Managed inference and self-hosted inference define who owns the production behavior of an AI system. Managed inference shifts parts of deployment, model serving, scaling, monitoring, optimization, and incident response to a provider. Self-hosted inference keeps those responsibilities inside the customer team. The right choice depends on workload shape, latency targets, internal infrastructure capability, cost behavior, security requirements, and how much operational ownership the team can carry over the next 6–12 months.
Definitions
What is managed inference?
Managed inference is an operating model where a provider runs part or all of the infrastructure required to serve AI models in production.
A managed inference provider may handle:
| Layer | Managed inference responsibility |
|---|---|
| Deployment | Endpoint creation, model loading, versioning, rollout |
| Model serving | Runtime configuration, request handling, streaming |
| GPU infrastructure | GPU allocation, capacity planning, region selection |
| Scaling | Autoscaling, warm capacity, burst handling |
| Observability | Metrics, logs, traces, alerts |
| Optimization | Latency, throughput, TTFT, GPU utilization |
| Reliability | Health checks, failover, recovery paths |
| Support | Debugging, tuning, incident response |
“Managed” is not a fixed standard. Some managed services mainly abstract deployment. Others also own runtime tuning, monitoring, scaling, and incident response. KServe describes model serving as covering autoscaling, networking, health checking, and server configuration for ML deployments on Kubernetes, which shows how many operational layers can sit around inference serving. (Hugging Face Text Generation Inference documentation)
For buyers, the important question is: what does the provider actually own?
What is self-hosted inference?
Self-hosted inference is an operating model where the customer runs the inference infrastructure directly.
A self-hosted team may use cloud GPUs, bare-metal GPUs, Kubernetes, custom deployment pipelines, open-source model serving frameworks, observability tools, and internal on-call processes.
Self-hosting gives more control over:
| Area | Self-hosted control |
|---|---|
| Runtime | Framework, scheduler, batching, quantization |
| Infrastructure | GPU type, region, networking, storage |
| Deployment | Versioning, rollout, rollback |
| Security | Tenancy, network boundaries, data path |
| Observability | Metrics, logs, traces, dashboards |
| Incident response | Alerting, debugging, recovery |
| Cost | Utilization, reserved capacity, idle capacity |
The tradeoff is that the customer also owns the operating burden.
Self-hosted inference can be the right choice when the team has strong infrastructure capability, strict compliance needs, or workload requirements that do not fit managed inference platforms.
What is serverless inference?
Serverless inference is a managed inference model where the customer does not provision or manage the underlying servers directly.
Serverless inference can be useful for intermittent or variable workloads. It can also introduce cold-start behavior. AWS documents that serverless endpoint cold-start time depends on model size, model download time, and container startup time. (AWS SageMaker Serverless Endpoints documentation)
Serverless inference should be evaluated carefully for latency-sensitive workloads, especially when P99 latency and TTFT matter.
What is dedicated inference?
Dedicated inference is an inference model where capacity or execution environments are isolated for one customer or workload.
Dedicated inference usually sits between shared managed inference and fully self-hosted inference.
It is relevant when the team needs:
- workload isolation
- clearer resource ownership
- lower multi-tenant contention risk
- more control than shared serverless inference
- less operational load than fully self-hosted inference
Dedicated inference does not remove every responsibility from the customer. It changes the responsibility boundary.
What is a dedicated GPU?
A dedicated GPU is raw GPU infrastructure allocated to one customer or workload.
A dedicated GPU does not automatically include model serving, autoscaling, inference optimization, observability, deployment safety, or incident response.
Dedicated GPU access solves the compute allocation problem. It does not, by itself, solve the production inference operations problem.
Managed inference vs self-hosted inference: comparison
| Dimension | Managed inference | Self-hosted inference |
|---|---|---|
| Main decision | Shift operational ownership to a provider | Keep operational ownership internal |
| Control | Depends on provider scope | Highest control |
| Deployment | Provider-managed or partly provider-managed | Customer-managed |
| Model serving | Provider-managed or partly provider-managed | Customer-managed |
| GPU infrastructure | Provider-managed | Customer-managed |
| Scaling | Provider-managed or shared | Customer-designed and customer-operated |
| Observability | Built in, but depth varies | Fully customizable |
| Optimization | Provider may tune runtime, batching, latency, and throughput | Customer owns tuning |
| P99 latency | Depends on provider runtime, capacity model, and workload fit | Fully controllable, but internally owned |
| TTFT | Depends on warm capacity, queueing, prefill, and runtime | Customer owns all causes |
| Throughput | Depends on provider serving stack and hardware utilization | Customer owns serving efficiency |
| Cost model | Usage-based, token-based, or capacity-based | GPU cost plus engineering and operations cost |
| Uptime | Depends on provider design and SLA/SLO | Depends on internal reliability engineering |
| Incident response | Provider-owned or shared | Customer-owned |
| Lock-in risk | API, provider workflow, runtime behavior, pricing | Infrastructure, framework, tooling, internal processes |
| Best fit | Teams reducing infrastructure burden | Teams with strong infrastructure capability and strict control needs |
The table is only useful if the responsibility boundary is explicit. A managed inference provider should state what it owns and what the customer still owns.
Key decision criteria
| Decision criterion | Why it matters | What to evaluate |
|---|---|---|
| P99 latency | Shows tail behavior under real demand | P95/P99 latency under realistic concurrency |
| TTFT | Affects perceived responsiveness in streaming products | Queue time, prefill time, cold starts |
| Throughput | Determines how much work the system can process | Tokens/sec, requests/sec, batch throughput |
| Workload shape | Inference cost and stability depend on request patterns | Prompt length, output length, traffic bursts |
| GPU utilization | Drives effective cost per token | Idle capacity, batching efficiency, memory pressure |
| Autoscaling | Determines behavior under changing demand | Scale-up time, scale-down policy, warm capacity |
| Observability | Determines how quickly issues can be diagnosed | Logs, metrics, traces, queue time, GPU memory |
| Incident response | Determines recovery path during degradation | Who responds, what they can inspect, how rollback works |
| Runtime control | Determines customization depth | Framework, quantization, batching, decoding |
| Security and isolation | Determines deployment model | Shared, dedicated, private, self-hosted |
| Cost predictability | Determines budget confidence | Token volume, concurrency, idle cost, engineering cost |
| Internal capability | Determines whether self-hosting is realistic | MLOps, SRE, GPU operations, on-call coverage |
Responsibility boundaries
A production inference decision should define who owns each part of the system.
| Responsibility | Managed inference | Self-hosted inference |
|---|---|---|
| GPU provisioning | Usually provider | Customer |
| Model deployment | Provider or shared | Customer |
| Runtime configuration | Provider or shared | Customer |
| Batching and scheduling | Provider or shared | Customer |
| KV cache behavior | Provider or shared | Customer |
| Quantization strategy | Provider or shared | Customer |
| Autoscaling | Provider or shared | Customer |
| Observability | Provider or shared | Customer |
| Alerts | Provider or shared | Customer |
| Rollback | Provider or shared | Customer |
| Incident response | Provider or shared | Customer |
| Cost forecasting | Provider may support | Customer |
| Prompt design | Customer | Customer |
| Application logic | Customer | Customer |
| Product behavior | Customer | Customer |
The most important row is incident response.
When production inference degrades, the team needs to know who has both the responsibility and the system access to act.
Why production inference fails after a working setup
A working endpoint is not the same as a production-stable inference system.
Production behavior depends on model size, context length, output length, request concurrency, batching, memory pressure, region placement, runtime efficiency, scaling policy, and incident response.
LLM inference has a specific memory profile. NVIDIA identifies model weights and KV cache as two main contributors to GPU memory requirements during LLM inference. KV cache stores attention tensors to avoid recomputing previous context. (NVIDIA Developer Blog)
This is why failures often appear under load, not during initial deployment.
Common production symptoms include:
| Symptom | Possible causes |
|---|---|
| P99 latency spikes | Queueing, batching, saturation, cold starts |
| Slow TTFT | Long prefill, queue depth, cold capacity |
| Out-of-memory failures | KV cache pressure, long context, high concurrency |
| Low throughput | Poor batching, underutilized GPUs, runtime limits |
| Rising cost | Overprovisioning, idle capacity, poor utilization |
| Unstable behavior under bursts | Weak autoscaling, insufficient warm capacity |
| Slow recovery | Missing observability, unclear incident ownership |
The question is not whether the model can run. The question is whether the system can hold under production demand.
Technical concepts that affect the decision
P99 latency
P99 latency is the latency experienced by the slowest 1% of requests.
For production AI products, P99 latency can matter more than average latency. Average latency can look acceptable while a small portion of users experience slow responses.
P99 latency can degrade because of queueing, overloaded GPUs, memory pressure, cold starts, poor batching, or traffic bursts.
TTFT
TTFT, or Time to First Token, measures the time between request submission and the first generated token.
TTFT matters in streaming AI products because it shapes perceived responsiveness.
TTFT is affected by:
- queue time
- context length
- prefill cost
- runtime scheduling
- batching policy
- cold starts
- warm capacity
- region placement
Throughput
Throughput measures how much inference work the system can process.
For LLM workloads, throughput should be measured in more than one way:
| Throughput metric | What it shows |
|---|---|
| Tokens per second | Generation capacity |
| Requests per second | Endpoint request handling |
| Batch throughput | Offline or bulk processing capacity |
| Concurrent requests | Multi-user behavior |
| Inter-token latency | Streaming generation smoothness |
Throughput and latency are linked. Increasing batching can improve GPU utilization, but it can also increase queue time or tail latency if scheduling is not tuned.
Continuous batching
Continuous batching dynamically reschedules batches during generation so new requests can join as others complete. Hugging Face describes continuous batching as a way to keep the GPU occupied and maintain high throughput. (Hugging Face continuous batching documentation)
Continuous batching can improve utilization. It also makes scheduling behavior important.
The buyer should evaluate how the serving system handles mixed request lengths, long prompts, short prompts, streaming requests, and burst traffic.
KV cache
KV cache stores key and value tensors from prior tokens so the model does not recompute previous context during generation. NVIDIA identifies KV cache as a main contributor to LLM inference memory use. (NVIDIA Developer Blog)
KV cache pressure increases with:
- longer context windows
- higher concurrency
- larger batches
- longer outputs
- inefficient memory layout
- poor cache management
The vLLM PagedAttention paper states that existing systems can waste KV cache memory through fragmentation and redundant duplication, limiting batch size. It proposes PagedAttention to manage KV cache memory more efficiently. (vLLM PagedAttention paper)
Quantization
Quantization reduces numerical precision to lower memory use and improve throughput.
It can help serve larger models or higher concurrency on the same hardware. It can also affect accuracy or output behavior depending on model, workload, context length, and quantization method.
Quantization should be validated against the actual workload.
Speculative decoding
Speculative decoding uses a faster draft path to propose tokens and verifies them with the target model.
NVIDIA TensorRT-LLM documentation describes speculative decoding as a set of techniques for generating more than one token per forward pass, which can reduce average per-token latency in some conditions. (NVIDIA TensorRT-LLM speculative decoding documentation)
The benefit depends on model pair, acceptance rate, batch size, hardware, and serving implementation.
Autoscaling
Autoscaling adjusts serving capacity as workload changes.
AWS documents serverless endpoint behavior where cold-start time depends on model size, model download time, and container startup time. (AWS SageMaker Serverless Endpoints documentation)
Inference autoscaling is not the same as ordinary web-service autoscaling.
Useful signals may include:
- queue depth
- active sequences
- GPU memory
- tokens per second
- TTFT
- P99 latency
- prompt length
- output length
- cache pressure
Scaling on request count alone can miss the actual bottleneck.
Deployment safety
Production inference needs controlled rollout and recovery behavior.
AWS deployment guardrails describe canary traffic shifting for rolling out endpoint updates with safety guardrails. (AWS SageMaker canary deployment documentation) AWS also documents rollback behavior when configured alarms trip during a baking period. (AWS SageMaker blue/green deployment documentation)
The same operating principle applies outside AWS: model and runtime changes need a safe release path.
Observability
Inference observability should cover more than uptime.
A production inference system should expose:
| Metric | Why it matters |
|---|---|
| P50/P95/P99 latency | Shows response distribution |
| TTFT | Shows streaming responsiveness |
| Inter-token latency | Shows generation smoothness |
| Queue time | Shows saturation before execution |
| Tokens per second | Shows generation throughput |
| GPU utilization | Shows hardware efficiency |
| GPU memory | Shows OOM and KV cache pressure risk |
| Error rate | Shows runtime and endpoint failure |
| Cold-start time | Shows serverless responsiveness |
| Model version | Supports rollback and root cause analysis |
Hugging Face Text Generation Inference is documented as a toolkit for serving large language models and includes production-oriented features such as continuous batching, streaming, optimized attention, decoding optimizations, quantization support, and related serving capabilities. (Hugging Face Text Generation Inference documentation)
When managed inference is a fit
Managed inference is usually a fit when the team wants production AI inference without building and operating the full inference infrastructure stack internally.
| Managed inference is a fit when… | Why |
|---|---|
| Infrastructure work is slowing product work | The operating load is already affecting delivery |
| P99 latency is unstable under load | Runtime, batching, and scaling need active ownership |
| GPU cost is hard to explain | Utilization and capacity planning may need attention |
| The team lacks dedicated MLOps capacity | Operating the stack may not be sustainable |
| Support quality matters during incidents | Recovery depends on people who can inspect the system |
| Traffic is bursty or changing | Scaling and cost behavior need continuous adjustment |
| The workload is production-facing | Reliability and recovery matter more than setup speed |
| Cost predictability matters | Managed planning may reduce surprises, depending on provider |
Managed inference does not remove the need for technical evaluation. It shifts part of the operating burden to a provider.
The buyer should verify what the provider owns, what the customer still owns, and what happens when inference degrades.
When managed inference is not a fit
Managed inference may not be a fit when the team needs full control and can operate the stack internally.
| Managed inference may not be a fit when… | Why |
|---|---|
| Runtime customization is deep | Managed platforms may abstract too much |
| Compliance requires full infrastructure ownership | Data and execution may need to stay inside customer-controlled systems |
| The model architecture is unusual | Provider runtimes may not support the required path |
| The team needs custom kernels or custom scheduling | Provider access may be limited |
| Internal utilization is predictable and high | Self-hosting economics may be stronger |
| The team already has mature MLOps and SRE coverage | Operating internally may be realistic |
| Vendor dependency is unacceptable | Managed inference introduces provider dependency |
This does not make managed inference weaker. It means the workload and operating model require a different boundary.
When self-hosted inference is a fit
Self-hosted inference is usually a fit when control is more valuable than operational abstraction.
| Self-hosted inference is a fit when… | Why |
|---|---|
| The team has GPU operations experience | The operating burden is realistic |
| Runtime control is required | Framework, batching, quantization, and scheduling can be customized |
| The compliance boundary is strict | Data path and infrastructure can stay fully customer-owned |
| Traffic is predictable | Capacity planning is easier |
| Utilization can stay high | Raw compute economics can improve |
| The team has mature observability | Incidents can be diagnosed internally |
| On-call coverage exists | Recovery does not depend on external support |
| The workload is unusual | Custom infrastructure may be necessary |
Self-hosting is not just a hosting decision. It is an operating commitment.
Fit / not fit table
| Option | Fit | Not fit |
|---|---|---|
| Serverless inference | Variable workloads, fast deployment, lower infrastructure management | Strict P99 latency without warm-capacity planning, deep runtime control |
| Managed inference | Production teams reducing operational ownership | Teams requiring full control or private-only infrastructure |
| Dedicated inference | Isolated workloads, clearer resource ownership, more control than shared inference | Very early prototypes, workloads that do not justify dedicated capacity |
| Dedicated GPU | Teams that want raw compute with full stack ownership | Teams without MLOps, serving, monitoring, and incident response capability |
| Self-hosted inference | Strong infrastructure teams with strict control needs | Teams already slowed by infrastructure operations |
Risks and tradeoffs
Risks of managed inference
| Risk | Why it matters |
|---|---|
| Unclear responsibility boundary | The buyer may assume the provider owns issues that remain customer-owned |
| Limited low-level control | Runtime, networking, hardware, or deployment behavior may be constrained |
| Provider lock-in | API behavior, pricing, deployment workflow, or runtime assumptions may become hard to change |
| Opaque cost behavior | Usage pricing can be hard to forecast without token and concurrency visibility |
| Cold starts | Some serverless models may add startup latency |
| Multi-tenant variability | Shared capacity may not fit strict isolation or predictability needs |
| Support depth variance | Support may exist without runtime-level operating capability |
The main managed inference risk is assuming “managed” means “fully owned.”
The provider should define the operating boundary before the buyer relies on it for production workloads.
Risks of self-hosted inference
| Risk | Why it matters |
|---|---|
| Hidden engineering cost | Operations work pulls time from product and model work |
| Overprovisioning | Teams may reserve too much GPU capacity for peak demand |
| Low utilization | Idle GPUs increase effective cost per token |
| Tail latency | Poor scheduling can create P95/P99 degradation |
| Memory pressure | KV cache and long context can trigger OOM or instability |
| Autoscaling mismatch | Request count may not reflect token load or GPU saturation |
| Runtime maintenance | Serving frameworks, drivers, CUDA, and dependencies require upkeep |
| Incident ownership | The team owns debugging across model, runtime, GPU, infrastructure, and app layers |
The main self-hosted inference risk is underestimating operating burden.
A system can be deployable and still become unreliable under production traffic.
Common misconceptions
Misconception 1: Self-hosted inference is always cheaper
Self-hosted inference can have lower raw compute cost when utilization is high and the team already has infrastructure capability.
It is not automatically cheaper.
The total cost includes GPU capacity, idle time, overprovisioning, monitoring, debugging, on-call coverage, reliability work, and engineering time.
Misconception 2: Managed inference removes all technical responsibility
Managed inference can reduce operational ownership.
It does not remove customer responsibility for application behavior, prompt logic, product integration, data handling, model choice, security requirements, and workload definition.
The responsibility boundary must be explicit.
Misconception 3: Dedicated GPUs are the same as dedicated inference
Dedicated GPUs provide raw compute.
Dedicated inference includes an inference-serving operating model around that compute. Depending on provider scope, this may include orchestration, monitoring, scaling, optimization, and incident response.
The distinction matters in production.
Misconception 4: Average latency is enough for evaluation
Average latency can hide production issues.
Technical buyers should evaluate P95 latency, P99 latency, TTFT, queue time, throughput, and behavior under realistic concurrency.
Misconception 5: A benchmark result predicts production behavior
Benchmarks are useful, but they are not a replacement for workload-specific testing.
MLCommons describes MLPerf as an industry-standard benchmark suite for measuring quality, performance, and risk in machine learning systems. (MLCommons)
A buyer still needs tests that match their model, request lengths, traffic shape, region, concurrency, and latency targets.
How to evaluate managed inference providers
1. Ask what the provider owns
| Question | Why it matters |
|---|---|
| Who owns deployment? | Defines release and rollback responsibility |
| Who owns scaling? | Defines response to traffic changes |
| Who owns runtime tuning? | Defines latency and throughput responsibility |
| Who owns monitoring? | Defines visibility during incidents |
| Who owns incident response? | Defines who acts when production degrades |
| Who owns cost optimization? | Defines whether efficiency is active or passive |
| What does the customer still own? | Prevents false assumptions |
If the provider cannot answer these questions clearly, the managed model may be operationally vague.
2. Ask how performance is measured
Performance claims should include test context.
A useful evaluation should specify:
| Required detail | Why |
|---|---|
| Model name and size | Different models behave differently |
| Precision | Affects memory, accuracy, and throughput |
| Hardware | Determines compute and memory profile |
| Region | Affects network latency |
| Prompt length | Affects prefill and memory use |
| Output length | Affects generation cost |
| Concurrency | Affects scheduling and memory pressure |
| Batch behavior | Affects latency and throughput |
| Streaming or non-streaming | Affects TTFT and inter-token latency |
| Warm or cold capacity | Affects startup behavior |
| P50/P95/P99 latency | Shows distribution |
| TTFT | Shows first-response behavior |
| Tokens per second | Shows generation throughput |
| Test duration | Shows stability over time |
Avoid accepting performance claims without workload context.
3. Ask how incidents are handled
A provider should explain:
- how alerts are triggered
- who receives alerts
- who investigates
- what runtime data is visible
- how rollback works
- how failures are communicated
- how root cause is handled
- what happens outside normal business hours
Incident response is part of inference infrastructure. It should not be treated as a separate support add-on.
4. Ask how cost is forecasted
Cost forecasting should include workload shape.
Ask for assumptions around:
- input tokens
- output tokens
- requests per second
- concurrency
- peak traffic
- idle time
- region
- dedicated vs shared capacity
- batch vs real-time traffic
- expected growth
A provider that cannot model cost behavior may not reduce cost uncertainty.
Where Geodd fits
Geodd is relevant when the buyer is not only looking for GPU access, but for production inference infrastructure with defined operational ownership.
Geodd’s product structure separates three options: Serverless Inferencing, Dedicated Inferencing, and Dedicated GPU infrastructure. Geodd’s internal product material defines Serverless Inferencing as fully managed multi-tenant inference where Geodd owns the full inference stack and the customer owns the application layer. It defines Dedicated Inferencing as a single-tenant inference environment with dedicated GPUs and isolated execution. It defines Dedicated GPUs as raw bare-metal GPU infrastructure where the customer is fully responsible for the stack.
| Geodd option | Role | Responsibility boundary |
|---|---|---|
| Serverless Inferencing | Managed inference endpoints | Geodd owns the inference stack; customer owns the application layer |
| Dedicated Inferencing | Isolated inference environment | Shared responsibility with more customer-level control |
| Dedicated GPU | Raw GPU infrastructure | Customer owns the inference stack |
Geodd’s MLOps Services are described as a managed operational layer for deployment, scaling, monitoring, continuous optimization, reliability engineering, and support across inference services and dedicated deployments.
Geodd’s DeployPad is described as a deployment and orchestration layer that converts workload requirements into deployment plans, including infrastructure selection, autoscaling, monitoring, observability, and cost optimization.
Geodd’s Optimised Model Engine is described as an execution layer focused on speed, latency, throughput, and predictability under real-world load, using techniques such as graph optimization, compilation, kernel-level tuning, speculative decoding, and state-aware caching.
These are Geodd-provided product claims. They should be validated against the buyer’s workload, model, region, latency target, security requirements, traffic pattern, and production constraints.
Geodd should not be positioned as “managed inference is always better.”
A more accurate position is:
Geodd is a fit when a team wants production inference support without carrying the full operational stack internally, and when the workload benefits from managed deployment, optimization, monitoring, scaling, and engineering support.
Practical decision framework
Choose managed inference if
| Condition | Why it points to managed inference |
|---|---|
| Infrastructure work is slowing product work | The operating load is affecting delivery |
| P99 latency is unstable under load | Runtime and scheduling need active ownership |
| GPU spend is hard to explain | Utilization and capacity planning may need attention |
| The team lacks dedicated MLOps capacity | Operations may not be sustainable internally |
| Support quality matters during incidents | Provider operating depth becomes part of reliability |
| Traffic is bursty or changing | Scaling and cost behavior need continuous adjustment |
| Production reliability matters more than stack ownership | Operational abstraction may be worth the tradeoff |
Choose self-hosted inference if
| Condition | Why it points to self-hosting |
|---|---|
| The team needs full runtime control | Managed platforms may abstract too much |
| Compliance requires full ownership | Data and infrastructure boundaries may need to stay internal |
| The team has strong infrastructure capability | Operational burden is realistic to carry |
| Workload volume is predictable | Internal capacity planning may be efficient |
| GPU utilization can stay high | Raw compute economics can improve |
| Custom serving behavior is required | Self-hosting avoids provider constraints |
Choose dedicated inference if
| Condition | Why it points to dedicated inference |
|---|---|
| Shared inference is too variable | Isolation may improve predictability |
| Raw self-hosting is too operationally heavy | Provider can own more operations |
| Workload needs dedicated capacity | Performance and tenancy requirements are clearer |
| The team wants control without full operations | Responsibility can be split |
| Latency and throughput need closer review | Dedicated environments can be evaluated per workload |
Final takeaway
Managed inference and self-hosted inference define who owns the production behavior of an AI system.
Self-hosted inference gives maximum control, but the team owns deployment, scaling, observability, runtime tuning, reliability, cost behavior, and incident response.
Managed inference can reduce that operating load, but buyers must verify what the provider actually manages, how performance is measured, how costs behave, and who responds when the system degrades.
For production AI workloads, the right choice is the one whose responsibility boundary matches the team’s technical capacity, workload profile, risk tolerance, and expected growth.
FAQ
What is the difference between managed inference and self-hosted inference?
Managed inference shifts part of the inference infrastructure to a provider. Self-hosted inference keeps infrastructure, model serving, scaling, monitoring, optimization, and incident response inside the customer team. The main difference is operational ownership.
Is managed inference cheaper than self-hosted inference?
Managed inference is not always cheaper. It can reduce operational cost, overprovisioning, and engineering burden. Self-hosted inference can have lower raw compute cost when utilization is high and the team can operate the stack efficiently.
When should a team move from self-hosted inference to managed inference?
A team should consider managed inference when infrastructure work slows product delivery, P99 latency becomes unstable under load, GPU costs become hard to explain, or incident response depends on too few internal people.
When does self-hosted inference make sense?
Self-hosted inference makes sense when a team needs deep control, has strong infrastructure capability, can maintain high GPU utilization, and is prepared to own monitoring, scaling, runtime tuning, and incidents.
What are the hidden costs of self-hosted inference?
Hidden costs include engineering time, on-call burden, GPU overprovisioning, idle capacity, monitoring setup, debugging time, runtime upgrades, reliability work, and delayed product execution.
What should a managed inference provider own?
A managed inference provider may own deployment, model serving, GPU allocation, autoscaling, observability, runtime optimization, failure recovery, and support. Buyers should verify this because managed inference varies by provider.
What metrics matter when comparing inference infrastructure?
Important metrics include P95 latency, P99 latency, TTFT, inter-token latency, tokens per second, throughput, concurrency, GPU utilization, error rate, cold-start time, cost per token, and recovery time during incidents.
Is dedicated inference different from serverless inference?
Yes. Serverless inference usually abstracts infrastructure and may run on shared capacity. Dedicated inference provides isolated capacity or dedicated environments, often with clearer resource ownership and more control.
What is the biggest risk of managed inference?
The biggest risk is an unclear responsibility boundary. The buyer must know what the provider owns, what the customer still owns, and how incidents are handled.
What is the biggest risk of self-hosted inference?
The biggest risk is underestimating the operating burden. A working endpoint can still become unstable when traffic, concurrency, context length, or customer expectations increase.