Traffic looks different now: More releases, slow AI features, and costly AI model calls that hit your APIs alongside human users. Our performance testing expertise shows how your applications, APIs, and AI features hold up under that load.
CMMI
Level 3
HIPAA
Compliant
AWS
Partner Network
ISO Certified
For Data Quality & Security
SOC 2
Type II Compliant
Engage our performance testing services for one final test before the big release, or build a full performance
engineering program that runs alongside your development.
We build open-model workloads from production logs, with realistic arrival rates, think times, and data mixes. Then we step load up to peak and beyond, tracking p95 and p99 latency, throughput, and error rates to find the exact point where the system saturates.
We check whether throughput scales linearly as you add pods, nodes, or regions, and how quickly horizontal autoscaling and scale-in respond under load. We map each result to cost per transaction, so capacity forecasts reflect both performance and budget.
We load-test REST, GraphQL, and gRPC endpoints on their own and as end-to-end service chains. Our performance testers use OpenTelemetry traces to break latency down per hop, which exposes chatty calls, N+1 patterns, retry storms, and saturated threads.
We combine protocol-level load with real-browser sessions, measuring front-end metrics while the back end is under pressure. Our experts use Lighthouse and WebPageTest traces to pinpoint render-blocking resources, heavy JavaScript bundles, and slow CDN or cache behavior.
We profile native and cross-platform apps on real-device clouds, measuring cold and warm start time, frame rendering, memory, CPU, and battery use. We use network shaping to reproduce 3G, high-latency, and packet-loss conditions while the mobile back-end and APIs run at peak load.
LLM features scale differently from standard APIs: latency depends on prompt and output token counts, and capacity is capped by provider rate limits or GPU memory. We test streaming latency, queueing, KV-cache pressure, and batching at production concurrency.
We profile the data tier under load using execution plans, wait statistics, and lock contention analysis to find missing indexes, full scans, deadlocks, and connection pool exhaustion. On platforms such as SAP, Salesforce, Dynamics 365 and Oracle, we also check that scheduled batch jobs finish within their time windows under load.
We keep load tests as version-controlled code (k6, Gatling or JMeter) and run them in your pipeline against fixed baselines. Thresholds on p95 latency, error rate and resource use fail the build automatically when a change regresses performance.
Tell us about your application, expected traffic and next big release. A performance engineer will come back with the risks we see and a recommended test plan.
We use AI performance testing tools to model traffic, draft test scripts, and correlate thousands of metrics. Every finding and
recommendation is then verified by a performance engineer.
Review architecture, incidents, and monitoring data, then agree on SLOs and budgets.
Build workload models and test scripts from real traffic patterns.
Run load, stress, spike, and soak scenarios with full observability switched on.
Trace each bottleneck to its cause, fix it with your team, and re-test.
Move key tests into CI/CD so every release is checked against the budgets.
Solved an acute problem
A commercial lender's underwriters spent 20–30 hours per loan file on manual data entry. We built a custom-trained document processing and workflow platform on AWS.
35%
higher throughput
99.5%
extraction accuracy
Rebuilt a broken core system
A bath & lighting brand in 55+ countries had site search converting just 10–15% of searchers. We trained a transformer ranking model on their own catalog and query logs.
80%
more accurate search
72%
higher conversion
Found the bigger problem
An eCommerce retailer brought us in for nopCommerce upkeep. The real bottleneck was inventory and pricing scattered across five channels — so we built the fix.
60%
increase in sales
~90%
tasks automated
Provided a dedicated engineering pod
A core retail POS platform had to outgrow three technology eras without losing what made it work. The same embedded pod carried it from CGI to PHP to AWS over 15 years, so no knowledge was ever lost in handover.
15+ years
Zero re-tendering and handovers
TechnoScore’s performance testing services measure how fast, stable, and scalable an application is under expected and extreme load. A full service engagement covers test strategy, workload modeling, load, stress, spike, and soak testing, bottleneck diagnosis and tuning, and automated performance checks in CI/CD.
Load testing checks that a system meets its targets at expected and peak traffic. Stress testing pushes beyond that peak until the system fails to find its real limit and see how it recovers.
We mostly use open-source, code-based tools such as k6, JMeter, Gatling, and Locust, as well as cloud services such as Azure Load Testing. If you already own LoadRunner or NeoLoad, we work with them. We analyze results alongside APM data from tools like Datadog, Dynatrace, or Grafana.
Yes. We can also test AI-native products. Our experts measure key performance metrics such as time to first token, tokens per second, full response time, and cost per request as concurrent users increase. We also test model provider rate limits, fallback behavior, and the speed of retrieval and agent steps in RAG and agentic workflows.
You should run a full round before major launches, peak events, migrations, and infrastructure changes. Between those, run smaller automated performance tests in your CI/CD pipeline so a slow code change is caught at the pull request, not in production.
We build workload models from your production logs, analytics, and monitoring data: which journeys users take, how often, and with what data. Our tests then reproduce that mix at the volumes you expect, rather than hammering a single endpoint.
A production-like staging environment is the safest for heavy load and stress tests, and we can help set one up on demand. We can also run carefully controlled production tests for some low-risk goals, with traffic limits, off-peak scheduling, and rollback plans agreed in advance.
Both, depending on what you need. Every report explains the root cause of each bottleneck and recommends a fix. Our performance testing engineers can also make the code, query, caching, and infrastructure changes with your team and re-test to confirm the improvement.
Cost depends on the number of user journeys, target load, environments, and the number of testers engaged. It also depends on whether tuning is included. The hourly rates start as low as USD 18 for basic requirements. Contact us at info@technoscore.com for a custom quote.
Project initiation within 48 hours
Round-the-clock support
Strict NDAs for data security
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