Performance Optimization Services
Cloud and application performance optimization services. Identify backend and database bottlenecks, test capacity and prioritize changes using measured evidence.
The API that responds in 200ms under light load is a different system under real concurrency.
Systems that were not designed with their access patterns in mind do not get faster by accident. The schema that works at 10,000 users is often actively destructive at one million.
Every read hitting the primary database
No read path optimization for data that is stable and heavily accessed. The primary database handles read volume it was not designed for. Performance degrades gradually, then suddenly.
N+1 query patterns nobody noticed at small scale
Queries that were fast enough at low volume become a problem at scale. The pattern looked fine in development. The database was masking it. Now it surfaces under real load.
Synchronous operations that should be asynchronous
Work that does not need to block the request path does anyway. Users wait for processing that could run in the background. The constraint is not compute; it is architectural.
Stateful sessions that prevent horizontal scaling
Session state tied to a specific instance means you cannot add capacity freely. Scaling requires sticky sessions or a session migration. The application works, but it cannot grow cleanly.
We review against the load profile your system will actually see, not peak theoretical load.
We identify the specific architectural decisions that will constrain you before you hit them, and recommend the right interventions at the right time.
Load profile analysis against realistic projections
We model traffic distribution, data volume, and query patterns over an agreed planning horizon. The scenario reflects credible demand rather than an unbounded theoretical maximum.
Tiered intervention recommendations
Which problems to solve now, which need a measurable trigger, and which should remain alone because the cost exceeds the benefit. That judgment is the output.
Implementation by agreement, not assumption
The assessment does not authorize production changes. Any caching, asynchronous processing, replica, schema or query change needs an agreed work boundary, owner approval and a tested rollback or recovery path. Record the post-change evidence separately from the recommendation.
Application-level. Realistic projections. Solve now, define a trigger, or leave alone.
An evidence-backed decision on the next constraint, without assuming every finding needs a build project.
A clear picture of where the next constraint lives so you can address it before it becomes a production incident.
Performance analysis report
Current access patterns, query profile, and bottlenecks assessed against an agreed planning horizon and demand scenario.
Tiered intervention plan
Problems ranked by decision point: solve now, define a later trigger, or leave alone. Each recommendation compares the cost and risk of acting with the cost of waiting.
A scoped path from findings to implementation
Recommendations identify the change, expected effect, dependencies and verification method. Implementation requires separately agreed scope, access, cost, recovery approach and acceptance criteria, whether your team or Ampity carries it out.
Review session with your team
We walk through the findings with the engineers who maintain the system. Context is shared. The team owns the roadmap.
Results
Profiled
Latency, throughput, errors, saturation, queueing, data access, and dependency time are observed together.
Reproduced
The active bottleneck is demonstrated under a representative workload before redesign work begins.
Verification plan
An approved change is checked against the baseline workload for performance, reliability, cost and correctness. Assessment alone does not demonstrate an improvement.
Decision evidence
The analysis retains workload definitions, traces, profiles, baselines, test conditions, findings, rejected hypotheses, recommended changes, and the verification method.
Related services
System Architecture Design
Performance constraints are often architectural. When the analysis surfaces structural decisions that need to change, architecture design is the next step.
Backend Systems & APIs
When the analysis identifies specific query patterns, async processing gaps, or API-level bottlenecks that need implementation work, this is where it gets built.
Platform Modernization
When performance analysis surfaces constraints that require moving to a different data model or runtime, platform modernization executes the migration.
Find the constraint before adding capacity
Application performance optimization separates slow application work, database contention, dependency latency, and capacity limits using representative production evidence.
Database performance consulting
Review slow-query distributions, execution plans, index selectivity, locks, connection pooling, and transaction duration. Reproduce the problematic workload before changing indexes, queries, schema, or database size. Verify write overhead and consistency as well as read latency; adding replicas does not resolve every contention or query problem.
Database scaling strategiesBackend and cloud performance optimization
Measure latency percentiles, throughput, error rates, resource saturation, and queue growth under the same workload before and after a change. Trace the limiting dependency rather than comparing isolated benchmarks. A useful recommendation specifies a reversible experiment, an acceptance threshold, and the conditions under which the team should revisit it.
Cloud bottleneck and scaling reviewFrequently asked questions
What do performance optimization services include?
This assessment profiles application behavior and database access against an agreed workload, identifies bottlenecks and delivers prioritised findings with evidence and a verification plan. Implementation requires separately agreed scope, access, cost, recovery and acceptance criteria. Approved changes are then measured against the baseline; an assessment alone is not proof of an improvement.
Why is our application slow under load?
Common causes are inefficient database queries, missing indexes, blocking calls, poor caching, undersized infrastructure or contention on shared resources. Profiling identifies which apply.
Do you offer database performance tuning?
Yes. We analyse slow queries, indexing, schema design, connection handling and configuration for databases such as PostgreSQL and MySQL.
How do you measure performance improvement?
We agree target metrics such as response time percentiles, throughput and error rate, then measure them before and after changes under realistic load.
Will performance work require a rewrite?
Rarely. Most gains come from targeted changes to queries, caching, concurrency and infrastructure rather than rewriting the application.