Why BaseKV Exists

Most databases are built for extremes. Either you're at hyperscale, pushing millions of requests per second, or you're tiny enough to get by with SQLite and a prayer. The middle is oddly underserved.

That's where BaseKV sits.

The Gap

If you've worked with Redis, you know the workflow: set a key, get a key, maybe use a few data structures. It's fast, familiar, and ubiquitous. But persistence is an afterthought. You can enable RDB snapshots or AOF logs, but the default posture is ephemeral. Most managed Redis services charge you for memory, not disk. When your dataset grows past a few gigabytes, costs climb fast.

DynamoDB goes the other direction. It's durable by default, scales to absurd levels, and has pricing that rewards throughput planning. But the API is verbose, the query patterns take time to learn, and the vendor lock-in is real. Exporting your data means setting up pipelines or writing custom scripts.

Between these two, there's a long, quiet middle. That's where most workloads live.

The Long, Quiet Middle

Most teams aren't running Netflix. They're building SaaS products, internal tools, or mid-scale applications with predictable traffic. Their datasets are measured in gigabytes, not petabytes. They need persistence, but not infinite scale. They want familiar APIs, not a new query language.

These workloads have a few things in common:

• They're steady. Traffic doesn't spike by 10x overnight.
• They're persistent. Data needs to survive restarts.
• They're budget-conscious. Paying for unused capacity hurts.
• They value simplicity. Infrastructure should fade into the background.

This is what BaseKV is built for.

What BaseKV Offers

Disk-first persistence. Every write goes to disk immediately. No eviction policies, no surprise data loss, no scrambling to configure AOF before you realize you needed it. Your data is there when you need it.

Flat pricing. You pick a tier based on storage and request volume. No per-read charges, no throughput math, no bill anxiety. If your workload stays steady, your costs stay steady.

Redis-compatible protocol. If you've used Redis, you already know how to use BaseKV. Drop in a new connection string, keep your existing code. No rewrite required.

Easy exports. Pull your data as JSON or CSV whenever you want. No support tickets, no pipeline setup, no waiting. Your data is yours.

Who It's For

BaseKV is built for small-to-mid scale applications. If you're serving a few thousand users, handling a few hundred requests per second, and storing a few dozen gigabytes, you're in the sweet spot.

Common use cases:

• Materialized views. Precompute expensive queries and serve them instantly.
• Session storage. Persist user sessions without holding everything in memory.
• Configuration data. Store feature flags, settings, and app state with low write churn.
• Edge caching. Keep regional key-value layers close to users.

If you care more about predictability than peak throughput, BaseKV will feel like home.

Who It's Not For

BaseKV isn't built for hyperscale. If you're pushing millions of requests per second, need global replication with sub-10ms consistency, or want complex secondary indexes, look elsewhere.

It's also not a replacement for relational databases. If your queries involve joins, aggregations, or complex relationships, stick with Postgres.

And if you genuinely need in-memory-only performance and don't care about persistence, raw Redis is still faster.

BaseKV is for the middle. If that's where you are, it'll fit.

How We Think About It

Databases should be boring. They should start up, stay up, and get out of your way. You shouldn't need to tune cache eviction policies, plan throughput capacity, or debug replication lag.

BaseKV follows a few principles:

• Persistence by default. Disk-first means you don't have to think about it.
• Flat pricing. No surprises, no math, no gotchas.
• Open exports. Your data isn't held hostage. Pull it anytime.
• Familiar APIs. Redis-compatible means you already know how it works.

Infrastructure should fade into the background. Your database shouldn't be the interesting part of your stack.

Closing

BaseKV exists because the middle deserves better tools. You shouldn't have to pay for hyperscale features you'll never use, or settle for ephemeral storage when you need reliability.

If your workload is steady, your budget is real, and you want infrastructure that just works, BaseKV is built for you.

Try it out. Your data will still be there tomorrow.