Best vector database and search
for vibe-coded apps
Updated 2026-09-18. Pinecone is the pick for most projects. Here is how the options compare, and how to run whichever you choose through a single Squiid key.
Short answer
- Our pick: Pinecone. Fully managed, no index tuning, and a small API surface that agents get right. When you have outgrown Postgres for retrieval, this is the least work.
- Runner-up: Qdrant Cloud. Open source underneath, strong filtering alongside vector search, and you can self-host later without a rewrite if cost or data residency changes the calculus.
- Pick something else when: Algolia, Typesense or Meilisearch when users are typing queries into a search box and expect typo tolerance and instant results rather than semantic similarity, Turbopuffer when the corpus is large and you care about cost per stored vector, and plain Postgres with pgvector when you have fewer than a million vectors, which is most projects.
Most vibe-coded apps that think they need a vector database need pgvector in the Postgres they already have. Add a dedicated service when retrieval quality, index size or write throughput actually hurts. When that day comes, Pinecone is the least work, and keyword search is a different product entirely.
How to choose a vector database and search for an AI-coded app
Semantic similarity or keyword lookup?
Vector search finds things that mean something similar. Keyword search finds things that contain the words the user typed, tolerates typos and returns in a few milliseconds. A product catalogue needs the second. A documentation assistant needs the first. Many apps need both, combined.
How many vectors, really?
Under roughly a million embeddings, Postgres with pgvector is fast enough and costs you nothing extra. Between one and a hundred million, a dedicated vector service earns its price. Estimate honestly, because premature migration here is a common way to add a service you did not need.
Metadata filtering
Real queries are almost never pure similarity. They are similarity within a tenant, a date range or a document set. Check how the provider combines filters with vector search and whether filtered queries stay fast, because implementations vary a lot.
Who computes the embeddings?
The vector store usually does not. You call an embedding model, then write the vectors. That means a model provider bill alongside your index bill, and it means changing the embedding model requires re-embedding the whole corpus. Pin the model version in configuration.
Cost per stored vector while idle
Vector services bill for stored dimensions whether or not anyone queries. A parked project with ten million embeddings is a monthly charge for nothing. Prefer serverless index pricing for bursty projects, and run it on credits that pause rather than a subscription that does not.
Compare the options
Every vector database and search option Squiid carries in the Vector and search category, with what each one is best at and how it bills.
| Service | Best for | Pricing | On Squiid |
|---|---|---|---|
| Algolia | Hosted search with typo tolerance, ranking rules and instant UI kits | Pay as you go $0/mo, usage | Roadmap |
| Meilisearch | Fast open-source search that is easy to configure, with AI search built in | both | Roadmap |
| Pinecone | Managed vector database for retrieval augmented generation at scale | both | Next up |
| Qdrant Cloud | Open-source vector search with rich payload filtering and quantization | both | Roadmap |
| Turbopuffer | Vector and full-text search built on object storage for low cost | usage | Roadmap |
| Typesense | Open-source instant search with a managed cloud and vector support | both | Roadmap |
| Weaviate | Vector database with built-in vectorization modules and hybrid search | both | Roadmap |
Why run your vector database and search through Squiid
One login instead of one per vendor. Squiid carries 7 vector database and search options here and every other layer of the stack beside it. You create one account, click the service you want, and Squiid provisions it upstream. No separate signup, no separate password, no second dashboard.
One bill, at the provider's price. Usage is paid from prepaid Squiid credits at par: $1 of credit is $1 at the provider, with no markup on what the provider charges. The free plan is $0/month with a 15% fee on credit top-ups and a $50 minimum top-up. Solo is $19.97/month and its top-up fee starts at 8%, falling to 6.5%, 5% and 3.5% as trailing 30-day API spend passes $100, $500 and $2,000. Team is $29.97 per seat per month for seats 1-5, $24.97 for seats 6-20 and $19.97 above that, with the same ladder one point lower. Custom pricing is 3% or less against a commitment. Services that are sold as a monthly subscription, such as Pinecone's paid plan, pass through on your Squiid invoice at the provider's own price with no fee on top.
Squiid holds the upstream key. The provider credential lives in Squiid, not in your repo, not in a chat window and not in a screenshot. Your project gets one variable, SQUIID_API_KEY, and that is the only secret an agent ever needs to see. Rotating it is one click and does not touch the provider.
It works with every coding agent. Point the SDK base URL at https://api.squiid.io/v1/<service>/ and send Authorization: Bearer $SQUIID_API_KEY. Claude Code, Cursor, Codex, Gemini CLI, Copilot, Windsurf, Cline, Devin, Replit, v0 and Lovable all read environment variables the normal way. See the gateway convention.
Running out pauses, it never deletes. Squiid warns you at 75%, 90% and 100% of your balance. At zero, calls pause instead of overdrawing, and your data stays where it is. Auto top-up is optional and takes a hard monthly cap. See spend controls.
Set it up in 3 steps
Add Pinecone in the dashboard
Create a Squiid account, open the catalogue, and click add on Pinecone. Squiid creates the upstream account or project for you and holds the credential.
Put one variable in .env
Copy your Squiid key and add SQUIID_API_KEY=sq_live_… to your project's .env. Delete any provider keys you were carrying. Nothing else changes.
Point your agent at the gateway
Tell your agent that calls go to https://api.squiid.io/v1/pinecone/ with Authorization: Bearer $SQUIID_API_KEY. Most SDKs take a base URL option, so it is a one-line change.
In practice that is one line in your project:
SQUIID_API_KEY=sq_live_xxxxxxxxxxxxxxxxxxxxThen every call your app or your agent makes goes to the gateway with that one key:
curl https://api.squiid.io/v1/pinecone/... \
-H "Authorization: Bearer $SQUIID_API_KEY"Questions people ask
Do I need a vector database for a RAG app?
Not at first. Postgres with the pgvector extension handles retrieval well up to around a million vectors, which covers the great majority of vibe-coded apps. Add Pinecone or Qdrant when index size, write throughput or recall quality becomes the bottleneck.
Pinecone or Qdrant?
Pinecone for the least operational work and the simplest API. Qdrant Cloud when you want rich filtering with vector search, or you want the option to self-host the same engine later. Both are on Squiid under one key, so testing the second costs no extra setup.
What is the difference between Algolia and a vector database?
Algolia is keyword search built for a search box: typo tolerance, ranking rules, facets and instant results. A vector database finds semantically similar items for retrieval augmented generation. They solve different problems and plenty of apps run both.
Which embedding model should I use?
Any current embedding model from OpenAI, Google or Cohere is fine for a first version. What matters is pinning the model and dimension in configuration, because changing it means re-embedding everything. All three are available through the same Squiid key.
How do I keep retrieval costs predictable?
Run both the embedding calls and the index through Squiid credits. Usage draws down a prepaid balance at provider prices, you get alerts at 75%, 90% and 100%, and at zero the calls pause instead of accruing a bill.
One key for your vector database and search.
And everything else.
Add Pinecone and 107 other services to one account, one key, one bill and one dashboard.