Portkey or Continual? A Comprehensive Review

Full-stack ops platform

Manages models

prompts

parameters

Live logs and analytics

Optimizes usage and API costs

State-of-the-art privacy architectures

Uptime SLAs

Proactive alerts on issues

Streamlines deployment of LLM APIs

Cloud-based predictive modeling

Uses SQL for app creation

Works with BigQuery

Snowflake

Redshift

and Databricks

No need for complex infrastructure

Models improve continually

Data and models stored on warehouse

Easily accessible to operational and BI tools

Not fully certified yet (ISO:27001

SOC2

GDPR)

Single vendor lock-in

Depends on third-party APIs

Only web-based interface

No listed third-party integrations

Possible extra latency

Smart caching might limit flexibility

Uptime not more than 99.9%

SQL-centric

Limited to cloud data platforms

Dependency on modern data stacks

No MLOPS infrastructure

Limited extensibility (Python only)

Dependent on dbt compatibility

Not suitable for traditional data management systems

Data must be on the same warehouse

No mention of multilingual support

Dependent on continuous access to data warehouse