What is the best real-time database in 2026?

There is no universally best real-time database because there is no universal definition of "real time." Agentic AI workloads, which fan out into many database calls per task, are increasingly important. To a trading firm, real time means single-digit milliseconds. To a product team shipping a live dashboard, it means the number on the screen updates within a few seconds of the event that produced it. To a Firebase developer, it means data is pushed to connected clients as soon as it changes. 

Adopting a real-time database without defining the term first is a common problem. A team picks a database because it's marketed as real time, discovers three months later that "real time" meant something narrower than what they needed, and buys new software under pressure. 

Before shopping for a real-time database, define the way the term is being used for a specific workload.

What counts as real time

Most disagreements about "real time" are really disagreements between a hard deadline that must never be missed, a system that processes and reacts to data as it arrives, or a system that's fast enough that a human doesn't notice the delay. These three problems each use a different architecture.

• A real-time database is designed to make current data available to applications within a defined latency or freshness target.

• A real-time operational database is designed for low-latency reads and writes against application state, often under high concurrency.

• A real-time analytics database is designed to ingest and query continuously changing data with low query latency, typically for aggregations, dashboards, monitoring, and event analysis.

Latency requirements are often grouped into three practical bands, though the exact boundaries are working guidelines rather than a universally agreed standard; different industries and SLOs draw the lines in different places. A commonly used grouping looks like this:

• Ultra-low latency: under 10 milliseconds end-to-end, the territory of high-frequency trading and real-time control systems where one availability zone and specialized hardware are typically required

• Low latency: roughly 10 to 200 milliseconds, covering interactive dashboards, alerting, and online machine learning features

• Latency-relaxed, spanning 200 milliseconds out to several minutes, covering most "near-real-time" analytics, ETL, and reporting workloads

Real-time web features are typically built on a multi-tier architecture, such as a display tier in the browser, a facade or service tier, and a data tier, with the service tier pushing updates to the browser over a mechanism such as WebSockets as soon as the data tier changes. That delivers fast, push-based synchronization, but no tier in that stack offers the predictable, bounded worst-case response that real-time computing requires: network conditions, service-tier load, and browser rendering each introduce variable delay. When people say a web app has a "real-time database," they almost always mean fast, synchronized replication, not a hard deadline guarantee.

Hard real-time systems are defined by predictable timing behavior and bounded worst-case response, such as anti-lock braking controllers, aircraft flight-control computers, industrial robotic control loops, or pacemakers, where missing a deadline counts as a system failure, not just degraded performance. That guarantee typically requires a real-time operating system and a controlled execution environment, not simply specialized hardware on its own. Hard real time is a narrow, deadline-bound category that most software teams don't need, because it requires an execution environment most stacks don't have.

More typically, "real time" is a freshness and latency target chosen for a specific workload, and the target is best set in milliseconds. A dashboard that refreshes every 30 seconds and a fraud check that has to respond in under 50 milliseconds may both be described as "real time" by their owners, but they need different databases underneath them.

That distinction is the first factor to nail down before evaluating anything else, because it influences the decision. Sub-10-millisecond requirements are a small category. Requirements between 10 and 200 milliseconds allow more options. Anything looser than that raises the question of whether a real-time database is needed at all.

The five types of real-time database workloads

Real-time database workloads fall into five categories, and each category has different requirements. These workloads should not be evaluated using the same database benchmark, because a benchmark built for one category says little about performance in another.

Which real-time database should you choose?

This decision rubric is a starting point based on workload requirements, before factoring in team expertise, existing infrastructure, and cost.

Do you need transactional reads and writes?

• Yes, and predictable sub-10-millisecond latency is important → Evaluate Aerospike and other low-latency operational databases.

• Yes, but sub-10-millisecond latency is not required → Evaluate DynamoDB, distributed SQL, and other operational databases.

Do you primarily analyze large volumes of event data?

• Yes → Evaluate ClickHouse, Apache Druid, Apache Pinot, StarRocks, and similar analytical engines.

Do you need both transactions and analytics in the same system?

• Yes → Evaluate HTAP or distributed SQL systems such as TiDB and SingleStore.

Does the workload involve an AI agent making multiple tool calls, retrieval steps, or sub-requests per user action?

• Yes → Treat it as a fan-out transactional workload, not a caching or analytical workload. Evaluate Aerospike and other operational databases with predictable tail latency under concurrent load; be skeptical of memory-first, cache-reliant databases.

This decision tree gives a starting shortlist, not a final answer. Use the evaluation criteria later in this article — ingestion throughput, tail latency, consistency, concurrency, multi-region behavior, and cost — to choose within the shortlist.

Agentic AI and fan-out workloads

AI models make decisions. Data platforms determine whether those decisions arrive in time to matter.

A growing share of real-time database traffic in 2026 comes from an AI agent completing a task: calling multiple tools, retrieving context from several sources, and making a chain of sub-decisions before returning an answer. That changes what "real time" requires, and it changes which databases are a good fit. Competitive advantage increasingly comes from fresher data, better context, faster retrieval, and predictable infrastructure, not from model reasoning capability alone.

From sequential lookups to parallel fan-out

Legacy AI systems and agentic AI systems query data differently. A legacy system typically runs one coordinator that issues lookups sequentially: lookup one, then lookup two, then lookup three, and so on, until a final lookup produces a response. Total latency is the sum of each sequential lookup, which is easy to reason about and easy to budget for.

An agentic AI system replaces that one coordinator with a coordinator that delegates to multiple sub-agents, each of which can issue its own lookups in parallel. The coordinator initiates a parallel lookup across several sub-agents at once, then waits for all of those responses before aggregating them into an answer. This moves the bottleneck from the sum of sequential steps to the slowest of the parallel ones. The coordinator doesn’t respond until every sub-agent has returned, so one slow lookup among many holds up the entire response.

Two points follow from this change, and both increase the pressure on the underlying database rather than the model. 

1. Agentic AI produces more parallel data lookup requests per user action than a legacy system did, because reasoning steps that used to be internal to a model call now dispatch to multiple sub-agents that each need their own data.

2. The SLA relationship to the end user remains constant. A person asking an agent a question still expects an answer in roughly the same amount of time they always have, even though the number of parallel lookups behind that answer has gone up. More parallel lookups against an unchanged deadline makes it harder to meet latency requirements at the database layer, because the deadline has to be met by whichever lookup is slowest, not by any one lookup or by the average.

Why agentic AI workloads behave like fraud detection

One agentic AI request typically fans out into many database requests: one per tool call, one per retrieval step, one per sub-agent. This fan-out is more like a fraud-detection pipeline rather than a typical web request. A fraud check waits on multiple fast lookups before returning one decision; an agentic AI workflow waits on multiple fast lookups before returning one response. In addition, there are often more of them, and they are growing as knowledge graphs and multi-modal context become more widely used. Both are decision paths that succeed or fail as a group, not as individual queries.

For fan-out workloads, average latency is less informative than tail latency distribution. The relevant question isn't "how fast is a typical request," but "how often does at least one request in the fan-out land in the slow tail."

Tail latency amplification under fan-out

Because the coordinator waits for all parallel responses before aggregating, the workflow's latency is determined by the slowest call in the fan-out, not the average one. If one database call has a 1% chance of landing above a given latency threshold, which is defined as  P99, and an agent step fans out to 50 such calls before it responds, the probability that at least one of those 50 calls lands above the threshold is roughly 40%, not 1%. The effective latency profile of an agentic workflow is always worse than the P99 of any individual call it depends on, and it gets worse as knowledge graphs grow and sub-agent counts increase. This is why measuring P95, P99, and P99.9 under realistic concurrency rather than average latency matters even more for agentic AI than for a typical transactional workload. A database that looks acceptable on average, but has an occasional multi-hundred-millisecond outlier, turns a five-call agent workflow into a routinely slow one, and a fifty-call workflow into a consistently slow one.

Why agentic AI workloads are largely uncacheable

Caching helps when a workload has a hot working set: a relatively small number of records that get requested repeatedly, so a cache layer handles most of the read traffic and only a few requests fall through to slower storage. 

Agentic AI access works differently, and larger knowledge graphs and multi-modal inputs make the problem worse over time. Higher-quality context and more comprehensive results both mean pulling from a wider set of records per request. Each agent session pulls context tied to a specific user, document, conversation, or task, which means a wide, long-tail set of records accessed once or a few times rather than a small set accessed constantly. That keeps cache hit rates low.

Memory-first, cache-reliant architectures, such as Couchbase, are built around the assumption that a cache handles most read traffic and that cache misses are the exception. Under wide, largely uncacheable access, a much larger share of requests fall through to the slower backing store. The system's average-case benchmark numbers, measured against a cache-friendly workload, don't hold up against the requests an agentic AI application generates.

Aerospike's patented Hybrid Memory Architecture doesn't depend on a cache hit to stay fast. Every read is a direct, one-hop lookup through the in-memory index to the record's location on disk, regardless of whether that specific record was recently accessed. That makes Aerospike's latency profile less sensitive to access-pattern randomness than a cache-dependent architecture, which matters most for agentic AI's wide, uncacheable, parallel fan-out.

Five misconceptions about real-time databases

1. Treating "real time" as a synonym for "fast." The defining feature of a real-time system is meeting a deadline or preserving the temporal validity of data, not having low average latency. Throughput and latency are different, and neither guarantees the other: a database processing 10 million transactions per second is not "real time" if individual transactions take 10 seconds to complete. Even judged purely on latency, a system that looks fast on average still fails a real-time requirement if its worst-case behavior is unpredictable.

2. Conflating near-real-time with real-time, treating anything faster than a nightly batch job as the same category. A system refreshing every few minutes and a system responding in single-digit milliseconds are solving different problems with different tools, even though both may get called "real time."

3. Assuming that a web-facing "real-time" database comes with the guarantees of real-time computing. No serious application runs a database inside a browser; the typical architecture is multi-tier. That delivers fast, push-based synchronization, not deadline-bound processing, because no tier in that stack offers a bounded worst-case response time.

4. Assuming eventual consistency is acceptable everywhere, without checking whether the specific workload tolerates it. For a product catalog or a content feed, eventual consistency is a reasonable tradeoff. For anything involving money, inventory counts, or other values where a stale read produces an incorrect decision, eventual consistency introduces bugs that show up under load.

5. Assuming in-memory storage is the only path to real-time speed. This assumption affects how many teams design for latency, and it's a factor in maintaining predictable tail latency, but flash-based and hybrid architectures can meet the same latency targets.

A benchmark showing fast average latency doesn't address the deadline-versus-speed confusion. A "real-time" label on a web SDK doesn't imply real-time operating system-grade guarantees. A consistency model described as "eventually consistent by default" is a design decision with potential problems under some conditions, not a universal default that's safe everywhere.

The SLA test for whether you need real time

Most systems have only a few latency-critical flows and a much larger number that only look time-sensitive because they were built inside a streaming platform by default. The SLA test is a path-by-path audit: Walk through each data path and ask what happens if this path is 30 seconds stale. If the answer is "nothing," that path isn't a real-time requirement.

The fix for over-engineered real-time infrastructure isn't to avoid real-time infrastructure altogether, but to treat "real-time" as a per-workload service-level agreement set deliberately, rather than an architecture adopted wholesale. Once the path-by-path audit is done, workloads that remain time-sensitive need real time, while the rest can run on something simpler and cheaper.

Decision criteria that matter

Different workloads rely on different criteria; a leaderboard has different requirements from a fraud-detection pipeline. Consider each criterion deliberately rather than uniformly.

How important is ingestion throughput in a real-time database?

Ingestion throughput determines whether the database sustains the peak write volume a workload produces. A system that handles 10,000 writes per second comfortably on a quiet Tuesday may not handle a traffic spike if it wasn't designed for bursty load. A database that can't keep up with ingestion isn't going to be real time, regardless of its query speed.

Why does tail latency matter more than average latency?

Average latency is a common benchmark but doesn't work for evaluating latency-sensitive workloads. A system can post an excellent average response time while still failing some requests enough to give users a bad experience.

Consider a database with a p50 latency of 10 milliseconds, a p95 of 20 milliseconds, a p99 of 40 milliseconds, and a p999 of 900 milliseconds, which is common under contention, garbage collection, or queueing effects. Judged one call at a time, that looks fine: 99% of individual calls finish in 40 milliseconds or less. Now put that same database behind a user query that fans out to 100 concurrent calls. Assuming the calls are independent, the user's wait time is set by whichever of the 100 calls is slowest, not by any individual call's percentile:

• About 0.6% of users see 20 milliseconds or less

• About 36% see between 20 and 40 milliseconds

• About 54% see between 40 and 900 milliseconds

• About 9.5% see more than 900 milliseconds

In other words, more than 63% of users land in the tail (40 milliseconds or worse) even though only 1% of individual calls do. Run those same 100 calls sequentially instead of concurrently, and the picture gets worse in a different way: the wait time stretches into multiple seconds, because even the median alone adds up to a full second before tail effects are added. This is why percentile-based evaluation, not averages, is the right metric for any workload with more than a handful of database calls behind one user-facing response.

High percentiles such as P95, P99, and sometimes P999 are generally more informative than the mean for these workloads, because percentiles show tail behavior that isn't obvious from an average. Which percentile matters most depends on the workload; P99.9 may not be statistically reliable if there are only a few requests, and a fast database-level P99 says little if the surrounding application path adds seconds of its own latency elsewhere.

Rigorous benchmarking methodologies for real-time analytics databases typically instruct readers to measure p50, p95, and p99 latency across increasing levels of concurrency, because a system that looks fine at low load responds differently under load.

The tail-latency question follows the same logic as the SLA test: What happens if the slowest 1% of requests take five times longer than average? For a background job, probably nothing. For a real-time bidding decision or an API call blocking a page render, a slow tail request is functionally the same as an outage. Once a data path's category is known, the next step is setting a concrete percentile ceiling and holding every candidate database to it under realistic concurrent load.

A tail-latency problem doesn't always look like a resource problem. Queueing, contention, scheduling behavior, network conditions, and internal synchronization produce latency spikes even when CPU, memory, and disk utilization look acceptable on a dashboard. Tail latency needs its own monitoring and its own service level objective, separate from both the average and aggregate resource graphs.

What consistency model does a real-time workload need?

A consistency model is one of the decisions most likely to cause problems when it's chosen without a workload-specific analysis.

Eventual consistency means a read doesn't immediately reflect the most recent write, which lets an architecture favor availability, lower coordination overhead, or both, depending on the implementation.

Strong consistency offers stronger ordering and visibility guarantees, though the exact guarantee still depends on the specific mode a database offers; "strong consistency" isn't always the same thing across vendors.

Not every consistency-sensitive workload needs strong consistency end to end. Systems routinely mix models within the same feature, using eventual consistency for low-stakes reads and strong consistency only where a stale value would make the business operation incorrect. If a stale value produces a wrong outcome, the decision path needs a consistency model that rules out the stale-read behavior the application can't tolerate. That matters even more once a system spans more than one region, because consistency and multi-region architecture become tightly coupled.

How does concurrency affect real-time database performance?

A database's behavior under one connection doesn't predict how it behaves under typical concurrent load. Testing at realistic concurrency, with hundreds or thousands of simultaneous connections, is the only way to see whether latency holds steady or degrades as load increases.

How should you evaluate multi-region real-time databases?

Once a system spans more than one region, consistency becomes a bigger question, raising the theorems of CAP and PACELC.

• CAP describes the trade-off between consistency and availability during the inevitable network partition.

• PACELC extends that idea to normal operation: if a Partition (P) occurs, a system trades off Availability (A) against Consistency ©. Else (E), during normal operation with no partition, it trades off Latency (L) against Consistency (C). 

CAP describes what a database does during an outage; PACELC adds that even without an outage, a database still has to trade some consistency for speed, or some speed for consistency, as a standing design choice.

Aerospike's session consistency (SC) means clients see an increasing sequence of their own record versions as the default SC read mode. A stricter global linearizability guarantee, with every client seeing the same, most-recent version, is available when selected per request. That strong-consistency guarantee was originally scoped to one record at a time, which mattered for how an application had to be modeled around it. 

Aerospike 8, released in 2025, extended this with distributed ACID transactions that add strict serializability across multiple records, built on the same strong-consistency foundation. Multi-record atomicity is available as a result, but it runs as a distinct transaction layer with its own overhead of roughly four extra writes and one extra read per transaction, rather than being the default behavior of every read and write.

Microsoft Azure Cosmos DB offers strong consistency across regions, but only in a single-write-region configuration: One region accepts all writes, and Cosmos DB synchronously replicates and confirms each write across every other region before acknowledging it, which is what makes a single, strongly consistent global order possible. It adds a latency cost of roughly twice the round-trip time to the farthest region. 

A multi-region-write configuration works differently. Multiple regions can each accept writes independently and acknowledge them locally, propagating changes to other regions asynchronously afterward and reconciling conflicting writes after the fact, such as with a last-write-wins rule. Because two regions can accept conflicting writes on the same item at the same time without coordinating first, there's no single global order for a client to read, which is what strong consistency requires. 

That's why Azure Cosmos DB does not support combining strong consistency with a multi-region-write configuration: the two options solve for opposite priorities, low-latency independent regional writes versus one guaranteed global order. "Multi-region" and "multi-region writes" are two different things, and the difference determines whether a given deployment can use strong consistency.

DynamoDB added a comparable feature, Multi-Region Strong Consistency (MRSC) for Global Tables, in mid-2025. MRSC synchronously replicates a write to another region before acknowledging it, at the cost of higher write and read latency compared with the eventually-consistent mode Global Tables also offer. DynamoDB supports multi-region strong consistency for tables and Local Secondary Indexes, but Global Secondary Indexes remain eventually consistent only, which is why consistency needs to be checked per access path.

Even where multi-region strong consistency is available, synchronous cross-region replication adds network latency to every write. Evaluating a candidate database means asking what happens to reads and writes during a regional outage or network partition under the consistency configuration being evaluated, and what the latency cost of that configuration is at the deployment's network distances.

How much does a real-time database cost at scale?

Consistency choices carry a cost. DynamoDB's strongly consistent reads on tables and LSIs use twice the read capacity of eventually consistent ones, so the safer, more predictable choice for money-sensitive data costs double. That’s worth knowing before defaulting to it everywhere out of caution. DynamoDB's multi-region strong consistency (MRSC) mode adds latency and infrastructure cost on top of that, a tradeoff to weigh against the resilience it buys.

What role does operational complexity play in choosing a real-time database?

Operational complexity covers how much work a given system adds, and whether the team evaluating it has the expertise to run it. SQL support, driver and BI-tool compatibility, and the learning curve of an unfamiliar query language affect how quickly a team becomes productive. Self-hosted, multi-component clusters are more work than one managed service, and that work costs money even when it doesn't show up on a line item the way per-operation billing does.

Matching the database to the use case

Every criterion matters differently depending on the workload. Here are some typical examples.

Best database for real-time leaderboards

Primary requirements:

• High read concurrency

• High concurrent write throughput, including synchronized update bursts

• Steady latency under concurrent load

• Loose freshness tolerance

Typical latency target: Low milliseconds for reads and writes; a few seconds of staleness on rank display is usually invisible to end users.

Database characteristics: Read-heavy in steady state, but needs to absorb sharp write bursts, such as a massively multiplayer game where many matches end within the same few seconds, each triggering a score update. Atomic update primitives, such as atomic increments or sorted-set operations, matter here because they let a score update happen without a separate read-modify-write round trip, which avoids contention when many players' scores change at once.

Strong candidates: Aerospike, DynamoDB, and other low-latency key-value databases with strong concurrent-read performance and atomic update operations that hold up under concurrent write bursts.

Poor fit: Systems that are designed for single-query speed but degrade under high concurrent read load, or that require a read-modify-write cycle for every score update and so serialize contention when write bursts hit.

Why: Ranking position rarely needs sub-millisecond accuracy, so the database that wins here is the one that holds steady latency under high concurrent read load, not necessarily the one with the fastest single-query benchmark. The write side matters just as much. When a large number of players finish at the same time, such as at the end of a match, a round, or a tournament, score updates arrive as a synchronized burst rather than a steady trickle, and a database that handles reads well but serializes writes, or needs a read-modify-write round trip per update, becomes the bottleneck when load peaks.

Best database for real-time fraud detection

Primary requirements:

• Low tail latency

• High concurrency

• Consistency

• High availability

Typical latency target: Under 50 milliseconds for the decision path; often substantially lower at the database layer

Database characteristics: Transactional operational database, not analytical OLAP

Strong candidates: Aerospike, DynamoDB, and other low-latency distributed operational databases

Poor fit: Columnar OLAP databases intended for scans

Why: Fraud decisions depend on retrieving current state quickly and predictably rather than scanning large datasets. A slow or stale read at the decision step produces either a missed fraud signal or a false decline. Not every read in a fraud pipeline needs strong consistency; plenty of behavioral features and historical aggregates tolerate eventual consistency. The decision path needs a consistency guarantee that eliminates stale reads so it doesn’t make the wrong decision. 

Best database for personalization and recommendations

Primary requirements:

• High read throughput

• Low query latency

• Freshness over strict consistency

Typical latency target: Low milliseconds; a slightly outdated recommendation is rarely a problem

Database characteristics: High-throughput operational database designed for read latency rather than strong consistency guarantees

Strong candidates: Aerospike, DynamoDB, and similar low-latency operational databases

Poor fit: Systems where strong consistency overhead adds latency without a corresponding benefit for this workload

Why: Personalization is more of a throughput and freshness problem than a consistency problem. Reads are high-volume and latency-sensitive, but consistency matters less than in fraud detection, because showing a slightly outdated recommendation rarely causes the kind of harm a stale balance does.

Best database for chat and notifications

Primary requirements:

• Ordering guarantees within a conversation

• Low delivery latency

• High concurrent connection handling

Typical latency target: Low milliseconds for message delivery; eventual consistency is acceptable for auxiliary state

Database characteristics: Real-time application database or message-broker-backed architecture, with ordering enforced at the application or broker layer

Strong candidates: Purpose-built real-time synchronization databases and message brokers, often paired with an operational database for durable state

Poor fit: Databases without native support for ordered delivery or high concurrent connection counts

Why: Chat systems often need explicit ordering guarantees for messages within one conversation, typically through sequence numbers, timestamps, or causal ordering at the application or message-broker layer, rather than through the database's consistency model alone. Auxiliary states, such as read receipts or presence indicators, usually tolerate eventual consistency.

Best database for agentic AI and AI agent orchestration

Primary requirements:

• Bounded tail latency under high request fan-out

• High concurrency

• Resilience to largely uncacheable, long-tail access patterns

Typical latency target: Low milliseconds per call, held steady across a fan-out of tens of concurrent calls per agent step

Database characteristics: Transactional operational database with a latency profile that doesn't depend on cache hit rate

Strong candidates: Aerospike and other low-latency operational databases whose read path doesn't rely on caching to stay fast

Poor fit: Memory-first, cache-reliant databases such as Couchbase, which assume a hot working set that agentic access doesn’t provide

Why: One agent response depends on the slowest of many fan-out calls, so tail latency is amplified across the fan-out rather than averaged out. Agentic access is wide and long-tail,  tied to a specific user, document, or task, which keeps cache hit rates low and doesn’t work with memory-first architectures that depend on a cache handling most read traffic.

The right database for the job

Aerospike is particularly well suited to real-time transactional workloads that require predictable tail latency, high throughput, and strong consistency. These workloads include fraud detection, real-time bidding, personalization, recommendation serving, session management, agentic AI orchestration, and other high-concurrency applications.

ClickHouse is best suited to large-scale analytical workloads involving scans and aggregations over event and log data.

Apache Druid is best suited to streaming event dashboards with known, pre-aggregated query patterns.

Apache Pinot is best suited to user-facing analytical applications that require low-latency queries at high concurrency.

DynamoDB is best suited to AWS-native operational workloads that need managed, simple key-value or document access, with an option for stronger cross-region guarantees where MRSC is enabled.

TimescaleDB is best suited to time-series workloads such as IoT and metrics that benefit from SQL alongside time-series-specific capabilities.

TiDB and SingleStore are best suited to workloads that need both transactional and analytical processing in one system (HTAP), where a distributed SQL interface is a priority.

Aerospike vs. ClickHouse, DynamoDB, and ScyllaDB

Here’s how Aerospike compares with other databases, head-to-head.

Aerospike vs. ClickHouse

Aerospike is generally the better fit for transactional, latency-sensitive point operations. ClickHouse is generally the better fit for large analytical scans and aggregations. The distinction is decisions versus analytical processing: Aerospike answers "what is the current state of this record, right now," while ClickHouse answers "what happened across this large dataset over a given window."

Aerospike vs. DynamoDB

Aerospike is generally stronger when predictable tail latency and high-throughput performance at scale are the primary requirements, and when node count needs to track index size and configuration rather than total data volume. DynamoDB is generally stronger when AWS-native managed infrastructure and simple key-value or document access are priorities, and when a team wants to use managed services for the cluster.

Aerospike vs. ScyllaDB

ScyllaDB positions itself against Aerospike by claiming it delivers predictable performance at a fraction of Aerospike's infrastructure cost by relying on hardware and CPU efficiency rather than a RAM-heavy architecture. Aerospike's default configuration keeps only the primary index in memory, not the record data itself, which is stored on SSD under Aerospike's patented Hybrid Memory Architecture (HMA). The index and the data are different, and the data, which is almost always larger, doesn't need to be in RAM under Aerospike's default hybrid memory design. 

Aerospike vs. Couchbase

Aerospike is generally the better fit for largely uncacheable, high-fan-out workloads such as agentic AI orchestration and fraud detection, where requests are wide and long-tail rather than concentrated on a hot working set. Couchbase is a memory-first, cache-reliant architecture that performs well when a workload has a small, frequently accessed working set that a cache handles. The distinction is dependence on cache hit rate: Aerospike's read path doesn't rely on a cache hit to stay fast, while Couchbase's latency profile degrades as cache hit rate drops, which happens under wide, agentic-style access. 

Real-time database strengths and weaknesses

Aerospike

• Strength: predictable low latency

• Strength: high throughput

• Strength: distributed operational workloads

• Strength: strong consistency options

• Weakness: ad hoc OLAP

Apache Druid

• Strength: streaming analytics

• Strength: time-oriented aggregations

• Weakness: highly mutable transactional workloads

Apache Pinot

• Strength: low-latency queries at high concurrency

• Strength: user-facing analytics

• Weakness: complex ad hoc relational joins

ClickHouse

• Strength: analytical scans

• Strength: aggregations

• Strength: massive event datasets

• Weakness: transactional point updates

DynamoDB

• Strength: managed AWS-native scale

• Strength: simple key-value and document access

• Weakness: limited query flexibility

• Weakness: GSIs are eventually consistent only

Where predictable tail latency comes from

A common assumption is that real-time performance requires keeping all data in memory. Aerospike's architecture shows why that isn’t true. 

A widely referenced example from the Aerospike Developer Blog walked through 700 million user profiles and 1.7TB of data, sustaining 500,000 transactions per second (400,000 reads plus 100,000 writes) at sub-millisecond latency, using under 84 gigabytes of RAM across a six-node cluster. Aerospike's patented Hybrid Memory Architecture (HMA), the default configuration, stores the primary index of location pointers to each record in memory, while the record data itself is stored on SSD. A read becomes a direct, one-hop lookup from the in-memory index to the record's location on disk, rather than a scan or a cache-dependent lookup that might miss.

While HMA is the default, Aerospike's primary index can also be configured to be stored on persistent memory (PMem) or on flash storage instead of DRAM. Aerospike also supports an all-in-memory configuration and an all-flash configuration where both the index and the data are stored on flash. Each namespace within a cluster can be configured independently, so a deployment mixes storage strategies across data sets based on their latency and cost requirements.

Aerospike's Hybrid Memory Architecture keeps only the index, not the much larger record data, in memory, which provides sub-millisecond latency without a fully in-memory dataset. Memory requirements for the primary index scale primarily with record count and index configuration rather than the total bytes of record data, but overall RAM usage on a cluster also depends on secondary indexes, replication factor, write caches, and other database structures. The primary index's memory footprint is the largest piece of that total picture, not the whole picture.

Garbage collection is another contributor to tail latency. Garbage collection causes latency variability in Java Virtual Machine (JVM)-based systems, particularly under allocation pressure that triggers collection activity, though today’s collectors are better at keeping individual pauses short, and garbage collection isn't necessarily the dominant source of tail latency in every JVM-based system.

Compaction is a comparable background-maintenance cost in log-structured merge-tree (LSM-tree) storage engines that write immutable SSTables and periodically merge them, which is the architecture ScyllaDB, Cassandra, and other Cassandra-family databases use. Compaction work competes with foreground reads and writes for I/O and CPU, and a compaction backlog is a well-documented cause of tail-latency spikes in these systems; vendors including ScyllaDB have worked on scheduling and throttling compaction to limit that impact. 

Aerospike isn't exempt from background storage maintenance either: its log-structured storage engine runs a background defragmentation process to reclaim space from overwritten records, and a high defragmentation rate causes latency spikes if left untuned. The architectural difference is in scope, not in the existence of the problem. Aerospike's defragmentation reclaims space without needing to merge multiple sorted files or maintain compaction levels the way an LSM-tree does. But neither architecture eliminates background maintenance as a tail-latency variable; both require it to be actively managed.

Aerospike avoids garbage collection pauses because Aerospike is written in C rather than a garbage-collected language, so its tail latency stays tightly bounded even as nodes age and datasets grow. Many JVM-based systems perform well despite garbage collection; Aerospike's C implementation removes that specific source of tail-latency variance.

Flexible index placement, data commonly stored on flash, and a non-garbage-collected runtime together explain how Aerospike gets predictable tail latency without requiring an all-in-memory dataset.

A note on benchmarks

Database benchmarks are not interchangeable. A benchmark intended for analytical scan throughput should not be used to select a database for sub-10-millisecond transactional decisions. Real-world performance depends on data size, access, concurrency, replication, consistency configuration, hardware, and network topology.

A framework for choosing, not a leaderboard

1. Start by defining the latency class the workload needs, in milliseconds.

2. Run the SLA test against every data path labeled real time, and see which ones would survive being 30 seconds stale.

3. For real-time data paths, name the consistency requirement and the concurrency profile on a path-by-path basis.

4. Check what multi-region behavior looks like during a failure rather than in the steady state.

5. Estimate cost at scale.

Only after these five steps does it make sense to match the result against a specific database comparison.

Five takeaways about real-time databases

1. "Real time" does not describe one workload.

2. Transactional and analytical real-time workloads require different architectures.

3. Tail latency matters more than average latency for deadline-sensitive applications.

4. Strong consistency should be applied where stale data can produce an incorrect decision.

5. The best real-time database is determined by workload, latency target, consistency requirement, and scale,  not benchmark speed alone.

Detailed comparison: analytical vs. operational real-time databases

The table below expands on the workload table above with consistency-model and cost details for teams comparing ClickHouse, Apache Druid, Apache Pinot, DynamoDB, and Aerospike directly.