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You can estimate whether your data is a plausible fit, choose a search strategy, and test it safely on the actual machine. Treat 2GB as a whole-system budget, not as memory available exclusively to the vector index.
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SQLite describes itself as an in-process database engine: it reads and writes ordinary files, rather than running as a separate database server. That keeps deployment simple, but it does not make vector search memory-free. Your application, SQLite’s caches, the vector extension, active queries, and the operating system’s file cache all compete for host resources.
A database file can be larger than RAM because it lives on disk. How much of it must be in memory at once depends on the extension and search mode. If an index or scan working set is not cached, storage reads can increase query latency. File size alone therefore neither predicts peak memory nor proves that a workload will be fast enough.
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Also distinguish process memory from whole-machine pressure: file-backed pages may affect the operating system’s available memory without appearing in an application’s process-resident-memory figure in the same way as private allocations. Monitor both the service and the VPS as a whole.
Estimate the vector payload before choosing an index
For uncompressed float32 vectors, calculate the raw payload as:
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vector count × dimensions per vector × 4 bytes
SQLite-Vector documentation estimates that 1 million 768-dimensional float32 vectors contain about 3.07 GB of raw vector payload, before SQLite row and page overhead. That is a storage estimate—not a measured database size or a prediction of resident memory. It is already larger than 2GB, leaving no sensible basis for assuming the whole workload will fit in RAM as float32 data.
Changing the representation can reduce the payload. For example, one byte per dimension for an INT8 representation would make that same 1-million-by-768 payload 768 MB before other storage and runtime costs. Smaller representations can save more, but may affect search accuracy. SQLite-Vector describes TurboQuant scan representations at roughly 13% of raw payload for 4-bit, 10% for 3-bit, and 7% for 2-bit. These are project documentation figures; validate the actual footprint with your chosen version and vectors.
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Do not translate any of those payload numbers directly into a RAM requirement. Include SQLite structures, extension/index data, query working memory, the application, and the operating system in your capacity test.
Choose between exact scans and approximate indexes
An exact search compares a query against the vectors in scope and can serve as a useful recall baseline. Approximate nearest-neighbor (ANN) indexes can reduce the work per query, but introduce index construction, tuning, and recall trade-offs. A compact scan representation is another option: it can reduce memory pressure without necessarily being the same thing as building a conventional ANN index.
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SQLite-related options documented by their projects differ in maturity and behavior. Verify the current release, supported formats, and compatibility with the SQLite build used by your application before committing to one.
| Option | Documented approach | Important qualification |
|---|---|---|
| SQLite-Vector | Ordinary SQLite tables with vector data in BLOBs; documentation describes float32, float16, bfloat16, int8, uint8, and 1-bit formats, plus exact scans, quantized scans, and a bounded streamed mode. | The project says its approach does not require pre-indexing. Confirm the package version and actual defaults; compact formats and streaming still need validation against your data and workload. |
| sqlite-vec | A portable C extension with vec0 virtual tables and float, int8, and binary vectors, plus metadata, auxiliary columns, and partition keys. |
The repository labels it pre-v1 and warns that breaking changes may occur. Pin and test the version you deploy. |
| vec1 | SQLite’s extension documentation describes approximate IVFADC search with OPQ and L2 or cosine distance. Its reference also describes a flat index that stores vectors in packed SQL BLOBs and includes a bucket-probe parameter. |
The overview lists incomplete areas and roadmap items. Do not assume features beyond those documented for the version you use. |
| sqlite-vss | A Faiss-based extension with documented index options; its documentation says IVF indexes require training. | The repository says the project is no longer actively developed and points to sqlite-vec as the maintainer’s focus. Its reported training and insertion times are specific to the example dataset, not a forecast for yours. |
Do not select an extension on vector count alone. Compare recall at your chosen k, warm and cold latency, index build or training time, incremental write behavior, on-disk footprint, peak service and system memory, storage bandwidth, extension maintenance, and compatibility with your application’s SQLite build.
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What the published bounded-memory benchmark establishes
SQLite-Vector reports results for 1 million vectors using an INT8 index on an Apple M5 Pro. In its preloaded mode, it reports 740 MB peak memory, 37.6 milliseconds per query, and 99.5% Recall@20. In streamed mode, it reports 30 MB peak memory, 114.4 milliseconds per query, and the same 99.5% Recall@20.
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| SQLite-Vector project mode | Reported peak memory | Reported query time | Reported recall |
|---|---|---|---|
| INT8, preloaded | 740 MB | 37.6 ms/query | 99.5% Recall@20 |
| INT8, streamed | 30 MB | 114.4 ms/query | 99.5% Recall@20 |
These are project-reported results, accessed in 2026, not independently reproduced VPS measurements. The project says timings are best-of-20 with the database file in the operating-system page cache. It describes streaming as using about 25 times less peak memory for roughly three times the query latency in this benchmark. The 30 MB figure is not the total physical-RAM requirement for an entire service. If the index does not fit in cache on your VPS, storage-read time adds latency; the result depends on the data read and the machine’s storage performance.
Quantization also needs a recall check on the data that matters to you. In one SQLite-Vector real-dataset example with 10,000 vectors, the project reports Recall@10 of 0.948 for TurboQuant 4-bit, 0.868 for 3-bit, and 0.596 for 2-bit. Those figures describe that example’s setup, not a guaranteed result for another embedding model or dataset. The documentation recommends starting with 4-bit when recall matters and validating 2-bit against target embeddings.
Benchmark the workload on the VPS you intend to use
Use a representative copy of the intended dataset and application. A smaller toy test may help catch integration problems, but it cannot establish how millions of vectors behave under the target memory limit or storage conditions.
- Record the machine and software. Note the VPS plan and region, actual memory limit, storage type and available capacity, operating system, SQLite build, extension name and pinned version, and application runtime. Confirm what the plan costs and includes directly with the provider; the benchmark evidence does not verify a current $5 offer.
- Describe the data and query. Record vector count, dimensions, representation, embedding source, distance metric, expected write/update pattern, concurrency, and the value of
kused for results. Use representative queries rather than only convenient examples. - Establish a recall baseline. Compare the proposed approximate or quantized method against exact results on the same vectors and queries. Set an acceptable recall target for the application before tuning; do not treat a project benchmark’s Recall@20 as your own result.
- Measure both warm and cold behavior. Separate repeated queries against a warmed cache from tests that require uncached reads. Record latency distributions, not just the fastest query. Include index or data loading and the expected write path where relevant.
- Measure peak resource use during realistic load. Track service-level and whole-VPS memory while running the application, database, index build, and concurrent queries. Include the period of highest expected load, not just idle startup.
- Repeat after changing one major choice. Compare representations or search modes using the same workload. Record on-disk size, build or training duration, recall, latency, and memory together so a memory reduction is not mistaken for an overall improvement.
This test is necessary because the cited documentation does not establish performance for the exact combination of a $5 VPS, 2GB RAM, and millions of vectors. No vector-count threshold can replace measurements of your dimensions, representation, query mix, and host.
Quick Recap
What to change if the test fails
- If memory is the limiting factor, try a more compact vector representation or a documented streamed mode, then recheck recall and latency. Reduce stored dimensions or the amount of data only if doing so preserves the application’s requirements.
- If cold queries are too slow, determine whether storage reads are the bottleneck and test a workload or search strategy with an acceptable balance of cached data and disk access.
- If approximate or quantized results miss the recall target, test a less aggressive representation or another supported search method against the exact baseline.
- If index construction or updates dominate, include that cost in the decision rather than measuring query speed alone. Training and insertion costs can be substantial and dataset-specific.
- If no tested configuration meets memory, recall, and latency requirements, the evidence does not justify forcing the workload onto this VPS. Revisit the data volume, dimensions, workload, or the choice of storage/search system.
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