Cloudant vs Elastic

What Cloudant is

IBM Cloudant is a proprietary managed JSON document database built on Apache CouchDB, with a compatible HTTPS API and replication protocol. The Lite plan is free for evaluation and fixes capacity at 20 reads, 10 writes and five global queries per second with 1 GB storage. Standard begins at one provisioned capacity block—100 reads, 50 writes and five global queries per second—includes 20 GB and bills capacity plus excess storage hourly. Dedicated Hardware adds workload isolation, private networking and selected compliance and key-management options. A Cloudant service is not the same as self-hosting CouchDB.

What Elastic is

Elastic builds on Elasticsearch to provide search, log analytics and observability, available self-hosted or as a managed cloud. The same engine backs product search, log analytics and security use cases, so one cluster can serve teams that would otherwise buy three tools. The trade-off is operational: index design, shard sizing and retention decide whether the cluster stays affordable.

What Cloudant is built to do

JSON document database

Stores schema-flexible documents, attachments and secondary indexes behind HTTPS APIs.

CouchDB-compatible replication

Supports replication patterns between Cloudant regions and compatible CouchDB systems.

Provisioned throughput

Scales read, write and global-query capacity in linked blocks through the dashboard or API.

IBM IAM and SDKs

Integrates IBM Cloud access controls and provides supported Java, Node.js, Python, Swift, Go and mobile clients.

What Elastic is built to do

Full-text search

Indexes documents for relevance-ranked queries with analyzers and synonyms.

Log and metrics analytics

Ingests operational data and queries it through Kibana dashboards.

Self-hosted or managed

Runs on your own infrastructure or as a managed cloud deployment.

Choose Cloudant if

• Offline or distributed applications that benefit from CouchDB-style replication
• JSON workloads dominated by key lookups and well-designed partitioned queries
• Teams able to test request-class capacity, 429 handling and multi-region conflicts

Skip Cloudant if

• The application depends on relational joins, broad ad-hoc SQL or multi-document ACID transactions
• Traffic is too unpredictable to provision and monitor request-class capacity
• A free Lite instance is being treated as a production SLA or scalable datastore

Choose Elastic if

• Teams that want managed Elasticsearch without capacity planning — Serverless bills by VCU-hours and GB retained
• Search or logging workloads that need a supported cluster with 24/7 support and a 99.95% SLA (Platinum and up)
• Engineers who want to start self-managed for free and move to Cloud later on the same APIs

Skip Elastic if

• You need a flat monthly bill: Hosted 'as low as $99/month' assumes a 120 GB, 2-zone config and scales with usage
• Machine learning or cross-cluster replication is required — that starts at Platinum, from $131/month
• You just need basic search on a small app and cannot justify a managed cluster's floor price

Cloudant: pros & cons

• Managed document service: IBM handles database servers, upgrades, patching and core high-availability infrastructure.
• CouchDB lineage: Compatible HTTP APIs and replication ease selected CouchDB application and migration patterns.
• Explicit capacity model: Read, write and global-query throughput are provisioned in documented blocks.
• Multi-region option: Application-managed replication can support active-active regional architectures.
• Lite is evaluation capacity: It cannot scale and blocks new writes above its storage limit.
• Capacity dimensions scale together: A workload dominated by one request class may pay for unused allowances in the others.
• Exceeding throughput returns 429: Applications need retry, backoff and load testing rather than assuming automatic unlimited scale.
• Cross-region is not automatic application resilience: Conflict behavior, traffic routing, monitoring and recovery remain design responsibilities.

Elastic: pros & cons

• Extremely capable search and aggregation
• One stack for logs, metrics and search
• Large ecosystem and documentation
• Operationally demanding at scale
• Licensing changes have caused confusion
• Resource-hungry compared with focused tools