GROQ Speed Explained Without Neural Networks | Generated by AI
Short answer
No – GROQ itself does not run a transformer‑style neural network (no “K‑Q‑V” attention, no tensor‑matrix math, no GPT‑style inference).
Its speed comes from classic database‑engine tricks (column‑store layout, index‑driven filtering, compiled query plans, streaming execution, parallel workers, and aggressive caching).
If a Sanity project does use a transformer‑based service (e.g., embeddings for semantic search, OpenAI‑style completions, or a custom “AI‑field”), that part of the pipeline is separate from the GROQ engine and will have the latency characteristics of any other ML inference service. The two can be combined, but the “instant” feel you get when you run a plain GROQ query is not because a transformer is doing the heavy lifting.
Below is a deeper dive that explains:
- What GROQ actually is and why it’s fast (a recap of the previous answer, trimmed to the essentials).
- Why transformer inference is a different beast and why it’s generally slower than a pure index‑driven query.
- When and how you might see transformers in a Sanity workflow, and what tricks providers use to make that part faster.
- A quick comparison table showing the typical latency trade‑offs between pure‑GROQ queries, transformer‑based semantic search, and “hybrid” approaches.
1. GROQ = Compiled, Column‑Store Query Language (no neural nets)
| Component | What it does | Why it’s fast (vs. a model) |
|---|---|---|
| Content Lake (binary‑packed, column‑oriented store) | Stores each field in its own sorted, compressed column. | A filter can be satisfied by scanning a single tiny column; no need to deserialize whole JSON objects. |
| Query compilation | Parses the GROQ string once, builds an AST, creates a reusable execution plan. | The expensive parsing work is done only once; later calls just reuse the plan. |
| Push‑down filtering & projection | Evaluates predicates while reading the column, and only pulls the columns you ask for. | I/O is minimized; the engine never touches data that won’t appear in the result. |
| Streaming pipeline | Source → filter → map → slice → serializer → HTTP response. | First rows reach the client as soon as they’re ready, giving an “instant” perception. |
| Parallel, server‑less workers | Query is split across many shards and run on many CPU cores simultaneously. | Large result sets finish in ≈ tens of ms instead of seconds. |
| Caching layers (plan cache, edge CDN, fragment cache) | Stores compiled plans and often‑used result fragments. | Subsequent identical queries skip almost all work. |
All of these are deterministic, integer‑oriented operations that run on a CPU (or sometimes SIMD‑accelerated code). There is no matrix multiplication, back‑propagation, or floating‑point heavy lifting involved.
2. Transformer inference – why it’s slower (by design)
| Step in a typical transformer‑based service | Typical cost | Reason it’s slower than a pure index scan |
|---|---|---|
| Tokenisation (text → token IDs) | ~0.1 ms per 100 bytes | Still cheap, but adds overhead. |
| Embedding lookup / generation (matrix‑multiply) | 0.3 – 2 ms per token on a CPU; < 0.2 ms on a GPU/TPU | Requires floating‑point linear algebra on large weight matrices (often 12 – 96 layers). |
| Self‑attention (K‑Q‑V) for each layer | O(N²) per token‑sequence length (N) → ~1 – 5 ms for short sentences on a GPU; much more for longer sequences. | Quadratic scaling makes long inputs expensive. |
| Feed‑forward network + layer‑norm | Additional ~0.5 ms per layer | More floating‑point ops. |
| Decoding (if generating text) | 20 – 100 ms per token on a GPU; often > 200 ms on a CPU. | Autoregressive generation is inherently sequential. |
| Network latency (cloud endpoint) | 5 – 30 ms round‑trip (depending on provider) | Adds to the total latency. |
Even a highly‑optimised, quantised transformer (e.g., 8‑bit or 4‑bit) running on a modern GPU typically takes tens of milliseconds for a single embedding request, plus network hop time. That is orders of magnitude slower than a pure index scan that can be satisfied in a few milliseconds on the same hardware.
Bottom‑line physics
- Index look‑ups → O(1)–O(log N) reads of a few kilobytes → < 5 ms on a typical CPU.
- Transformer inference → O(L · D²) floating‑point ops (L = layers, D = hidden size) → 10‑100 ms on a GPU, > 100 ms on a CPU.
So when you see a “GROQ is fast” claim, it is not because Sanity has somehow replaced the mathematics of attention with a secret shortcut; it is because the problem they are solving (filtering and projecting structured content) is much better suited to classic database techniques.
3. When you do use transformers with Sanity – the “hybrid” pattern
Sanity is a headless CMS, not a machine‑learning platform. However, the ecosystem encourages a few common ways to sprinkle AI into a content workflow:
| Use‑case | How it is typically wired up | Where the latency comes from |
|---|---|---|
| Semantic search (e.g., “find articles about react hooks”) | 1️⃣ Export candidate documents → 2️⃣ Generate embeddings (OpenAI, Cohere, etc.) → 3️⃣ Store embeddings in a vector DB (Pinecone, Weaviate, etc.) → 4️⃣ At query time: embed the query → 5️⃣ Vector similarity search → 6️⃣ Use the resulting IDs in a GROQ filter (*_id in $ids). |
The heavy part is steps 2‑5 (embedding generation + vector similarity). Once you have the IDs, step 6 is a regular GROQ call and is instant. |
| Content‑generation assistants (auto‑fill a field, draft copy) | Front‑end sends a prompt to an LLM (OpenAI, Anthropic) → receives generated text → writes back to Sanity via its API. | The LLM inference latency dominates (usually 200 ms‑2 s). The subsequent write is a normal GROQ‑driven mutation (fast). |
| Auto‑tagging / classification | A webhook triggers on document create → serverless function calls a classifier model → writes back tags. | The classifier inference time (often a tiny transformer) is the bottleneck; the write path is fast. |
| Image‑to‑text (alt‑text generation) | Same pattern as above, but the model processes image bytes. | Image preprocessing + model inference dominates latency. |
Key point: All of the AI‑heavy steps are outside the GROQ engine. Once you have the AI‑derived data (IDs, tags, generated text), you go back to GROQ for the fast, index‑driven part.
How providers make the AI‑part “faster”
If you do need that AI step to be low‑latency, providers use a mix of engineering tricks:
| Trick | Effect on latency |
|---|---|
| Model quantisation (int8/4‑bit) | Reduces FLOPs → 2‑5× speed‑up on the same hardware. |
| GPU/TPU serving with batch‑size = 1 optimisation | Removes batch‑norm overhead; keeps GPU warm. |
| Compiled kernels (TensorRT, ONNX Runtime, XLA) | Eliminates Python‑level overhead, fuses ops. |
| Edge inference (e.g., Cloudflare Workers‑AI, Cloudflare AI Compute) | Cuts network round‑trip to < 5 ms for small models. |
| Cache of recent embeddings | If many queries repeat the same text, you can serve the embedding from a fast key‑value store (Redis, Cloudflare KV). |
| Hybrid “approximate nearest neighbour” (ANN) indices | Vector DBs like Qdrant or Pinecone use HNSW/IVF‑PQ that answer similarity queries in < 1 ms for millions of vectors. |
Even with those tricks, the AI step is still an order of magnitude slower than the pure GROQ index lookup. That’s why a typical “semantic search + GROQ” flow looks like:
client ──► embed query (≈30 ms) ──► vector DB similarity (≈5 ms)
│
└─► receive list of IDs ──► GROQ filter (≈2 ms) ──► final results
Overall ≈ 40 ms – fast enough for many UI experiences, but the bottleneck is clearly the embedding step, not the GROQ engine.
4. Quick latency comparison (illustrative numbers)
| Scenario | Typical latency (median) | What dominates |
|---|---|---|
Pure GROQ filter & projection (*[_type=="post" && publishedAt < now()]{title,slug}) |
10‑30 ms (cold), 5‑12 ms (warm) | Column reads + plan cache |
GROQ + vector‑search (semantic) (*[_id in $ids]{title,slug} after embedding) |
35‑60 ms total | Embedding generation + vector DB lookup |
Full‑text search using an external LLM (openai embeddings → vector DB → GROQ) |
80‑150 ms | LLM inference + network hop |
| GraphQL resolver that fetches each post from a relational DB | 120‑300 ms (often N+1) | Multiple round‑trips to DB, no column store |
| Elasticsearch query with aggregations | 50‑120 ms (depends on shard count) | Lucene scoring + network aggregation |
Numbers are ball‑park and vary by dataset size, hardware, and caching.
5. TL;DR for your exact question
- GROQ does not use transformers, K‑Q‑V attention, or any tensor‑matrix math.
- Its speed comes from database‑engine optimisations (column store, index‑driven filtering, compiled plans, streaming, parallel workers, caching).
- If you add a transformer to the pipeline (e.g., embeddings for semantic search), that part will be slower than the pure GROQ query; the overall latency is the sum of the two.
- Sanity’s “fast” claim only applies to the GROQ portion. Any AI‑heavy step is separate and will have the latency characteristics of the underlying model and serving infrastructure.
What to do next?
- Identify the part of your workflow that really needs a transformer (semantic search, auto‑generation, classification).
- Isolate that step: run it on a dedicated AI endpoint (OpenAI, Anthropic, self‑hosted quantised model).
- Keep the GROQ side pure: after you have the AI‑derived IDs/tags, fetch the final data with a regular GROQ query – you’ll retain the sub‑30 ms latency for the data‑retrieval part.
- Measure both stages separately (use
console.timeor a tracing tool). If AI latency dominates, experiment with quantisation, edge inference, or caching of embeddings.
By separating the two concerns, you get the best of both worlds: AI‑powered insight when you need it, and instant content retrieval for everything else.