FastAPI developers build asynchronous Python APIs where type models define validation, serialisation, and documentation from one definition. Companies hire them because a single blocking call inside an async handler stalls every concurrent request on that worker, and because the framework deliberately omits authentication, an ORM, and migrations that you must assemble deliberately.
The most common production problem with this framework is not the framework. It is a synchronous database driver or a blocking HTTP client inside an async endpoint, which quietly serialises all your concurrency. Load testing finds it in an afternoon. TechEsperto Solutions builds and audits with that specifically in mind.
Computation inside the event loop makes things worse, and the honest answer for that work is a separate worker process.
Our engagements cover new asynchronous API development, blocking call audits and remediation, migration to the current major version of the validation library, scaffolding decisions for authentication and data access, model inference endpoints, and deployment and worker tuning. Blocking call audits are the single most requested piece of work and usually the most immediately valuable. Clients scoping wider backend delivery review our API development practice alongside hiring developers directly.
Endpoints with type models defining contracts, dependency-injected authentication and sessions, async drivers throughout, and load testing before launch rather than after complaints.
Identifying synchronous database drivers, HTTP clients, file operations, and libraries inside async handlers, then correcting them. Frequently produces large throughput improvements from small changes.
The most recent major version rewrote the core and changed the API surface, so migration is more involved than a version bump. Handled with tests and schema comparison throughout.
Choosing and wiring the components the framework omits, documented so the reasoning survives. These decisions are difficult to reverse and worth deciding deliberately.
Serving machine learning models with request batching, model loading at startup rather than per request, timeouts, and computation kept out of the event loop.
Worker count and class, connection pool sizing, timeout configuration, graceful shutdown, and health endpoints. Measured against concurrent load rather than single-request timings.
Assessment for this work tests asynchronous reasoning above everything else. We ask candidates what happens when a synchronous call appears in an async handler, how a sync endpoint is executed differently, where a thread pool is appropriate, and how connection pool size interacts with worker count. Developers who cannot answer these produce services that fail under exactly the load they were built for. Deployment work is frequently staffed alongside our DevOps services team.
Knowing that handlers declared synchronously are executed in a thread pool automatically, while blocking work inside an async handler is not. Choosing the right declaration per endpoint matters.
Separate models for request and response, nested validation, custom validators, and serialisation control. Reusing one model for both input and output leaks fields and causes real incidents.
Dependencies for sessions, authentication, and shared clients, with expensive resources created once during application startup through the lifespan context rather than per request.
Asynchronous drivers and session handling, pool sizing relative to worker count, and transaction scoping. A synchronous driver here defeats the entire reason for choosing this framework.
The built-in background mechanism runs in the same process and does not survive a restart. Anything that must complete belongs in a durable queue, and we draw that line explicitly.
Accurate response models, documented status codes, examples, and tags. The schema is a published contract that client teams and code generators depend on, so it deserves review.
Most clients begin with a performance review, because the presenting symptom is usually poor throughput and the cause is usually specific and fixable. Options then include blocking call audits, greenfield builds, validation library migration, an embedded developer, and a maintenance retainer. Our standard arrangements sit in our engagement models. Transparent pricing, an executed NDA, and full intellectual property transfer apply throughout.
We examine handler declarations, drivers and clients in use, dependency and lifespan handling, worker configuration, and pool sizing, then report where concurrency is being lost.
A focused engagement identifying every blocking operation in asynchronous paths, with the correction named per instance and expected throughput improvement estimated. Frequently the best value available.
New development with async drivers throughout, dependency-injected scaffolding, documented model contracts, and load testing as an acceptance criterion rather than a later exercise.
Migration to the current major version with model changes, validator rewrites, and schema comparison to confirm the published contract has not changed unintentionally.
One specialist inside your team building endpoints while maintaining asynchronous discipline. Prevents the gradual reintroduction of blocking calls as new features arrive.
Dependency updates, security patching, throughput monitoring, pool and worker tuning as traffic grows, and periodic review for blocking calls introduced by new code.
This section describes most of the value we add on existing services. We find the blocking calls first, because guessing wastes time and the offender is frequently a library nobody suspected. Asynchronous drivers replace synchronous ones in async paths. Work that genuinely cannot be asynchronous moves to a thread pool explicitly. Computation moves out of the process entirely into workers. Measurement uses concurrent load rather than single-request timings, since a blocked event loop looks perfectly fast with one user. And every outbound call gets a timeout, because a slow upstream otherwise holds a worker indefinitely. Clients working with TechEsperto Solutions get throughput they can demonstrate.
Profiling under concurrent load, plus a review of every library used inside async handlers. Synchronous database drivers, requests-style HTTP clients, and file operations are the usual culprits.
An asynchronous database driver and HTTP client throughout the async path. This is frequently the single change that transforms measured concurrency on an existing service.
Libraries with no asynchronous equivalent wrapped so they run off the event loop. Explicit and correct, rather than hoping the call is fast enough not to matter.
Image processing, heavy computation, and model training belong in separate workers reached through a queue. Running them in an API process starves every concurrent request.
Load tests at realistic concurrency levels reporting throughput and tail latency. A service that answers one request in forty milliseconds may collapse at fifty concurrent requests.
Database queries, HTTP calls, and cache operations all bounded. Without timeouts, one slow dependency consumes every worker and takes down a service that was otherwise healthy.
Suitability depends on what your service spends its time doing. Waiting on other systems is where asynchronous handling wins decisively. Computing is where it loses. Our developers assess the profile before choosing an architecture, and will say plainly when your workload does not benefit. Inference endpoints are frequently delivered alongside our machine learning development practice.
One request triggering several parallel calls to other services. The clearest case for asynchronous handling, since concurrent waiting is exactly what it optimises.
Models loaded once at startup, requests batched where latency budgets allow, and computation kept off the event loop. Pool and worker sizing matter more here than almost anywhere.
Long-lived responses pushing data as it becomes available. Connection limits, heartbeats, and cleanup on disconnect need explicit handling rather than defaults.
Fast acknowledgement with processing deferred to a durable queue, signature verification, and idempotency. Throughput matters and processing time inside the request does not belong here.
Calls between your own services with tight timeouts, retries with backoff, and correlation identifiers propagated. Contract stability matters less than in public APIs, and reliability matters more.
Where a service mainly computes, asynchronous handling adds no benefit and some risk. We recommend synchronous handlers with more workers, or a queue-based worker architecture instead.
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Workloads dominated by computation belong in workers. We say so rather than building an asynchronous API around the wrong problem.
The entry point is a free performance review. Give us read access and a description of your throughput problem, and we return a written assessment covering handler declarations, blocking operations found in asynchronous paths, dependency and lifespan handling, worker and pool configuration, and schema quality, with fixes ranked by expected throughput improvement. A load test under realistic concurrency usually accompanies it. Your team interviews the matched developers, onboarding completes inside a week, and retainers stay optional.
A few days of reading plus a concurrency load test. The finding is frequently one synchronous driver, which is satisfying to report and cheap for you to fix.
Throughput and tail latency measured at realistic concurrency rather than single-request timings. Establishes the baseline that every later improvement is measured against.
Every blocking operation in asynchronous paths identified with the correction named, at a fixed price. Detailed enough for your own developers to implement independently.
Profiles arrive with relevant asynchronous Python and deployment experience. You assess them against your standards, decline at no cost, and matching continues until the fit is right.
Repository access, database access, environment configuration, and sprint planning handled immediately so measurable improvement lands inside the first fortnight.
Support begins with dependency updates and throughput monitoring, expanding into worker and pool tuning as your traffic grows.
Our work and story have been picked up by news outlets and databases worldwide.
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Performance reviews are free. Blocking call audits are a short fixed-price engagement and usually return their cost immediately in capacity. Greenfield builds are priced against acceptance criteria including load test targets. Validation library migrations are quoted after assessment. Embedded developers are quoted monthly.
Performance reviews and load tests deliver in under a week. Blocking call remediation is frequently one to two weeks and produces the largest measured improvement. Greenfield APIs run four to eight weeks depending on endpoint count and scaffolding decisions. Validation library migrations take two to four weeks.
Performance and audit work is one senior developer, since it is investigative. Greenfield builds commonly run two once contracts are agreed. Inference endpoints occasionally add a machine learning engineer for model handling and batching.
Our developers join your repository, board, and chat workspace and follow your review process. Weekly sessions cover deployed changes and throughput measurements, which is a clearer progress signal than a task list.
Repository access and a development database or anonymised dump. A mutual NDA is signed first. Production access is rarely necessary and always scoped and revocable when it is. We work in your environment rather than copying code elsewhere.
We staff for at least four hours of daily overlap with your business day across North American, UK, European, and Australian schedules. Load test reviews and deployment windows sit inside that window.
Dependency updates and periodic review for blocking calls introduced by new features, since asynchronous discipline erodes as teams add endpoints under pressure. Retainers also cover worker and pool tuning as traffic grows and security patching across the dependency tree.
FastAPI wins where you want type-driven contracts, automatic schema and documentation, and genuine asynchronous concurrency for services that spend their time waiting on other systems. Flask remains excellent for small synchronous services, applications with server-rendered templates, and teams with deep existing Flask expertise, and its ecosystem is mature and stable. Flask has gained asynchronous support, though its extension ecosystem is largely synchronous, so asynchronous throughout is less straightforward. If your service is API-only and concurrency matters, choose FastAPI. If it is small, synchronous, or template-driven, Flask is a perfectly good answer and we will say so.
Tell us what you’re building. Our team will get back to you within one business day with a clear, no-obligation plan.
