,000 | 2000  | 1790 | 0.029 sec    | 0.051 sec   | 45 GB         | 256 GB |
 
 For 1,000,000 vectors 10 probes results to accuracy of 0.91. And for 500,000 vectors and below 10 probes results to accuracy in the range of 0.95 - 0.99. To increase accuracy, you need to increase the number of probes.
 
@@ -209,10 +251,33 @@ There are various ways to improve your pgvector performance. Here are some tips:
 
 It's useful to execute a few thousand “warm-up” queries before going into production. This helps help with RAM utilization. This can also help to determine that you've selected the right instance size for your workload.
 
-### Increase the number of lists
+### Finetune index parameters
 
-You can increase the Requests per Second by increasing the number of `lists`. This also has an important caveat: building the index takes longer with more lists.
+You can increase the Requests per Second by increasing `m` and `ef_construction` or `lists`. This also has an important caveat: building the index takes longer with higher values for these parameters.
 
+<Tabs
+  scrollable
+  size="small"
+  type="underlined"
+  defaultActiveId="hnsw"
+>
+<TabPanel id="hnsw" label="HNSW">
+
+<div>
+  <img
+    alt="dbpedia embeddings comparing hnsw queries-per-second using different build parameters (light)"
+    className="dark:hidden"
+    src="/docs/img/ai/going-prod/dbpedia-hnsw-build-parameters--light.png"
+  />
+  <img
+    alt="dbpedia embeddings comparing hnsw queries-per-second using different build parameters (dark)"
+    className="hidden dark:block"
+    src="/docs/img/ai/going-prod/dbpedia-hnsw-build-parameters--dark.png"
+  />
+</div>
+
+</TabPanel>
+<TabPanel id="ivfflat" label="IVFFlat">
 <div>
   <img
     alt="multi database"
@@ -225,6 +290,8 @@ You can increase the Requests per Second by increasing the number of `lists`. Th
     src="/docs/img/ai/instance-type/lists-for-1m--dark.png"
   />
 </div>
+</TabPanel>
+</Tabs>
 
 Check out more tips and the complete step-by-step guide in [Going to Production for AI applications](going-to-prod).
 
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